Updated on 2026/04/29

写真a

 
KANEKO Hiromasa
 
Organization
Undergraduate School School of Science and Technology Professor
Title
Professor
External link

Degree

  • 博士(工学) ( 東京大学 )

Research Interests

  • 時間変数

  • プロセス設計

  • 材料設計

  • 分子設計

  • ケモインフォマティクス

  • プロセスインフォマティクス

  • マテリアルズインフォマティクス

  • 機械学習

  • 直接的逆解析

  • 実験計画法

  • ベイズ最適化

  • 能動学習

  • ソフトセンサー

  • アンサンブル学習

  • 予測誤差

  • ベイズの定理

  • 時間差分

  • 適応型モデル

  • プロセス管理

  • モデルの劣化

Research Areas

  • Manufacturing technology (mechanical,electrical/electronic, chemical engineering) / Chemical reaction and process system engineering

Research History

  • Meiji University   Senior Assistant Professor

    2017.4 - 2020.3

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  • The University of Tokyo   Assistant Professor (non-tenured)

    2012.4 - 2017.3

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Papers

  • Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies

    Yuta Sakai, Motosuke Katayama, Hiromasa Kaneko

    Analytical Sciences   2026.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1007/s44211-026-00919-9

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  • Construction of a multi-label odor prediction model based on molecular structures and olfactory receptor binding profiles with a novel interpretability framework

    Yuta Wakutsu, Hiromasa Kaneko

    Analytical Sciences   2026.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1007/s44211-026-00900-6

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  • Design of a green ammonia production process by machine learning

    Sho Takaoka, Hiromasa Kaneko

    Analytical Sciences   2026.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1007/s44211-026-00903-3

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  • Deep autoencoder for low dimensionality for high dimensional data in regression models and direct inverse analysis of models

    Hiromasa Kaneko

    Analytical Sciences   2026.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1007/s44211-026-00904-2

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  • Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for CO2 Separation Membranes

    Yuto Shino, Motosuke Katayama, Yuri Ito, Hiromasa Kaneko

    Molecular Informatics   2026.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/minf.70032

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  • Development of machine learning models for predicting properties of carbon materials and design of process conditions for production of materials with desired multiple properties

    Masayoshi Matsubara, Ryo Sasaki, Jun P. Takahara, Shinji Moritake, Yasuyuki Harada, Hiromasa Kaneko

    Analytical Sciences   2026.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1007/s44211-026-00890-5

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  • Enhancing the Interpretability of Asymmetric Catalysis CoMFA through PLS (n = 1) with Contribution Map Merging and Spatial Aggregation

    Yuta Sumii, Hiromasa Kaneko

    The Journal of Organic Chemistry   2026.2

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.joc.5c02293

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  • Design of Gold Extraction Solvents Using Machine Learning Models

    Takuto Tsunemi, Tatsuya Oshima, Hiroki Yokota, Hiromasa Kaneko

    ACS Omega   2026.2

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.5c11629

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  • Infrared Spectral Descriptors for Reaction Yield Prediction: Toward Redefining Experimental Spaces

    Yuya Endo, Hiromasa Kaneko

    Molecular Informatics   2026.2

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/minf.70019

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  • Machine Learning–Driven Design of Sustainable Polymer Membranes: Integrated Prediction of Gas Permeability, Selectivity, and Biodegradability

    Haruki Ochiai, Kazukiyo Nagai, Hiromasa Kaneko

    ACS Omega   2026.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.5c10251

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  • Adsorption Energy Prediction Model for CO2 Reduction on Electrocatalysts Containing Previously Unencountered Metal Elements

    Issa Onishi, Hiromasa Kaneko

    Journal of Chemical Information and Modeling   2026.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.jcim.5c02182

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  • Beware of optimality criteria in design of experiments: New criterion based on distance to the ideal distribution for advanced machine learning

    Hiromasa Kaneko

    Materials Today Communications   2026.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.mtcomm.2026.114648

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  • Post-ageing guided closed-loop discovery of multi-element alloy catalysts for automotive exhaust purification

    Hitoshi Mikami, Azusa Kamiyama, Kohei Kusada, Megumi Mukoyoshi, Hiromasa Kaneko, Masaaki Haneda, Hiroshi Maeno, Tomokazu Yamamoto, Yasukazu Murakami, Hiroshi Kitagawa

    Nanoscale Advances   2026

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1039/D5NA01017A

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  • Construction and Improvement of a Model for Quantifying Blood Glucose Concentration Using Mid‐Infrared Spectroscopy

    Yuta Takami, Keita Miyagawa, Yuki Tsuda, Koichi Akiyama, Yuji Matsuura, Hiromasa Kaneko

    Journal of Chemometrics   2025.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/cem.70091

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  • Design of Polymeric Nickel Catalysts for Suzuki–Miyaura Type Cross–Coupling Reaction Using Machine Learning

    Sho Takaoka, Zhenzhong Zhang, Yoichi M. A. Yamada, Hiromasa Kaneko

    ACS Applied Polymer Materials   2025.11

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsapm.5c02957

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  • Development of Pesticide-Likeness Scores and Models for Predicting Pesticide Activity of Molecular Scaffolds with Machine Learning

    Yuta Sakai, Hiromasa Kaneko

    ACS Omega   2025.11

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.5c07323

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  • Distortion prediction model considering process types in film manufacturing process and identification of critical process variables

    Yuta Wakutsu, Satoshi Natori, Hiroki Ochiai, Kazuya Suda, Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   2025.9

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.chemolab.2025.105474

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  • Batch Process Design Including Initial and Operating Conditions and Online Property Estimation in Acrylic Resin Polymerization

    Rinta Kawagoe, Fumiya Hamada, Kazutoshi Terauchi, Toshinori Yamaji, Hiromasa Kaneko

    ACS Omega   2025.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.5c01274

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  • Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results

    Yuta Sakai, Shota Horikawa, Mamoru Aizawa, Hiromasa Kaneko

    Analytical Science Advances   2025.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/ansa.70021

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  • Design of the Ethylbenzene production process using machine learning

    Eri Ishikawa, Hiromasa Kaneko

    Case Studies in Chemical and Environmental Engineering   2025.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.cscee.2025.101157

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  • Correlations between the constituent molecules, crystal structures, and dielectric constants in organic crystals

    Yuya Shiraki, Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   2025.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.chemolab.2025.105376

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  • Development of a Model for Predicting the Adsorption Performance of Zeolites and Designing New Zeolites

    Ruka Ando, Hiromasa Kaneko

    Industrial & Engineering Chemistry Research   2025.5

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.iecr.5c00225

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  • Adaptive Design of Experimental Conditions for LaFeO3 Crystals with Experiments and Bayesian Optimization

    Daigo Kaneko, Risa Iwatsubo, Hajime Wagata, Hiromasa Kaneko

    Industrial & Engineering Chemistry Research   2025.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.iecr.4c04680

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  • Machine Learning Model for Predicting Dielectric Constant of Epoxy Resin with Additional Data Selection and Design of Monomer Structures for Low Dielectric Constant

    Yuya Shiraki, Yuko Kawanami, Kenichi Shinmei, Hiromasa Kaneko

    ACS Applied Polymer Materials   2025.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsapm.4c03279

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  • Molecular Odor Prediction Using Olfactory Receptor Information

    Yuta Wakutsu, Hiromasa Kaneko

    Molecular Informatics   2025.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/minf.202400274

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  • Data analysis on yield and electrical properties of proton-conducting ceramic fuel cells

    Yamato Nakanishi, Hiromasa Kaneko

    Next Research   2025.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.nexres.2025.100161

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  • Prediction of CFD Simulation Results Using Machine-Learning Models and Process Designs Based on Direct Inverse Analysis of the Models

    Hiromasa Kaneko

    Industrial & Engineering Chemistry Research   2025.2

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.iecr.4c03669

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  • Construction of Machine Learning Models to Predict the Maximum Absorption Wavelength Considering the Solute and Solvent and Inverse Analysis of the Models

    Haruki Ochiai, Hiromasa Kaneko

    ACS Omega   2025.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.4c07490

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  • Improving Molecular Design with Direct Inverse Analysis of QSAR/QSPR Model

    Yuto Shino, Hiromasa Kaneko

    Molecular Informatics   2025.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/minf.202400227

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  • Data-driven exploration of layered double hydroxide crystals exhibiting high fluoride ion adsorption properties and chemical stability

    Fumitaka Hayashi, Ryuki Harada, Hiroaki Sugitani, Hiromasa Kaneko, Tien Quang Nguyen, Mongkol Tipplook, Tetsuya Yamada, Michihisa Koyama, Katsuya Teshima

    CrystEngComm   2025

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1039/D5CE00313J

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  • Exploring Molecular Descriptors and Acquisition Functions in Bayesian Optimization for Designing Molecules with Low Hole Reorganization Energy

    Rinta Kawagoe, Tatsuhito Ando, Nobuyuki N. Matsuzawa, Hiroyuki Maeshima, Hiromasa Kaneko

    ACS Omega   2024.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.4c09124

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  • Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics

    Ayami Ohkuma, Yoshihito Yamauchi, Nobuhito Yamada, Satoshi Ooyama, Hiromasa Kaneko

    Results in Engineering   2024.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.rineng.2024.103475

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  • Adaptive soft sensor considering process state in film manufacturing process and identification of critical process variables

    Yuya Shiraki, Yuki Nakayama, Satoshi Natori, Kazuya Suda, Yuki Ono, Hiromasa Kaneko

    Results in Chemistry   2024.7

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.rechem.2024.101677

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  • Predicting product quality and optimising process design using dynamic time warping in batch processes with varying batch times

    Shuto Yamakage, Kazutoshi Terauchi, Fumiya Hamada, Toshinori Yamaji, Hiromasa Kaneko

    Case Studies in Chemical and Environmental Engineering   2024.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.cscee.2024.100655

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  • Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning

    Yuki Nakayama, Saki Morishita, Hayato Doi, Tatsuya Hirano, Hiromasa Kaneko

    ACS Omega   2024.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.4c00655

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  • Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models

    Hiromasa Kaneko

    ACS Omega   2024.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.3c08036

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  • Clustering method for the construction of machine learning model with high predictive ability

    Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   2024.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.chemolab.2024.105084

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  • Development of New Molecular Descriptors Based on Flare Software Considering Three-Dimensional Chemical Structures

    Yuki Nakayama, Hiromasa Kaneko

    Industrial & Engineering Chemistry Research   2024.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.iecr.3c02775

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  • Development of a Flux-Method Process Informatics System and Its Application in Growth Control for Layered Perovskite Ba5Nb4O15 Crystals

    Tetsuya Yamada, Hiromasa Kaneko, Fumitaka Hayashi, Tatsuya Doi, Michihisa Koyama, Katsuya Teshima

    Crystal Growth & Design   2023.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.cgd.3c00828

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  • T-Gen: Time series data generator for inverse analysis of machine learning models

    Hiromasa Kaneko

    Case Studies in Chemical and Environmental Engineering   2023.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.cscee.2023.100475

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  • Simultaneous Design of Gas Separation Membranes and Schemes through Combined Process and Materials Informatics

    Shunsuke Yuyama, Hiromasa Kaneko

    Industrial & Engineering Chemistry Research   2023.11

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.iecr.3c02444

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  • Design of Ammonia Borane Dehydrogenation Catalysts Using Previous Study Data, Public Data, and Machine Learning

    Daisuke Sugizaki, Hiromasa Kaneko

    Industrial & Engineering Chemistry Research   2023.11

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.iecr.3c02591

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  • Defect rate prediction and failure‐cause diagnosis in a mass‐production process for precision electric components

    Hiromasa Kaneko

    Analytical Science Advances   2023.10

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/ansa.202300019

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  • Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions

    Kohei Motojima, Abhijit Sen, Yoichi M. A. Yamada, Hiromasa Kaneko

    Journal of Chemical Information and Modeling   2023.9

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.jcim.3c01196

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  • Enhancing the Search Performance of Bayesian Optimization by Creating Different Descriptor Datasets Using Density Functional Theory

    Toshiharu Morishita, Hiromasa Kaneko

    ACS Omega   2023.9

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.3c04891

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  • Robust Design of a Dimethyl Ether Production Process Using Process Simulation and Robust Bayesian Optimization

    Yuki Nakayama, Hiromasa Kaneko

    ACS Omega   2023.8

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.3c02344

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  • Predictive Modeling of HMG-CoA Reductase Inhibitory Activity and Design of New HMG-CoA Reductase Inhibitors

    Shigeyoshi Samizo, Hiromasa Kaneko

    ACS Omega   2023.8

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.3c02567

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  • Retrosynthetic and Synthetic Reaction Prediction Model Based on Sequence‐to‐Sequence with Attention for Polymer Designs

    Hiroaki Taniwaki, Hiromasa Kaneko

    Macromolecular Theory and Simulations   2023.7

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1002/mats.202300011

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  • Interpretation of Machine Learning Models for Data Sets with Many Features Using Feature Importance

    Hiromasa Kaneko

    ACS Omega   2023.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.3c03722

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  • Molecular Descriptors, Structure Generation, and Inverse QSAR/QSPR Based on SELFIES

    Hiromasa Kaneko

    ACS Omega   2023.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.3c01332

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  • Nanoscale chemical patterning of graphite at different length scales Reviewed

    Sasikumar Rahul, Miriam C. Rodríguez González, Shingo Hirose, Hiromasa Kaneko, Kazukuni Tahara, Kunal S. Mali, Steven De Feyter

    Nanoscale   15 ( 24 )   10295 - 10305   2023.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1039/d3nr00632h

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  • Design of batch process with machine learning, feature extraction, and direct inverse analysis

    Shuto Yamakage, Hiromasa Kaneko

    Case Studies in Chemical and Environmental Engineering   2023.6

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.cscee.2023.100308

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  • Spatially Controlled Aryl Radical Grafting of Graphite Surfaces Guided by Self-Assembled Molecular Networks of Linear Alkane Derivatives: The Importance of Conformational Dynamics

    Sota Aoi, Shingo Hirose, Wakana Soeda, Hiromasa Kaneko, Kunal S. Mali, Steven De Feyter, Kazukuni Tahara

    Langmuir   2023.5

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.langmuir.2c03434

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  • Machine Learning Model for Predicting the Material Properties and Bone Formation Rate and Direct Inverse Analysis of the Model for New Synthesis Conditions of Bioceramics

    Kohei Motojima, Rina Shiratsuchi, Kitaru Suzuki, Mamoru Aizawa, Hiromasa Kaneko

    Industrial & Engineering Chemistry Research   2023.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.iecr.3c00332

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  • Selecting optimum miRNA panel for miRNA signature-based companion diagnostic model to predict the response of R-CHOP treatment in diffuse large B-cell lymphoma

    Noriko Nakamura, Risa Hamada, Hiromasa Kaneko, Seiichi Ohta

    Journal of Bioscience and Bioengineering   2023.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.jbiosc.2023.01.005

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  • Local interpretation of nonlinear regression model with k-nearest neighbors

    Hiromasa Kaneko

    Digital Chemical Engineering   2023.3

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.dche.2022.100078

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  • De Novo Direct Inverse QSPR/QSAR: Chemical Variational Autoencoder and Gaussian Mixture Regression Models

    Kohei Nemoto, Hiromasa Kaneko

    Journal of Chemical Information and Modeling   2023.2

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.jcim.2c01298

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  • Initial Sample Selection in Bayesian Optimization for Combinatorial Optimization of Chemical Compounds

    Toshiharu Morishita, Hiromasa Kaneko

    ACS Omega   2023.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.2c05145

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  • Process-Informatics-Assisted Preparation of Lithium Titanate Crystals with Various Sizes and Morphologies

    Daigo Kaneko, Hiromasa Kaneko, Fumitaka Hayashi, Kohei Fukaishi, Tetsuya Yamada, Katsuya Teshima

    Industrial & Engineering Chemistry Research   2023.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.iecr.2c02729

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  • Direct prediction of the batch time and process variable profiles using batch process data based on different batch times

    Hiromasa Kaneko

    Computers & Chemical Engineering   2023.1

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.compchemeng.2022.108072

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  • True Gaussian mixture regression and genetic algorithm-based optimization with constraints for direct inverse analysis

    Hiromasa Kaneko

    Science and Technology of Advanced Materials: Methods   2022.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1080/27660400.2021.2024101

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  • Integration of Materials and Process Informatics: Metal Oxide and Process Design for CO2 Reduction

    Ryo Iwama, Hiromasa Kaneko

    ACS Omega   2022.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acsomega.2c06008

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  • Design of adaptive soft sensor based on Bayesian optimization

    Shuto Yamakage, Hiromasa Kaneko

    Case Studies in Chemical and Environmental Engineering   2022.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.cscee.2022.100237

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  • Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization

    Tatsuhito Ando, Naoto Shimizu, Norihisa Yamamoto, Nobuyuki N. Matsuzawa, Hiroyuki Maeshima, Hiromasa Kaneko

    The Journal of Physical Chemistry A   2022.9

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1021/acs.jpca.2c05229

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  • Symmetry and spacing controls in periodic covalent functionalization of graphite surfaces templated by self-assembled molecular networks Reviewed

    Shingo Hashimoto, Hiromasa Kaneko, Steven De Feyter, Yoshito Tobe, Kazukuni Tahara

    NANOSCALE   14 ( 35 )   12595 - 12609   2022.9

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1039/d2nr02858a

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  • Design and Analysis of Metal Oxides for CO2 Reduction Using Machine Learning, Transfer Learning, and Bayesian Optimization

    Ryo Iwama, Koji Takizawa, Kenichi Shinmei, Eisuke Baba, Noritoshi Yagihashi, Hiromasa Kaneko

    ACS Omega   7 ( 12 )   10709 - 10717   2022.3

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    Publishing type:Research paper (scientific journal)   Publisher:American Chemical Society ({ACS})  

    DOI: 10.1021/acsomega.2c00461

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  • Genetic Algorithm-Based Partial Least-Squares with Only the First Component for Model Interpretation

    Hiromasa Kaneko

    ACS Omega   7 ( 10 )   8968 - 8979   2022.3

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    Publishing type:Research paper (scientific journal)   Publisher:American Chemical Society ({ACS})  

    DOI: 10.1021/acsomega.1c07379

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  • Deep Convolutional Neural Network with Deconvolution and a Deep Autoencoder for Fault Detection and Diagnosis

    Yasuhiro Kanno, Hiromasa Kaneko

    ACS Omega   2022.1

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    Publishing type:Research paper (scientific journal)   Publisher:American Chemical Society ({ACS})  

    DOI: 10.1021/acsomega.1c06607

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  • Development of Prediction Models for the Self-Accelerating Decomposition Temperature of Organic Peroxides

    Toshiharu Morishita, Hiromasa Kaneko

    ACS Omega   2022.1

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    Publishing type:Research paper (scientific journal)   Publisher:American Chemical Society ({ACS})  

    DOI: 10.1021/acsomega.1c06481

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  • Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal–Organic Frameworks

    Shunsuke Yuyama, Hiromasa Kaneko

    Journal of Chemical Information and Modeling   61 ( 12 )   5785 - 5792   2021.12

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    Publishing type:Research paper (scientific journal)   Publisher:American Chemical Society ({ACS})  

    DOI: 10.1021/acs.jcim.1c01205

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  • Adaptive soft sensor ensemble for selecting both process variables and dynamics for multiple process states

    Nobuhito Yamada, Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   2021.12

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.chemolab.2021.104443

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  • Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts

    Tomoya Ebi, Abhijit Sen, Raghu N. Dhital, Yoichi M. A. Yamada, Hiromasa Kaneko

    ACS Omega   6 ( 41 )   27578 - 27586   2021.10

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    Publishing type:Research paper (scientific journal)   Publisher:American Chemical Society ({ACS})  

    DOI: 10.1021/acsomega.1c04826

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  • Transfer learning and wavelength selection method in NIR spectroscopy to predict glucose and lactate concentrations in culture media using VIP‐Boruta

    Hiromasa Kaneko, Shunsuke Kono, Akihiro Nojima, Takuya Kambayashi

    Analytical Science Advances   2 ( 9-10 )   470 - 479   2021.10

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    Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    DOI: 10.1002/ansa.202000177

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  • Prediction of spin–spin coupling constants with machine learning in NMR

    Kaina Shibata, Hiromasa Kaneko

    Analytical Science Advances   2 ( 9-10 )   464 - 469   2021.10

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    Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    DOI: 10.1002/ansa.202000180

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  • Estimating the reliability of predictions in locally weighted partial least‐squares modeling

    Hiromasa Kaneko

    Journal of Chemometrics   35 ( 9 )   2021.9

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    DOI: 10.1002/cem.3364

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  • Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

    Ryo Iwama, Hiromasa Kaneko

    Journal of Advanced Manufacturing and Processing   3 ( 3 )   2021.7

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    DOI: 10.1002/amp2.10085

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  • Examining variable selection methods for the predictive performance of regression models and the proportion of selected variables and selected random variables

    Hiromasa Kaneko

    Heliyon   7 ( 6 )   2021.6

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    DOI: 10.1016/j.heliyon.2021.e07356

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  • Design of thermoelectric materials with high electrical conductivity, high Seebeck coefficient, and low thermal conductivity

    Hiroki Yoshihama, Hiromasa Kaneko

    Analytical Science Advances   2 ( 5-6 )   289 - 294   2021.6

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    DOI: 10.1002/ansa.202000114

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  • Estimation and visualization of process states using latent variable models based on Gaussian process

    Hiromasa Kaneko

    Analytical Science Advances   2 ( 5-6 )   326 - 333   2021.6

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    DOI: 10.1002/ansa.202000122

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  • Support vector regression that takes into consideration the importance of explanatory variables

    Hiromasa Kaneko

    Journal of Chemometrics   35 ( 4 )   2021.4

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    DOI: 10.1002/cem.3327

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  • Two‐ and Three‐dimensional Quantitative Structure‐activity Relationship Models Based on Conformer Structures

    Fumika Nitta, Hiromasa Kaneko

    Molecular Informatics   40 ( 3 )   2000123 - 2000123   2021.3

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    DOI: 10.1002/minf.202000123

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  • Ensemble Just-in-time Model Based on Gaussian Process Dynamical Models for Nonlinear and Dynamic Processes, Reviewed

    Yasuhiro Kanno, Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   2020.8

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  • Porous Self-Assembled Molecular Networks as Templates for Chiral-Position-Controlled Chemical Functionalization of Graphitic Surfaces Reviewed

    Kazukuni Tahara, Yuki Kubo, Shingo Hashimoto, Toru Ishikawa, Hiromasa Kaneko, Anton Brown, Brandon E. Hirsch, Steven De Feyter, Yoshito Tobe

    JOURNAL OF THE AMERICAN CHEMICAL SOCIETY   142 ( 16 )   7699 - 7708   2020.4

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  • Improvement of Predictive Accuracy in Semi-Supervised Regression Analysis by Selecting Unlabeled Chemical Structures Reviewed

    asuhiro Kanno, Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   2019.8

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  • Estimation of Predictive Performance for Test Data in Applicability Domains Using y-randomization Reviewed

    Hiromasa Kaneko

    Journal of Chemometrics   33 ( 9 )   2019.7

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    DOI: 10.1002/cem.3171

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  • 高屈折率および高ガラス転移温度をもつ高分子材料のモノマー設計 Reviewed

    高野 森乃介, 金子 弘昌

    Journal of Computer Chemistry, Japan   2019

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  • モデルの適用範囲の考慮したアンサンブル学習法の開発 Reviewed

    佐藤 圭悟, 金子 弘昌

    Journal of Computer Chemistry, Japan   2019

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  • Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring Reviewed

    Shojiro Shibayama, Hiromasa Kaneko, Kimito Funatsu

    Computers and Chemical Engineering   113   86 - 97   2018.5

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    DOI: 10.1016/j.compchemeng.2018.01.025

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  • Sparse Generative Topographic Mapping for Both Data Visualization and Clustering Reviewed

    Hiromasa Kaneko

    Journal of Chemical Information and Modeling   58 ( 12 )   2528 - 2535   2018

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    DOI: 10.1021/acs.jcim.8b00528

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  • Data Visualization, Regression, Applicability Domains and Inverse Analysis Based on Generative Topographic Mapping Reviewed

    Hiromasa Kaneko

    Molecular Informatics   38 ( 3 )   1800088 - 1800088   2018

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    DOI: 10.1002/minf.201800088

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  • Automatic Outlier Sample Detection Based on Regression Analysis and Repeated Ensemble Learning Reviewed

    Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   177   74 - 82   2018

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    DOI: 10.1016/j.chemolab.2018.04.015

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  • Beware of r2 even for Test Datasets: Using the Latest Measured y-values (r2LM) in Time Series Data Analysis Reviewed

    Hiromasa Kaneko

    Journal of Chemometrics   2018

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    DOI: 10.1002/cem.3093

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  • Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Sub-Models Reviewed

    Hiromasa Kaneko

    Journal of Chemical Information and Modeling   58 ( 2 )   480 - 489   2018

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    DOI: 10.1021/acs.jcim.7b00649

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  • Illustration of Merits of Semi-supervised Learning in Regression Analysis Reviewed

    Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   182   47 - 56   2018

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    DOI: 10.1016/j.chemolab.2018.08.015

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  • K-Nearest Neighbor Normalized Error for Visualization and Reconstruction - A New Measure for Data Visualization Performance Reviewed

    Hiromasa Kaneko

    Chemometrics and Intelligent Laboratory Systems   176   22 - 33   2018

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    DOI: 10.1016/j.chemolab.2018.03.001

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  • Selective Use of Adaptive Models Considering the Prediction Efficiencies Reviewed

    N. Yuge, K. Tanaka, K. Funatsu

    Industrial & Engineering Chemistry Research   57 ( 42 )   14286 - 14296   2018

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    DOI: 10.1021/acs.iecr.8b01171

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  • A new measure of regression model accuracy that considers applicability domains Reviewed

    Hiromasa Kaneko

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   171   1 - 8   2017.12

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    DOI: 10.1016/j.chemolab.2017.09.018

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  • Novel Method Proposing Chemical Structures with Desirable Profile of Activities Based on Chemical and Protein Spaces Reviewed

    Iwao Maeda, Kiyoshi Hasegawa, Hiromasa Kaneko, Kimito Funatsu

    MOLECULAR INFORMATICS   36 ( 12 )   2017.12

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    DOI: 10.1002/minf.201700075

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  • Detection of nonlinearity in soil property prediction models based on near-infrared spectroscopy Reviewed

    Lu Yan, Matheus S. Escobar, Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   167   139 - 151   2017.8

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    DOI: 10.1016/j.chemolab.2017.04.001

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  • Improvement of Process State Recognition Performance by Noise Reduction with Smoothing Methods Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    JOURNAL OF CHEMICAL ENGINEERING OF JAPAN   50 ( 6 )   422 - 429   2017.6

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    DOI: 10.1252/jcej.16we325

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  • lA Novel Calibration-Minimum Method for Prediction of Mole Fraction in Non-Ideal Mixture Reviewed

    Shojiro Shibayama, Hiromasa Kaneko, Kimito Funatsu

    AAPS PHARMSCITECH   18 ( 3 )   595 - 604   2017.4

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    DOI: 10.1208/s12249-016-0547-6

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  • On Generative Topographic Mapping and Graph Theory combined approach for unsupervised non-linear data visualization and fault identification Reviewed

    Matheus S. Escobar, Hiromasa Kaneko, Kimito Funatsu

    COMPUTERS & CHEMICAL ENGINEERING   98   113 - 127   2017.3

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    DOI: 10.1016/j.compchemeng.2016.12.009

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  • Applicability Domains and Consistent Structure Generation Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    MOLECULAR INFORMATICS   36 ( 1-2 )   2017.1

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    DOI: 10.1002/minf.201600032

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  • Chemical-Space-Based de Novo Design Method To Generate Drug Like Molecules Reviewed

    Shunichi Takeda, Hiromasa Kaneko, Kimito Funatsu

    JOURNAL OF CHEMICAL INFORMATION AND MODELING   56 ( 10 )   1885 - 1893   2016.10

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    DOI: 10.1021/acs.jcim.6b00038

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  • Iterative optimization technology combined with wavelength selection based on excess absorption for a process analytical technology calibration-minimum approach Reviewed

    Shojiro Shibayama, Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   156   137 - 147   2016.8

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    DOI: 10.1016/j.chemolab.2016.06.001

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  • Ring system-based chemical graph generation for de novo molecular design Reviewed

    Tomoyuki Miyao, Hiromasa Kaneko, Kimito Funatsu

    JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN   30 ( 5 )   425 - 446   2016.5

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    DOI: 10.1007/s10822-016-9916-1

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  • Development of an Adaptive Experimental Design Method Based on Probability of Achieving a Target Range through Parallel Experiments Reviewed

    Atsuyuki Nakao, Hiromasa Kaneko, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   55 ( 19 )   5726 - 5735   2016.5

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    DOI: 10.1021/acs.iecr.6b00852

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  • Preparation of comprehensive data from huge data sets for predictive soft sensors Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   153   75 - 81   2016.4

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    DOI: 10.1016/j.chemolab.2016.02.011

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  • Ensemble locally weighted partial least squares as a just-in-time modeling method Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    AICHE JOURNAL   62 ( 3 )   717 - 725   2016.3

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    DOI: 10.1002/aic.15090

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  • Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x) Reviewed

    Tomoyuki Miyao, Hiromasa Kaneko, Kimito Funatsu

    JOURNAL OF CHEMICAL INFORMATION AND MODELING   56 ( 2 )   286 - 299   2016.2

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    DOI: 10.1021/acs.jcim.5b00628

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  • Ring-system-based Chemical Structure Enumeration for de Novo Design

    Tomoyuki Miyao, Hiromasa Kaneko, Kimito Funatsu

    YAKUGAKU ZASSHI-JOURNAL OF THE PHARMACEUTICAL SOCIETY OF JAPAN   136 ( 1 )   101 - 106   2016.1

  • Practical Use of Compound-target Interactions in Chemistry and Drug Discovery similar to A Chemoinformatics Approach similar to Foreword

    Hiromasa Kaneko, Shinya Nakamura, Norihito Kawahsita

    YAKUGAKU ZASSHI-JOURNAL OF THE PHARMACEUTICAL SOCIETY OF JAPAN   136 ( 1 )   95 - 96   2016.1

  • Generative Topographic Mapping Visualization Performance allied to Root Mean Square Error of Midpoint among Nearest Neighbors

    Escobar Matt, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2016   O17   2016

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    In the realm of data visualization, reducing complex data sets to 2-D maps is a common practice used for reconciling information and for identifying meaningful patterns and clusters. Within the plethora of different methodologies and criteria for defining optimal maps, however, one must be aware on how to assess the performance of such visualization. This work focuses on Generative Topographic Mapping, a well-known nonlinear visualization methodology, to investigate and propose different indexes used for defining optimal latent 2-D maps. More specifically, this work focuses on criteria used for obtaining optimal hyperparameters used for map training. Common criterion such as RMSE is used, but also a new strategy relying on Root Mean Square Error of Midpoint (RMSEM) and its association with Nearest Neighbors (NN) is proposed. In order to evaluate their performance, an artificial data set and Tennessee Eastman Process (TEP) were used as case studies, highlighting the potential of the proposed criterion for defining more reliable and meaningful data visualization.

    DOI: 10.11545/ciqs.2016.0_O17

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  • Practical Use of Savitzky-Golay Filtering-Based Ensemble Online SVR Reviewed

    Hiromasa Kaneko, Takuya Matsumoto, Shigeki Ootakara, Kimito Funatsu

    IFAC PAPERSONLINE   49 ( 7 )   371 - 376   2016

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    DOI: 10.1016/j.ifacol.2016.07.364

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  • Soft Sensors: Chemoinformatic Model for Efficient Control and Operation in Chemical Plants Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    FRONTIERS IN MOLECULAR DESIGN AND CHEMIAL INFORMATION SCIENCE - HERMAN SKOLNIK AWARD SYMPOSIUM 2015: JURGEN BAJORATH   1222   159 - 174   2016

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  • Data Visualization & Clustering: Generative Topographic Mapping Similarity Assessment Allied to Graph Theory Clustering Reviewed

    Matheus de Souza Escobar, Hiromasa Kaneko, Kimito Funatsu

    FRONTIERS IN MOLECULAR DESIGN AND CHEMIAL INFORMATION SCIENCE - HERMAN SKOLNIK AWARD SYMPOSIUM 2015: JURGEN BAJORATH   1222   175 - 210   2016

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  • A Novel Calibration-Minimum Method for Prediction of Mole Fraction in Non-Ideal Mixture.

    S. Shibayama, K. Funatsu

    AAPS PharmSciTech   1 - 10   2016

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  • Prediction of Membrane Resistance in Newly Constructed Membrane Bioreactor

    OMATA Shingo, KANEKO Hiromasa, FUNATSU Kimito

    Journal of Computer Chemistry, Japan   15 ( 2 )   23 - 31   2016

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    <p>Membrane bioreactors (MBRs) are a system integrating biological degradation of wasteproducts and solid-liquid separation by membrane filtration. One of the obstacles to wideapplication of MBRs is membrane fouling, which is a phenomenon whereby particles depositon the membrane surface or in the pores. Fouling decreases membrane permeability andincreases operating costs. In order to reduce fouling, chemical cleaning must be performedat an appropriate time. However, chemicals used in cleaning are costly and preparationtime is needed to do chemical cleaning. Hence, a fouling prediction model is required.Although statistical models have been proposed in previous studies, it is difficult topredict fouling in newly constructed MBRs or when operating conditions are changed. Wefocused on the fact that the fouling mechanism is common in various MBRs and to predictfouling in a certain newly established MBR, we developed a statistical model trained bydata measured in other MBRs. Through case studies using data sets measured in real MBRs,it was confirmed that a constructed model using the proposed method could predict foulingmore accurately than a model which was trained by data measured in the newly constructedMBR (Figures 4, 5, Table 2).</p>

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  • Study on chemical graph generation based on reduced graphs

    Miyao Tomoyuki, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2016   O7   2016

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    Generating virtual chemical structures by combining building blocks is frequently conducted by chemists with the help of computer programs. This simple task usually suffers from generating duplicates, leading to inefficiency of the structure generation. On top of that, diversity-oriented structure generation is required when exhaustive generation is intractable. Here, we have proposed a structure generation algorithm by combining ring systems and atom fragments in a tree-like way without generating duplicates. The algorithm makes use of reduced graphs, which retain the same topology as that of the corresponding ring systems but have less vertices than in the corresponding ring systems. For diversity-oriented structure generation, we have proposed an algorithm that generate a chemical graph per atom-fragment-based framework. One of the biggest advantages of this algorithm is that it takes diversity into account during structure generation instead of sampling diverse structures after structure generation. The features and efficiencies of the proposed two algorithms were demonstrated with simple structure generation case studies.

    DOI: 10.11545/ciqs.2016.0_O7

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  • Development of Soil Properties Nonlinear Prediction Model with Variable Region Selection and Applicability Domain

    Yan Lu, Escobar Matheus de Souza, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2016   P13   2016

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    In precision agriculture, near-infrared (NIR) spectroscopy is a useful tool to predict soil properties. When applying statistical learning methods to near-infrared spectroscopy, wavelength selection becomes an inevitable issue due to the wide range of wavelengths measured. By comparing linear regression method and nonlinear regression method combined with variable region selection, it could be confirmed that some variables in NIR spectroscopy are nonlinearly related to soil properties. Additionally, soil properties are quite different for each area, so this large inter-area variability makes the prediction of new areas difficult. From this premise, multiple Bayesian ensemble regression is proposed for solving this applicability domain problem. For NIR spectroscopy data coming from different sources, prediction results of the proposed method were shown to be significantly superior to traditional modeling methods.

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  • Fault detection and fault state estimateion based on ensemble learning in industrial plants

    Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2016   O20   2016

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    For the safe and stable operation of industrial and chemical plants, it is necessary to monitor and control their operating conditions. Because of the huge amount of operating data in plants, data-based process control systems have received considerable attention in recent years. Controlling each process variable independently is inefficient, because there are many process variables that must be controlled. One practical solution is multivariate statistical process monitoring (MSPM) which monitors multiple process variables and their relationships simultaneously. Principal component analysis is widely used as an MSPM method. However, it cannot consider nonlinearities between process variables and multimodal data distributions. In addition, although process faults can be detected, it is difficult to estimate process states in detail. Therefore we developed a new MSPM method to detect process faults and to estimate each process state in industrial plants simultaneously by combining PCA and ensemble learning. Many PCA models, each of which represents local process state, are prepared using initial database. Fault detection and process state estimation are performed by checking similarity between a query and each PCA model. We demonstrate the effectiveness of the proposed method using numerical simulation data in which actual industrial process is simulated.

    DOI: 10.11545/ciqs.2016.0_O20

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  • Smoothing-Combined Soft Sensors for Noise Reduction and Improvement of Predictive Ability Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   54 ( 50 )   12630 - 12638   2015.12

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    DOI: 10.1021/acs.iecr.5b03054

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  • Adaptive model and model selection for long-term transmembrane pressure prediction in membrane bioreactors Reviewed

    H. Oishi, H. Kaneko, K. Funatsu

    JOURNAL OF MEMBRANE SCIENCE   494   86 - 91   2015.11

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    DOI: 10.1016/j.memsci.2015.07.002

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  • Data density-based fault detection and diagnosis with nonlinearities between variables and multimodal data distributions Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   147   58 - 65   2015.10

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    DOI: 10.1016/j.chemolab.2015.07.016

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  • A Mini-review on Chemoinformatics Approaches for Drug Discovery Reviewed

    Norihito Kawashita, Hiroyuki Yamasaki, Tomoyuki Miyao, Kentaro Kawa, Yoshitake Sakae, Takeshi Ishikawa, Kenichi Mori, Shinya Nakamura, Hiromasa Kaneko

    Journal of Computer Aided Chemistry   16 ( 1 )   15 - 29   2015.10

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    We have reviewed chemoinformatics approaches for drug discovery such as aromatic interactions, aromatic clusters, structure generation, virtual screening, de novo design, evolutionary algorithm, inverse-QSPR/QSAR, Monte Carlo, molecular dynamics, fragment molecular orbital method and matched molecular pair analysis from the viewpoint of young researchers. We intend to introduce various fields of chemoinformatics for non-expert researchers. The structure of this review is given as follows: 1. Introduction, 2. Analysis of Aromatic Interactions, 2.1 Aromatic Interactions, 2.2 Aromatic Clusters, 3. Ligand Based Structure Generation, 3.1 Virtual Screening, 3.2 De Novo Ligand Design, 3.3 Combinatorial Explosion, 3.4 Inverse-QSPR/QSAR, 4. Trends in Chemoinformatics-Based De Novo Drug Design, 5. Conformational Search Method Using Genetic Crossover for Bimolecular Systems, 6. Interaction Analysis using Fragment Molecular Orbital Method for Drug Discovery, 7. Matched Molecular Pair Analysis and SAR Analysis by Fragment Molecular Orbital Method, 8. Chemoinformatics Approach in Pharmaceutical Processes, 9. Conclusion.

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  • Improvement of iterative optimization technology (for process analytical technology calibration-free/minimum approach) with dimensionality reduction and wavelength selection of spectra Reviewed

    Hiromasa Kaneko, Koji Muteki, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   147   176 - 184   2015.10

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    DOI: 10.1016/j.chemolab.2015.08.017

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  • Analysis of a transmembrane pressure (TMP) jump prediction model for preventing TMP jumps Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    DESALINATION AND WATER TREATMENT   55 ( 12 )   3241 - 3246   2015.9

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    DOI: 10.1080/19443994.2014.940646

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  • Classification of drug tablets using hyperspectral imaging and wavelength selection with a GAWLS method modified for classification Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    INTERNATIONAL JOURNAL OF PHARMACEUTICS   491 ( 1-2 )   130 - 135   2015.8

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    DOI: 10.1016/j.ijpharm.2015.06.012

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  • Adaptive database management based on the database monitoring index for long-term use of adaptive soft sensors Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   146   179 - 185   2015.8

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    DOI: 10.1016/j.chemolab.2015.05.024

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  • Combined Generative Topographic Mapping and Graph Theory Unsupervised Approach for Nonlinear Fault Identification (vol 61, pg 1559, 2015)

    M. S. Escobar, H. Kaneko, K. Funatsu

    AICHE JOURNAL   61 ( 7 )   2372 - 2372   2015.7

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  • Strategy of Structure Generation within Applicability Domains with One-Class Support Vector Machine Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN   88 ( 7 )   981 - 988   2015.7

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    DOI: 10.1246/bcsj.20150054

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  • Combined generative topographic mapping and graph theory unsupervised approach for nonlinear fault identification Reviewed

    Matheus S. Escobar, Hiromasa Kaneko, Kimito Funatsu

    AICHE JOURNAL   61 ( 5 )   1559 - 1571   2015.5

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    DOI: 10.1002/aic.14748

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  • Fast optimization of hyperparameters for support vector regression models with highly predictive ability Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   142   64 - 69   2015.3

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    DOI: 10.1016/j.chemolab.2015.01.001

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  • Model for predicting transmembrane pressure jump for various membrane bioreactors Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    DESALINATION AND WATER TREATMENT   53 ( 6 )   1471 - 1481   2015.2

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    DOI: 10.1080/19443994.2014.943469

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  • Moving Window and Just-in-Time Soft Sensor Model Based on Time Differences Considering a Small Number of Measurements Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   54 ( 2 )   700 - 704   2015.1

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  • Is overfitting really a problem?

    Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2015   28 - 31   2015

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    Accuracy and applicability domains (ADs) of regression models are discussed in our presentation. Generally, we construct a regression model so as to prevent overfitting to training data and to have highly predictive performance for diverse compounds. However, an overfitted model must have highly predictive ability only within an AD, which is narrowly limited. In this study, the aqueous solubility data set was analyzed to compare performance of regression models while considering their ADs. Support vector regression (SVR) was used as a regression analysis method and hyperparameters of SVR changed. The ADs were set based on data density. There existed two types of SVR models. One is well-constructed SVR models that could predict solubility values for diverse compounds. The other is overfitted SVR models that seemed to have bad predictive ability but provided better prediction results for compounds within the ADs than the other type of SVR models. It was confirmed that overfitting itself was not a problem and we could operate overfitted models by setting their ADs appropriately.

    DOI: 10.11545/ciqs.2015.0_28

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  • Development of a New Feed-Forward Control Method Based on Soft Sensors and Inverse Analysis Reviewed

    Ippei Kimura, Hiromasa Kaneko, Kimito Funatsu

    KAGAKU KOGAKU RONBUNSHU   41 ( 1 )   29 - 37   2015

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    DOI: 10.1252/kakoronbunshu.41.29

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  • Improvement of Prediction Accuracy in Just-In-Time Modelling Using Distance-based Database Update Reviewed

    Tanaka Kenichi, Kaneko Hiromasa, Nagasaka Kyosuke, Funatsu Kimito

    Journal of Computer Aided Chemistry   16 ( 0 )   1 - 14   2015

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    Soft sensors have been widely used in chemical processes to predict values of difficult-to-measure process variables online. If the relationship between explanatory variables X and an objective variable y is changed by catalyst deterioration, change of product and so on, prediction accuracy of a soft sensor is reduced. This problem is called degradation of a soft sensor model. To overcome the degradation, many adaptive soft sensors have been proposed. In this paper, we aim to improve prediction accuracy of just-in-time (JIT) models. JIT models are constructed with only data close to a query or with all data having weights according to similarity with a query. If the type of degradation is shift of y-value, prediction accuracy of JIT models is reduced since data with similar X-values but different y-values are mixed in database and the relationship between X and y is not consistent. To resolve this problem, we propose to update database based on not only X-distance but also y-distance. The updated database is called JIT database. When a y-value is measured and a datum of X and y is obtained, data whose X-distance is low and y-distance is high from the datum are moved from JIT database to original database, and data whose X-distance and y-distance are both low are moved from original database to JIT database. To evaluate the performance of the proposed method, we used fifteen types of simulation data containing five types of state transition (Y-shift, X-shift, Slope-change, Y-shift + Slope-change and X-shift + Slope-change), and three types of transition speed (Instant, Rapid and Gradual). By using the proposed method, improvement of prediction accuracy of JIT models was achieved for all types of simulation data.

    DOI: 10.2751/jcac.16.1

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  • Development of TMP Prediction Model and TMP Jump Prediction Model in MBRs

    membrane   40 ( 6 )   337 - 341   2015

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    Membrane bioreactors (MBRs) have been widely used for wastewater treatment. Although complete solid–liquid separation can be achieved using membrane, MBRs are subject to membrane fouling. To enable chemical cleaning to be performed at an appropriate time, fouling must be predicted in the long–term. Fouling prediction corresponds to transmembrane pressure (TMP) prediction under a condition of constant–rate filtration. One of the reasons to make TMP difficult to predict is a TMP jump. After the long-term operation of MBR under the condition of constant–rate filtration, TMP increases rapidly, which is called a TMP jump. We therefore have been developing both a TMP prediction model and a TMP jump prediction model. A TMP prediction model can predict future TMP with high accuracy in the long–term. A TMP jump prediction model can accurately predict timing of TMP jumps. By using our proposed models, we can arrange a schedule of chemical cleaning and optimize operating conditions and water quality that can prevent MBR fouling.

    DOI: 10.5360/membrane.40.337

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  • Study of molecular design based on inverse-QSPR

    Miyao Tomoyuki, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2015   36 - 39   2015

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    Inverse-QSPR is a method to propose chemical structures having desired properties by inversely analyzing regression models. In general, QSPR models are neither surjective nor injective, so it is difficult to define the pre-image of these models. The authors once proposed to solve this problem by using probability distribution. In that method, Bayesian theorem played a crucial role to retrieve posterior distribution of independent variables given an objective variable value. Although that method works well for some case studies, regression models must be constructed using Multiple Linear Regression (MLR). This premise, however, does not fit many cases. To overcome this limitation, herewith we have developed two methodologies for inverse-QSPR. One is using different MLR models for each cluster, defined by Gaussian mixture models. The other is using Gaussian mixture regression. Both of them can analytically define the posterior probability distribution of independent variables for inverse analysis. We investigated how both of them capture features of data with nonlinearity and showed they worked well at least for a simulation dataset.

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  • Development of de novo design method generating diverse structures in a target area on chemical space

    Takeda Shunichi, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2015   100 - 103   2015

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    For drug development, methods of constructing a library consisting of diverse ligand candidates are required. We focused on de novo design algorithm for exploring chemical space (DAECS) which is the method generating many structures in a selected target area on the chemical space, and improved it. DAECS can generate only structures that exist in a specific area on a subspace set by using ligands data. But it is impossible to consider properties other than the activity and ensure the diversity of generated structures in DAECS. In this study, we introduce an area selection method with the visualization of drug-likeness distribution of the chemical space and a structural conversion method using substructures for solving the problem. To confirm superiority of our methods over the prior study, we performed a case study using a data set of ligands for human adrenergic alpha2A receptors from GVK database and showed the proposed methods can generate more diverse structures on a selected area and generate structures considering their drug-likeness.

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  • Development of calibration-minimum regression model with infrared spectra for mole fractions of pure components in mixture

    Shibayama Shojiro, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2015   96 - 99   2015

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    In pharmaceutical process, process state is monitored and managed by online and non-destructive spectroscopy testing and this methodology is focused on as Real Time Release Testing. Prediction of mole fractions of pure components in mixtures is an important issue for proper control in blending process. In order to predict mole fractions of pure components in mixtures with high accuracy, statistical models must be built from much amount of training data. However, only little amount of data is available, because taking much amount of data costs much. In this study, we proposed a calibration-minimum method that enables to predict mole fractions of pure components in non-ideal mixtures with high accuracy by expressing molecular interaction effect on a mixture spectrum as a function of mole fractions. The parameters in the proposed model equation can be inferred from little amount of data. The molecular interaction effect obtained from the proposed model is supposed to enhance further understanding of molecular interaction in complex mixtures.

    DOI: 10.11545/ciqs.2015.0_96

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  • The optimization of Lithium ion battery based on an adaptive experimental design method

    Nakao Atsuyuki, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2015   128 - 131   2015

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    Lithium ion batteries (LIB) are widely used in recent years, for example, a mobile PC, a smartphone, and an electric car. There are many design parameters for LIB such as size and material of components. Those parameters should be optimized depending on a use of LIB, which is a very difficult task because the number of combinations of design parameters is numerous and many properties of LIB should be considered simultaneously. We propose an optimization method based on an adaptive experimental design method. The probability P that a result of a simulation on a combination of design parameters will improve on the previous optimal value of an optimizing property and meet requirements of other properties is calculated in the proposed method. The combination of design parameters with the highest P-value is simulated next. The superiority of the proposed method was verified through comparison with a traditional method, and the optimized LIB could be proposed.

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  • Ring-System-Based Exhaustive Structure Generation for Inverse-QSPR/QSAR Reviewed

    Tomoyuki Miyao, Hiromasa Kaneko, Kimito Funatsu

    MOLECULAR INFORMATICS   33 ( 11-12 )   764 - 778   2014.12

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    DOI: 10.1002/minf.201400072

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  • Development of a New De Novo Design Algorithm for Exploring Chemical Space Reviewed

    Kazuaki Mishima, Hiromasa Kaneko, Kimito Funatsu

    MOLECULAR INFORMATICS   33 ( 11-12 )   779 - 789   2014.12

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    DOI: 10.1002/minf.201400056

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  • Selective Use of Adaptive Soft Sensors Based on Process State Reviewed

    Hiromasa Kaneko, Takeshi Okada, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   53 ( 41 )   15962 - 15968   2014.10

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  • Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   137   57 - 66   2014.10

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    DOI: 10.1016/j.chemolab.2014.06.008

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  • Flour concentration prediction using GAPLS and GAWLS focused on data sampling issues and applicability domain Reviewed

    Matheus S. Escobar, Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   137   33 - 46   2014.10

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    DOI: 10.1016/j.chemolab.2014.06.005

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  • Applicability Domain Based on Ensemble Learning in Classification and Regression Analyses Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    JOURNAL OF CHEMICAL INFORMATION AND MODELING   54 ( 9 )   2469 - 2482   2014.9

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    DOI: 10.1021/ci500364e

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  • Multivariate Statistical Process Control Method Including Soft Sensors for Both Early and Accurate Fault Detection Reviewed

    Yasuyuki Masuda, Hiromasa Kaneko, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   53 ( 20 )   8553 - 8564   2014.5

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  • Corrigendum to Nonlinear Regression Method with Variable Region Selection and Application to Soft Sensors [Chemom. Intell. Lab. Syst. 121 (2013) 26-32]

    Hiromasa Kaneko, Kimito Funatsu

    Chemometrics and Intelligent Laboratory Systems   132   176   2014.3

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    DOI: 10.1016/j.chemolab.2013.10.002

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  • Corrigendum to Strategic Parameter Search Method Based on Prediction Errors and Data Density for Efficient Product Design [Chemom. Intell. Lab. Syst. 127 (2013) 70-79]

    Takuya Kishio, Hiromasa Kaneko, Kimito Funatsu

    Chemometrics and Intelligent Laboratory Systems   132   177   2014.3

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  • Corrigendum to A chemometric approach to prediction of transmembrane pressure in membrane bioreactors [Chemom. Intell. Lab. Syst. 126 (2013) 30-37]

    Hiromasa Kaneko, Kimito Funatsu

    Chemometrics and Intelligent Laboratory Systems   132   175   2014.3

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    DOI: 10.1016/j.chemolab.2013.10.003

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  • Application of Online Support Vector Regression for Soft Sensors Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    AICHE JOURNAL   60 ( 2 )   600 - 612   2014.2

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    DOI: 10.1002/aic.14299

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  • Database Monitoring Index for Adaptive Soft Sensors and the Application to Industrial Process Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    AICHE JOURNAL   60 ( 1 )   160 - 169   2014.1

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    DOI: 10.1002/aic.14260

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  • Applicability Domain in Classification and Regression Analyses

    Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2014   O16 - O16   2014

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    We discuss applicability domains (ADs) based on ensemble learning in classification and regression analyses. In regression analysis, the AD can be appropriately set, although attention needs to be paid to the bias of predicted values. However, because the AD set in classification analysis is too wide, we propose an AD based on ensemble learning and data density. First, we set a threshold for data density, below which the prediction result of new data is not reliable. Then, only for new data with a data density higher than the threshold, we consider the reliability of the prediction result based on ensemble learning. By analyzing data from numerical simulations, we demonstrate that the ADs based on ensemble learning are too wide. Then, by using quantitative structure-property relationship data and quantitative structure-activity relationship data, we validate our discussion on ADs in classification and regression analyses and confirm that appropriate ADs can be set using the proposed method.

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  • Development of a Novel Spectra Analysis Method to Construct Accurate NIR Models Reviewed

    Kamma Koji, Kaneko Hiromasa, Funatsu Kimito

    Journal of Computer Aided Chemistry   15 ( 0 )   1 - 9   2014

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    Near-infrared spectroscopy (NIR) is widely used for non-destructive food quality check. The prediction models are constructed between NIR spectra and quality parameters. However, because of the noise included in spectra and the duplication between the peaks of the target components and those of the other components, the prediction accuracy of the models decreases. To avoid this problem, derivative spectra are used in modeling. Derivation of spectra has an effect to emphasize the small and narrow peaks so that the affection of peak duplication decreases. On the other hand, derivation of spectra also has an effect to enlarge the noise. The impacts of these effects change as the derivative changes, hence it is necessary to select the adequate derivative for each data. Besides, if there are several peaks of the target components, the adequate derivative is different for each peak. In this paper, we therefore construct regression models using the spectra, the first, second and third derivative spectra, and the combinations of them. The accuracy of the models which are constructed with different derivative spectra or the combinations of them changes when the number of the training data changes. Thus, we proposed a method to select the proper model according to the number of the training data. The selection is performed based on the prediction accuracy of each model. A simulation data set that mimics the spectra where three different peaks duplicate was analyzed using the proposed method. Then, the proposed method was applied to sugar content prediction of oranges. The results showed that the most accurate model changed as the number of the training data changed, and that the effectiveness of the proposed method was proved.

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  • Automatic Database Monitoring for Process Control Systems Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    MODERN ADVANCES IN APPLIED INTELLIGENCE, IEA/AIE 2014, PT I   8481   410 - 419   2014

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  • Analysis of a Transmembrane Pressure (TMP) Jump Prediction Model for Preventing TMP Jumps.

    K. Funatsu

    Desalination and Water Treatment   53 ( 1 )   1 - 6   2014

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  • Model for Predicting Transmembrane Pressure Jump for Various Membrane Bioreactors.

    K. Funatsu

    Desalination and Water Treatment   52 ( 1 )   1 - 11   2014

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  • Erratum to "A new process variable and dynamics selection method based on a genetic algorithm-based wavelength selection method" [58, 6, (2012) 1829-1840], DOI: 10.1002/aic.13814

    AIChE Journal   59   4888   2013.12

  • Erratum: Development of a New Regression Analysis Method Using Independent Component Analysis (Journal of Chemical Information and Modeling (2008) 48:3 (534-541))

    H. Kaneko, M. Arakawa, K. Funatsu

    Journal of Chemical Information and Modeling   53   3113   2013.11

  • Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    COMPUTERS & CHEMICAL ENGINEERING   58   288 - 297   2013.11

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    DOI: 10.1016/j.compchemeng.2013.07.016

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  • Estimation of predictive accuracy of soft sensor models based on data density Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   128   111 - 117   2013.10

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    DOI: 10.1016/j.chemolab.2013.08.005

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  • Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors (vol 52, pg 1322, 2013)

    H. Kaneko, K. Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   52 ( 40 )   14505 - 14505   2013.10

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  • Criterion for Evaluating the Predictive Ability of Nonlinear Regression Models without Cross-Validation Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    JOURNAL OF CHEMICAL INFORMATION AND MODELING   53 ( 9 )   2341 - 2348   2013.9

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  • Strategic parameter search method based on prediction errors and data density for efficient product design Reviewed

    Takuya Kishio, Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   127   70 - 79   2013.8

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    DOI: 10.1016/j.chemolab.2013.06.002

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  • A chemometric approach to prediction of transmembrane pressure in membrane bioreactors Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    Chemometrics and Intelligent Laboratory Systems   126   30 - 37   2013.7

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    DOI: 10.1016/j.chemolab.2013.04.016

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  • Classification of the Degradation of Soft Sensor Models and Discussion on Adaptive Models Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    AICHE JOURNAL   59 ( 7 )   2339 - 2347   2013.7

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    DOI: 10.1002/aic.14006

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  • Effects of Experimental Method on Aggregation State and Thermal Conductivity of Carbon Nanotube-Based Fluids Reviewed

    Y. Shimoda, T. Aoyama, H. Kaneko, Y. Onumata, F. Okada

    INTERNATIONAL JOURNAL OF THERMOPHYSICS   34 ( 7 )   1308 - 1324   2013.7

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    DOI: 10.1007/s10765-013-1481-4

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  • Applicability domain of soft sensor models based on one-class support vector machine Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    AICHE JOURNAL   59 ( 6 )   2046 - 2050   2013.6

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    DOI: 10.1002/aic.14010

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  • Development of Nonlinear Soft Sensor Methods Considering Process Dynamics

    KANEKO Hiromasa, FUNATSU Kimito

    Transactions of the Society of Instrument and Control Engineers   49 ( 2 )   206 - 213   2013.2

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    Soft sensors have been widely used for process control in industrial plants to estimate difficult-to-measure process variables online. A genetic algorithm-based process variables and dynamics selection (GAVDS) method is one method used to select important process variables and optimal time-delays of each variable simultaneously. However, the GAVDS method cannot handle a nonlinear relationship between <b>X</b> and an objective variable <b>y</b> because linear regression is used as a modeling technique. We therefore proposed a region selection method based on GAVDSand support vector regression (SVR), which is a nonlinear regression method. The proposed method is named GAVDS-SVR. We applied GAVDS-SVR to simulation data having high correlation between close pairs of <b>X</b>-variables and a nonlinear relationship between <b>X</b> and <b>y</b>. The GAVDS-SVR method could select regions of <b>X</b>-variables appropriately by considering the nonlinearity and could construct predictive models with high accuracy. Through soft-sensor analysis of industrial polymer process data, we confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed method.

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  • Physical and statistical model for predicting a transmembrane pressure jump for a membrane bioreactor Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    Chemometrics and Intelligent Laboratory Systems   121   66 - 74   2013.2

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    DOI: 10.1016/j.chemolab.2012.11.013

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  • Nonlinear regression method with variable region selection and application to soft sensors Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   121   26 - 32   2013.2

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    DOI: 10.1016/j.chemolab.2012.11.017

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  • Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   52 ( 3 )   1322 - 1334   2013.1

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  • Construction of two-dimensional quantitative structure retention relationship models and structure elucidation based on inverse analysis with QSRR models

    Kamma Koji, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2013   O10 - O10   2013

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    Gas chromatography (GC) and two-dimensional GC (GC-GC) are widely used for separation, structure elucidation and quantitative analysis. In GC and GC-GC, the chemical structure is elucidated by comparing the measured retention time of each compound with the database. But, structure elucidation is infeasible if the retention time is not available from the database. Thus, quantitative structure retention relationship (QSRR) is proposed to predict the retention time from the structure. Some researchers constructed the QSRR models specialized for the limited types of compounds. In this study, we aim to construct the QSRR models that can predict the retention time of various compounds in GC-GC with high accuracy. In addition, we propose a structure elucidation method based on the inverse analysis with the models. First, the objective value of the retention time is set. Then, structure elucidation is accomplished by comparing the objective value with the predicted retention time of new structures. The prediction errors can be a problem in comparison between the predicted and objective values. We deal with this problem by setting an acceptable error for each compound based on the reliability of the predicted value. The analysis with the measurement data proved the effectiveness of the proposed method.

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  • 「製造プロセスにおける情報化学技術の活用」特集について

    金子 弘昌, 高橋 崇宏

    日本化学会情報化学部会誌   31 ( 1 )   1 - 1   2013

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    DOI: 10.11546/cicsj.31.2

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  • Development of a Strategic Parameter Search Method forEfficient Product Design Reviewed

    KISHIO Takuya, KANEKO Hiromasa, FUNATSU Kimito

    Journal of Chemical Software   12 ( 2 )   113 - 121   2013

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    In experimental design for functional molecules, materials or products, complicated relationships exist between various experimental parameters and objective physical and chemical properties. Regression analysis with experimental data are a useful way for understanding those relationships. A constructed regression model can be used to effectively search for functional products. However, although those products can be found in domains out of existing data, the predictive ability of the model tends to be low in regions where data density is low, and new candidates whose predicted values of a property are unreliable will not achieve desired values of the property. Therefore to search for new candidates in appropriate extrapolation domains, we consider the probability that a new candidate will have intended values of a property and the reliability of a predicted value of the property for the candidate. The probability is calculated from a predicted value and its prediction error estimated by using the gaussian process model, and the reliability is based on data density calculated with the one-class support vector machine (OCSVM) model. The proposed method is applied to simulation data and aqueous solubility data, and the efficiency of the method could be confirmed.

    DOI: 10.2477/jccj.2012-0033

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  • Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate parameter settings Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    17TH INTERNATIONAL CONFERENCE IN KNOWLEDGE BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS - KES2013   22   580 - 589   2013

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    DOI: 10.1016/j.procs.2013.09.138

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  • Automatic Determination Method Based on Cross-Validation for Optimal Intervals of Time Difference Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    JOURNAL OF CHEMICAL ENGINEERING OF JAPAN   46 ( 3 )   219 - 225   2013

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    DOI: 10.1252/jcej.12we241

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  • 予測性を考慮した新規回帰分析手法の開発および二酸化炭素分離回収に用いるアミン化合物の分子設計

    三島 和晃, 船津 公人

    14 ( 1 )   1 - 10   2013

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  • Design of transfection reagents in RNAi therapeutics by chemoinformatics approach

    Sakai Yu, Kaneko Hiromasa, Ohta Seiichi, Itoh Taichi, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2013 ( 0 )   O6 - O6   2013

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    RNAi is a natural biological process involving gene silencing or regulation with siRNA and expected to be applied in the therapeutics of gene disorders. The delivery of siRNA into the cell is demonstrated using cationic lipid. The lipid is called 'Transfection reagents'and the development of new reagents has recently advanced. Some relation exists between the chemical structures of the reagents and their properties, but it is not clear yet quantitatively and the development of new reagents depends on the previous studies. In this study we collected the data of the transfection reagents from literature and constructed statistical models of QSAR models of transfection reagents in order to predict the chemical structures of high efficient reagents in future. The descriptors calculated for chemical structures and experimental parameters were used as explanatory variables, and their rate of gene expression was used as an objective variable. In this study we calculated descriptors for the structures of amines and carbon chains separately and constructed the regression models predicting the rate of gene expression. The value of q2 improved in PLS and SVR models compared with the value of q<sup>2</sup> calculated with the descriptors for the structures of transfection reagents. The descriptors derived from carbon chains had high correlations with the rate of gene expression compared with the correlations of the descriptors derived from amines.

    DOI: 10.11545/ciqs.2013.0.O6.0

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  • Visualization of Models Predicting Transmembrane Pressure Jump for Membrane Bioreactor Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   51 ( 28 )   9679 - 9686   2012.7

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    DOI: 10.1021/ie300727t

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  • Statistical Approach to Constructing Predictive Models for Thermal Resistance Based on Operating Conditions Reviewed

    Hiromasa Kaneko, Susumu Inasawa, Nagisa Morimoto, Mitsutaka Nakamura, Hirofumi Inokuchi, Yukio Yamaguchi, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   51 ( 29 )   9906 - 9912   2012.7

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    DOI: 10.1021/ie300315t

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  • A new process variable and dynamics selection method based on a genetic algorithm-based wavelength selection method Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    AICHE JOURNAL   58 ( 6 )   1829 - 1840   2012.6

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    DOI: 10.1002/aic.13814

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  • Development of high predictive soft sensor method and the application to industrial polymer processes Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING   7   S39 - S47   2012.5

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    DOI: 10.1002/apj.631

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  • Development of a model selection method based on the reliability of a soft sensor model

    Takeshi Okada, Hiromasa Kaneko, Kimito Funatsu

    Songklanakarin Journal of Science and Technology   34 ( 2 )   217 - 221   2012

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  • Prediction model of transmembrane pressure jump for membrane bioreactor using physical and statistical approaches Reviewed

    H. Kaneko, K. Funatsu

    EUROMEMBRANE CONFERENCE 2012   44   1366 - 1367   2012

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    DOI: 10.1016/j.proeng.2012.08.790

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  • Estimation of Predictive Accuracy of Soft Sensor Models Based on One-Class Support Vector Machine Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    11TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING, PTS A AND B   31   1246 - 1250   2012

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  • Development of An Adaptive Soft Sensor Method Considering Prediction Confidence of Models Reviewed

    OKADA Takeshi, KANEKO Hiromasa, FUNATSU Kimito

    Journal of Chemical Software   11 ( 1 )   24 - 30   2012

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    Soft sensors are widely used to realize highly efficient operation in chemical processes because not every important variable such as product quality can be measured online. By using soft sensors, one can estimate such a difficult-to-measure variable y from other process variables which are measured online. For estimating y without degradation of a soft sensor model, a time difference (TD) model was developed previously. Though a TD model has high predictive ability, it does not function well when a process is operated under conditions that have never been observed. In order to cope with this problem, a soft sensor model can be updated with newest data. But, updating a model needs time and effort for plant operators. We therefore developed an online monitoring system to judge whether a TD model can predict y accurately or an updating model should be used for both reducing maintenance cost and improving predictive accuracy of soft sensors. The monitoring system is based on a support vector machine or on standard deviations of y-values estimated from various intervals of time difference. We confirmed that the proposed system has functioned successfully in a distillation column with real industrial data.

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  • Construction of Statistical Models for Predicting the Presence of Azeotropy at Any Pressure in Separation Processes Reviewed

    KIM Taehyung, KANEKO Hiromasa, YAMASHIRO Naoya, FUNATSU Kimito

    Journal of Chemical Software   11 ( 2 )   112 - 120   2012

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    Distillation is one of the dominating separation processes, but there are some problems. One of those problems is that inseparable mixtures are formed in some cases. This phenomenon is called azeotropy. It is essential to understand azeotropy in any distillation processes since azeotropes, i.e., inseparable mixtures, cannot be separated by ordinary distillation. In this study, to construct a model which predicts the azeotropic formation at any pressure, a novel approach is presented with support vector machine (SVM). The SVM method is used to classify data in the two classes, that is, azeotropes and nonazeotropes. 13 variables including pressure were used as explanatory variables. From the result of comparing the SVM models which were constructed with data measured at 1 atm and data measured at any pressure, the 1 atm model shows a higher prediction performance to the data measured at 1 atm than any pressure model does. Thus, for improving the performance of the any pressure model, we focused on intermolecular forces of solvents. The SVM models were constructed with only data of the solvents having the same subgroups. The accuracy of the model increased and the model predicted change of the presence of azeotrope according to pressure. It is expected that this proposed method will be used to predict azeotropic formation at any pressure with high accuracy.

    DOI: 10.2477/jccj.2011-0028

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  • Development of Soft Sensor Methods Based on Wavelength Region Selection Methods Reviewed

    KANEKO Hiromasa, FUNATSU Kimito

    Journal of Chemical Software   11 ( 1 )   31 - 42   2012

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    Soft sensors have been widely used in industrial plants to estimate process variables that are difficult to measure online (Figure 1). Soft sensor models predicting an objective variable should be constructed with only important explanatory variables in terms of predictive ability, better interpretation of models and lower measurement costs. Besides, some process variables can affect an objective variable with time-delays. We therefore have proposed the methods for selecting important process variables and optimal time-delays of each variable simultaneously, by modifying the wavelength selection methods (Figure 3, 4) in spectrum analysis. The proposed methods can select time-regions of process variables as a unit by using process data that includes process variables that are delayed for a duration ranging from 0 through some decided time. A case study with real industrial data confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed methods (Table 2, 3, Figure 11, 12).

    DOI: 10.2477/jccj.2011-0011

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  • Construction of Long-Term Transmembrane Pressure Estimation Model for a Membrane Bioreactor Reviewed

    Sung Kyung-mo, Kaneko Hiromasa, Funatsu Kimito

    Journal of Computer Aided Chemistry   13 ( 0 )   10 - 19   2012

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    A membrane bioreactor (MBR) is equipment which filters polluted water such as factory disposal and sewage. Activated sludge is used to remove organic substances metabolically and filtrated to a membrane by transmembrane pressure (TMP). Since the MBR is able to treat water for a short time and has space-saving features, carrying out distributed installation of the MBR and performing unmanned operation to a building, a factory, and so on, attracts much attention as a solution of water-shortage. However, the rise of transmembrane pressure (TMP) which arises as a result of accumulation of foulants on a membrane is one of the biggest problems. Membrane needs to be washed when TMP reaches to some extent. The focus of this study is to estimate TMP with statistical models and also know when the membrane wash-up will become necessary. In this study, two types of statistical models were constructed between explanatory variables related to fouling and an objective variable, i.e., membrane resistance (R) or deposition rate of foulants to membrane (DR). Partial least squares (PLS) and support vector regression (SVR) were employed for the construction of each model. It is able to predict TMP because R or DR can be converted into TMP. As a result of TMP prediction with real industrial data, usage of DR as an objective variable and the SVR method improved the accuracy of TMP prediction.

    DOI: 10.2751/jcac.13.10

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  • Consideration of Soft Sensor Methods Based on Time Difference and Discussion on Intervals of Time Difference Reviewed

    Kaneko Hiromasa, Funatsu Kimito

    Journal of Computer Aided Chemistry   13 ( 0 )   29 - 43   2012

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    In chemical plants, soft sensors have been widely used to estimate difficult-to-measure process variables online. The predictive accuracy of soft sensors decreases due to changes in the state of chemical plants, and soft sensor models based on time difference (TD) have been constructed for reducing the effects of deterioration with age such as the drift. However, details on models based on TD (TD models) remain to be clarified. In this study, therefore, TD models were discussed in terms of noise and variance in data, auto-correlation in process variables, degree of model accuracy, and so on. Then, we theoretically clarified and formulated the difference of predictive accuracy between normal models and TD models. The relationships and the formulas of TD were verified through the analysis of simulation data. Furthermore, we analyzed dynamic simulation data with considering observed disturbances and unobserved disturbances, and confirmed that predictive accuracy of TD models increased by setting appropriate intervals of TD.

    DOI: 10.2751/jcac.13.29

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  • Proposal of a novel near-infrared spectral analysis method for constructing robust and high-precision models

    Kamma Koji, Kaneko Hiromasa, Funatsu Kimito

    Proceedings of the Symposium on Chemoinformatics   2012   1B1a - 1B1a   2012

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    Non-destructive testing of food quality with Near-Infrared Spectroscopy (NIR) is becoming common. The prediction models are constructed between NIR spectra and quality parameters. Many investigations have been done for the construction of high predictive models. Although some models indeed have suitably predictive accuracy, those models work well in only limited data domains and the accuracy decreases with time. Hence the models should be reconstructed with new data by wasting samples of objective foods and measuring the quality. To perform both the reduction of the loss of the food and the high performance of the models, overlapped peaks of NIR spectra should be considered because the overlapped peaks make relationships between NIR spectra and quality parameters unclear. Derivation of spectra is generally used to solve this problem. An adequate order of derivative changes depending on how peaks are overlapping, but the dependence of an adequate derivative order on the number of training samples remains to be clarified. Therefore we propose a method using some kinds of derivative order of spectra according to the number of samples for the construction of regression models. The effectiveness of the proposed method was confirmed thorough the analyses of simulation data and real data.

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  • A soft sensor method based on values predicted from multiple intervals of time difference for improvement and estimation of prediction accuracy Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   109 ( 2 )   197 - 206   2011.12

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    DOI: 10.1016/j.chemolab.2011.09.003

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  • Development of a Wavelength Region Selection Method Based on Genetic Algorithm-based WaveLength Selection and Support Vector Regression Reviewed

    KANEKO Hiromasa, FUNATSU Kimito

    Journal of Chemical Software   10 ( 1 )   122 - 130   2011.12

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    Regions of explanatory variables &lt;b&gt;X&lt;/b&gt; need to be selected in many fields such as spectral analysis and process control. Genetic algorithm-based wavelength selection (GAWLS) method is one of the methods that is used to select combinations of important variables from &lt;b&gt;X-&lt;/b&gt;variables using regions as a unit of measurement. However, a partial least-squares method is used as a regression method; hence, a GAWLS method cannot handle a nonlinear relationship between &lt;b&gt;X&lt;/b&gt; and an objective variable &lt;b&gt;y&lt;/b&gt;. We therefore proposed a region selection method based on GAWLS and support vector regression (SVR), one of the nonlinear regression methods, for achieving both appropriate selection of variable regions and construction of a high predictive model when there is a nonlinear relationship between &lt;b&gt;X&lt;/b&gt; and &lt;b&gt;y&lt;/b&gt;(Figure 1). The proposed method is named GAWLS-SVR. The &lt;i&gt;q&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; value of a SVR model, which is calculated using a cross-validation method, is used as a fitness value of the chromosome. In order to verify the effectiveness of the GAWLS-SVR method, we applied it to simulation data in which correlation between close pairs of &lt;b&gt;X&lt;/b&gt;-variables was high and the relationship between &lt;b&gt;X&lt;/b&gt; and &lt;b&gt;y&lt;/b&gt; was nonlinear. The GAWLS-SVR method could select regions of variables appropriately, while considering the nonlinearity and could construct a predictive model with high accuracy (Table 2, Figure 6).

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  • Development of a Model Predicting Transmembrane Pressure in Membrane Bioreactors Reviewed

    KANEKO Hiromasa, FUNATSU Kimito

    Journal of Chemical Software   10 ( 1 )   131 - 140   2011.12

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    Membrane bioreactors (MBRs) have been widely used to purify wastewater for reuse. However, MBRs are subject to fouling, which is the phenomenon whereby foulants absorb or deposit on the membrane. When MBR filtration is operated at a constant permeate flow rate, the transmembrane pressure (TMP) and the energy required to maintain the permeate rate increase with time. To enable chemical cleaning to be performed at an appropriate time, one must be able to predict membrane fouling in the long-term. There has been research on correlations among fouling phenomena, water quality variables, and operating conditions. Therefore, in this paper, we aimed to construct a statistical model between the increase in TMP and MBR parameters such as water quality variables and operating conditions and to use this model to predict TMP (Figure 1). In our study, two methods are used to construct regression models. One is a partial least-squares method and the other is a support vector regression method. We analyzed two data sets measured in a real industrial MBR plant and then confirmed that the constructed model could predict TMP over time with high accuracy (Figures 4, 5).

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  • Development of Soft Sensor Models Based on Time Difference of Process Variables with Accounting for Nonlinear Relationship Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH   50 ( 18 )   10643 - 10651   2011.9

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    DOI: 10.1021/ie200692m

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  • 測定困難な対象を推定するソフトセンサー

    船津 公人, 金子 弘昌

    化学工学 = Chemical engineering   75 ( 8 )   533 - 533   2011.8

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  • Maintenance-free soft sensor models with time difference of process variables Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS   107 ( 2 )   312 - 317   2011.7

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    DOI: 10.1016/j.chemolab.2011.04.016

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  • Applicability Domains and Accuracy of Prediction of Soft Sensor Models Reviewed

    Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu

    AICHE JOURNAL   57 ( 6 )   1506 - 1513   2011.6

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    DOI: 10.1002/aic.12351

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  • Novel soft sensor method for detecting completion of transition in industrial polymer processes Reviewed

    Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu

    COMPUTERS & CHEMICAL ENGINEERING   35 ( 6 )   1135 - 1142   2011.6

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    DOI: 10.1016/j.compchemeng.2010.09.003

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  • Construction of QSPR Models on Properties of Amine Compounds Based on Semi-Empirical Quantum Chemical Calculation and Suggestion of New Amine Compounds

    Mishima Kazuaki, Kaneko Hiromasa, Yamashiro Naoya, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences   2011   P11 - P11   2011

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    Carbon dioxide capture and storage (CCS) is widely performed as one of measures against global warming. In the present CCS, however, cost of carbon dioxide capture is high, and therefore it is strongly required to develop more effective absorbing method. Chemical absorption method with amine compounds which have hydroxyl grope is one of the potent methods for carbon dioxide capture. For decreasing the cost of carbon dioxide capture, novel amine compounds with high capability of absorption and diffusion is required. Hence, the objective of this study is to explore such amine compounds. To construct regression models for predicting the absorption rate and the amount of diffusion of amine compounds, we evaluated the amine compounds whose properties are measured, applying semi-empirical molecular orbital method. By using this method, steric effect and electric nature can be considered. After constructing regression models by the GAPLS (genetic algorithm based-partial least squares) method in which choice of predictive variables is carried out, we evaluated amine compounds which are virtually generated with a structure generator. By using their properties estimated with the GAPLS models, we explored novel amine compounds with desirable properties.

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  • Improvement and estimation of prediction accuracy of soft sensor models based on time difference Reviewed

    Hiromasa Kaneko, Kimito Funatsu

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   6703 ( 1 )   115 - 124   2011

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    DOI: 10.1007/978-3-642-21822-4_13

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  • Development of Statistical Models for Predicting Presence of Azeotropy at any Pressure

    Kim Taehyung, Kaneko Hiromasa, Yamashiro Naoya, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences   2011   O11 - O11   2011

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    Distillation is one of the dominating separation processes, but there are some problems that inseparable mixtures are formed in some cases. This phenomenon is called as azeotropy. It is essential to understand azeotropy in any distillation processes since azeotropes, i.e. inseparable mixtures, cannot be separated by ordinary distillation. In this study, to construct a model which predicts the azeotropic formation at any pressure, a novel approach is presented with support vector machine (SVM). The SVM method is used to classify data in the two classes, that is, azeotropes and nonazeotropes. 13 variables including pressure data were used as explanatory variables. From the result of comparing the SVM models which were constructed with data measured at 1atm and data measured at any pressure, the 1atm model shows a higher prediction performance to the data measured at 1atm than the any pressure model. Thus, for improving the performance of the any pressure model, we focused on intermolecular forces of solvents. The SVM models were constructed with only data of the solvents having same subgroups. The accuracy of the model increased and it is expected that this proposed method will be used to predict azeotropic formation at any pressure with high accuracy.

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  • A chemoinformatics approach to predicting rate of gene silencing on transfection reagent of RNAi therapeutics

    Sakai Yu, Kaneko Hiromasa, Ito Taichi, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences   2011   P13 - P13   2011

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    RNAi is a specific gene silencing mechanism triggered by siRNA. The application of RNAi for gene diseases requires the development of safe and effective delivery systems. For this reason, development of transfection reagents has been expanding. A transfection reagent is a lipid which introduces siRNA into a cell efficiently. We need to predict adequate structures for development of efficient reagents, because the efficiency of introducing siRNA into a cell and the rate of gene silencing differ depending on the structures of reagents. However, the structure-activity relationship has not been clear yet. We therefore proposed a chemoinformatics approach predicting the rate of gene silencing on transfection reagents from the structure by using a regression model constructed with database of transfection reagents. In this study, descriptors as explanatory variables in construction of the model were calculated with the softwares, DRAGON and CODESSA, and an objective variable was their Luciferase expression in the HeLa cell. By using a genetic algorithm based-partial least squares method, which is one of the variable selection methods, a high predictive model could be constructed with a small number of explanatory variables.

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  • Construction of transmembrane pressure estimation model for membrane bioreactor

    Sung Kyung-mo, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences   2011   O2 - O2   2011

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    A membrane bioreactor (MBR) is the equipment to filtrate sewage or factory disposal water by MF/UF membrane. Activated sludge is used to remove organic substances metabolically and filtrated to membrane by transmembrane pressure (TMP). Though MBR is able to remove activated sludge, other particles of raw water, or both of them effectively, increase of TMP as a result of fouling growth brought by deposition of particles is one of the biggest problems for improving its performance. Membrane needs to be washed when TMP reaches about 25-30 kPa. The focus of this study is to estimate TMP with statistical prediction models and also know when the membrane wash-up will become necessary for the realization of distributed MBR and its unattended operation. In this study, two statistical models were constructed between explanatory variables related to fouling and an objective variable, i.e., membrane resistance (R) or deposition rate of foulant (DR). It is able to predict TMP because R or DR could be converted into TMP. As results of TMP prediction with real industrial data, usage of DR as an objective variable improved the accuracy of TMP prediction.

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  • 高精度ソフトセンサーの開発とプロセス管理への応用

    金子 弘昌, 船津 公人

    化学工学 = Chemical engineering   74 ( 8 )   402 - 405   2010.8

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  • 実用的ソフトセンサーのためのモデル劣化問題解決への取り組み

    金子 弘昌, 船津 公人

    化学工学会 研究発表講演要旨集   2010   38 - 38   2010

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    DOI: 10.11491/scej.2010f.0.38.0

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  • 新規ソフトセンサー手法の開発およびプロセス管理への応用

    金子 弘昌

    日本化学会情報化学部会誌   28 ( 2 )   29 - 29   2010

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    DOI: 10.11546/cicsj.28.29

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  • Development of the time difference model capturing nonlinear relationships between variables and the application to soft sensors

    Hiromasa Kaneko, Kimito Funatsu

    Symposium on Chemical Information and Computer Sciences   2010   J02 - J02   2010

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    In industrial plants, soft sensors are widely used to estimate process variables that are difficult to measure online. Though regression models are reconstructed with new data in order to maintain their high accuracy, some problems remain in practice. Hence, it is attempted to construct soft sensor models based upon the time difference of an objective variable and that of explanatory variables for reducing the effects of deterioration with age such as the drift and gradual changes in the state of plants. However, the time difference model cannot account for the nonlinearity in process variables. Therefore, we have proposed to construct time difference models after modeling nonlinear relationship between and among process variables. Variables obtained by physical models or those calculated by statistical nonlinear regression methods are used to consider the nonlinearity, and then, a time difference model is constructed including these variables. We applied the proposed methods to the actual industrial data obtained during an industrial polymer process. The proposed models achieved high predictive accuracy and eased the bias of the prediction errors for both density and melt flow rate. We confirmed the usefulness of the proposed methods without reconstruction of soft sensor models.

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  • Development of a New Soft Sensor Method Using Independent Component Analysis and Partial Least Squares Reviewed

    Hiromasa Kaneko, Masamoto Arakawa, Kirnito Funatsu

    AICHE JOURNAL   55 ( 1 )   87 - 98   2009.1

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    DOI: 10.1002/aic.11648

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  • ポリマープラントにおけるトランジション終了判定モデルの構築とソフトセンサーへの応用

    金子 弘昌, 荒川 正幹, 船津 公人

    化学工学会 研究発表講演要旨集   2009   147 - 147   2009

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    DOI: 10.11491/scej.2009.0.147.0

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  • Proposition of a New Fault Detection Method Using Independent Component Analysis and Support Vector Machine for Developing of High Predictive Soft Sensor Reviewed

    Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu

    KAGAKU KOGAKU RONBUNSHU   35 ( 4 )   382 - 389   2009

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    DOI: 10.1252/kakoronbunshu.35.382

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  • QSAR/QSPRモデルの逆解析と適用範囲

    荒川 正幹, 金子 弘昌, 船津 公人

    日本化学会情報化学部会誌   27 ( 4 )   69 - 69   2009

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  • ソフトセンサーモデルの適用範囲と予測誤差の関係に関する研究

    金子 弘昌, 荒川 正幹, 船津 公人

    化学工学会 研究発表講演要旨集   2009   575 - 575   2009

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    DOI: 10.11491/scej.2009f.0.575.0

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  • Development of a new regression analysis method using independent component analysis Reviewed

    Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu

    JOURNAL OF CHEMICAL INFORMATION AND MODELING   48 ( 3 )   534 - 541   2008.3

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    DOI: 10.1021/ci700245f

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  • 独立成分分析とサポートベクターマシンを組み合わせた新規異常値検出手法の提案とソフトセンサーへの応用

    金子 弘昌, 荒川 正幹, 船津 公人

    化学工学会 研究発表講演要旨集   2008   509 - 509   2008

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    Language:Japanese   Publisher:公益社団法人 化学工学会  

    DOI: 10.11491/scej.2008f.0.509.0

    CiNii Research

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  • 新規ソフトセンサー手法の開発とプロセス管理への応用

    金子 弘昌, 荒川 正幹, 船津 公人

    化学工学会 研究発表講演要旨集   2008   257 - 257   2008

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    DOI: 10.11491/scej.2008.0.257.0

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  • Development of The New Regression Analysis Method Using Independent Component Analysis and Genetic Algorithm Reviewed

    Kaneko Hiromasa, Arakawa Masamoto, Funatsu Kimito

    Journal of Computer Aided Chemistry   8   41 - 49   2007

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    In this paper, independent component analysis (ICA) and regression analysis are combined to extract significant components. ICA is a method that extracts mutually independent components from explanatory variables. We propose a new method that selects combination of independent components by using genetic algorithm (GA). It can construct a PLS model that has high predictive accuracy. This method is named ICA-GAPLS. In order to verify the superiority of ICA-GAPLS, this method was applied to QSPR analysis of aqueous solubility. The result of comparison with PLS and other regression methods is shown. R2, Q2 and Rpred2 values of the PLS model are 0.826, 0.821 and 0.790, respectively. These values of the ICA-GAPLS model are 0.945, 0.882 and 0.889, respectively. ICA-GAPLS achieved higher predictive accuracy than PLS. ICA-GAPLS showed better result regarding Q2 and Rpred2 value than other methods. ICA-GAPLS could extract effective components from explanatory variables and construct the regression model having high predictive accuracy.

    DOI: 10.2751/jcac.8.41

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Books

  • 化学のための Pythonによるデータ解析・機械学習入門

    金子 弘昌( Role: Sole author)

    オーム社  2019.10 

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  • ファウリングの原因と対策・抑制技術

    渡辺 義公, 山村 寛, 池嶋 規人, 糸川 博然, 森田 優香子, 島村 和彰, 中村 一穂, 根本 雄一, 薮野 洋平, 中西 弘貴, 中川 彰利, 船津 公人, 金子 弘昌, 伊藤 世人, 熊野 淳夫, 加藤 玲朋, 寺田 昭彦, 赤松 憲樹, 高羽 洋充, 的場 雄介, 澤田 繁樹( Role: Joint author第4節 MBRにおける長期的ファウリング予測およびファウリングの進行を抑制する運転方法探索)

    S&T出版株式会社  2016.2  ( ISBN:4907002521

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    Total pages:230  

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  • Soft Sensors: Chemoinformatic Model for Efficient Control and Operation in Chemical Plants.

    K. Funatsu( Role: Joint authorChapter 9)

    2016 

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    Responsible for pages:159-174   Language:English   Book type:Scholarly book

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  • Data Visualization & Clustering: Generative Topographic Mapping Similarity Assessment Allied to Graph Theory Clustering.

    ( Role: Joint authorChapter 10)

    2016 

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    Responsible for pages:175-210   Language:English   Book type:Scholarly book

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  • 化学工場・研究所の事故・災害対策とリスク管理

    田崎裕人( Role: Joint author第5章 プラント制御システムの設計とその応用 第4節ソフトセンサーを使った化学プロセスの安全管理)

    技術情報協会  2015.4  ( ISBN:4861045843

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    Total pages:651  

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  • 濾過スケールアップの正しい進め方と成功事例集

    菅原隆( Role: Joint author第10章 濾過膜のファウリングトラブル対策 第1節 長期ファウリング予測モデル)

    技術情報協会  2014.8  ( ISBN:4861045371

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    Total pages:531  

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  • ソフトセンサー入門―基礎から実用的研究例まで

    船津 公人, 金子 弘昌( Role: Joint author)

    コロナ社  2014.7  ( ISBN:4339066338

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    Total pages:227  

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  • ソフトセンサー入門 -基礎から実用的研究例まで

    船津 公人( Role: Joint author)

    コロナ社  2014 

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  • 化学分野におけるプロセスシステムの計測・モニタリング技術

    金子弘昌( Role: Joint authorソフトセンサー ~測定困難な対象を高精度で推定する技術~)

    シーエムシー出版  2011.7 

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MISC

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Presentations

  • データ駆動型モデルを活用した分子設計・材料設計・プロセス設計・プロセス管理 Invited

    新化学技術推進協会(JACI) 高分子シミュレーション技術セミナー  2018.11 

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  • Measure of Regression Model Accuracy for Quantitative Structure-Activity(Property) Relationship Considering Applicability Domains Invited International conference

    International Congress on Pure & Applied Chemistry (ICPAC)  2018.3 

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  • 化学産業におけるデータ活用 Invited

    INCHEM TOKYO 2017産学官マッチングフォーラム  2017.11 

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  • データベースおよびインフォマティクス技術を活用した分子設計・材料設計・プロセス設計 Invited

    高分子計算機科学研究会  2017.10 

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  • Process Design and Process Control Based on Statistical Analysis and Machine Learning Using Big Data Invited International conference

    The 8th China-Japan Symposium on Chemical Engineering  2017.10 

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  • Molecular, Material, Product and Process Design, and Process Control Based on Statistics and Informatics Invited International conference

    ISPAC2017  2017.6 

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  • Visualization of chemical space and protein space considering compound-protein interaction

    2016.11 

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  • QSAR/QSPRにおける適用範囲内への望ましい物性・活性をもつ構造の生成

    越智 奨貴, 宮尾 知幸, 船津 公人

    日本コンピュータ化学会2016秋季大会  2016.10 

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    Venue:島根大学  

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  • 縮約グラフを利用した化学グラフ生成に関する研究

    宮尾 知幸, 船津 公人

    第39回ケモインフォマティクス討論会  2016.9 

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    Venue:静岡大学浜松キャンパス  

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  • Generative Topographic Mapping Visualization Performance Allied to Root Mean Square Error of Midpoints among Nearest Neighbors

    M.S. Escobar, K. Funatsu

    2016.9 

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  • アンサンブル学習を活用した産業プラントにおける異常検出および異常状態推定

    船津 公人

    第39回ケモインフォマティクス討論会  2016.9 

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    Venue:静岡大学浜松キャンパス  

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  • 変数領域選択と適用範囲を考慮した土壌成分値予測のための非線形モデル開発

    厳 路, M.S. Escobar, 船津 公人

    第39回ケモインフォマティクス討論会  2016.9 

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    Venue:静岡大学浜松キャンパス  

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  • 適応型ソフトセンサーおよび推定値の平滑化を実現するソフトセンサーツールの開発

    大寳 茂樹, 松本 卓也, 船津 公人

    化学工学会第48回秋季大会  2016.9 

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    Venue:徳島大学  

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  • 赤外スペクトルを用いたプロセス監視のための波長選択手法

    柴山 翔二郎, 船津 公人

    化学工学会第48回秋季大会  2016.9 

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    Venue:徳島大学  

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  • Improvement of Process State Recognition Performance by Noise Reduction. International conference

    K. Funatsu

    PSE ASIA 2016  2016.7 

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    Venue:Tokyo, Japan  

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  • Generative Topographic Mapping Similarity Index Applied for Fault Detection in Chemical Plants. International conference

    M. S. Escobar, K. Funatsu

    PSE ASIA 2016  2016.7 

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    Venue:Tokyo, Japan  

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  • Development of a New Process Control Method for MIMO Process Based on Soft Sensors and Inverse Analysis. International conference

    T. Watanabe, K. Tanaka, K. Funatsu

    PSE ASIA 2016  2016.7 

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    Venue:Tokyo, Japan  

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  • Process Analytical Technologyとしてのソフトセンサーによる、リアルタイムプロセス管理技術 Invited

    第17回ヤングプロフェッショナルのためのセミナー  2016.7 

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    Venue:ISPE 日本本部オフィス  

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  • リアルタイムなプロセス監視および制御のためのProcess Analytical Technologyとしてのソフトセンサー Invited

    独立行政法人 医薬品医療機器総合機構での講演  2016.6  医薬品医療機器総合機構

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  • Practical Use of Savitzky-Golay Filtering-Based Ensemble Online SVR. International conference

    T. Matsumoto, S. Ootakara, K. Funatsu

    DYCOPS-CAB2016  2016.6 

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    Venue:Trondheim, Norway  

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  • ソフトセンサーによる製品品質の推定およびプロセス監視・制御への応用 Invited

    技術情報協会セミナー  2016.5 

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    Venue:技術情報協会セミナールーム  

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  • Partial Derivative of Data Density Estimation Model for Structure Generation and Fault Diagnosis. International conference

    K. Funatsu

    The 6th French-Japanese Workshop on Computational Methods in Chemistry  2016.3 

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    Venue:Sugiura Comunity Care Research Center in Kyoto University  

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  • Generative Topographic Mapping Similarity Assessment for Anomaly Detection in Chemical Plants. International conference

    M.S. Escobar, K. Funatsu

    The 6th French-Japanese Workshop on Computational Methods in Chemistry  2016.3 

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    Venue:Sugiura Comunity Care Research Center in Kyoto University  

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  • Molecular Structure Generation Algorithm for Inverse QSPR/QSAR. International conference

    T. Miyao, K. Funatsu

    The 6th French-Japanese Workshop on Computational Methods in Chemistry.  2016.3 

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    Venue:Sugiura Comunity Care Research Center in Kyoto University.  

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  • Molecular Structure Generation Algorithm for Inverse QSPR/QSAR International conference

    T. Miyao, H. Kaneko, K. Funatsu

    The 6th French-Japanese Workshop on Computational Methods in Chemistry  2016.3 

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    Venue:Sugiura Comunity Care Research Center in Kyoto University  

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  • Partial Derivative of Data Density Estimation Model for Structure Generation and Fault Diagnosis International conference

    H. Kaneko, K. Funatsu

    The 6th French-Japanese Workshop on Computational Methods in Chemistry  2016.3 

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    Venue:Sugiura Comunity Care Research Center in Kyoto University  

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  • Generative Topographic Mapping Similarity Assessment for Anomaly Detection in Chemical Plants International conference

    M.S. Escobar, H. Kaneko, K. Funatsu

    The 6th French-Japanese Workshop on Computational Methods in Chemistry  2016.3 

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    Venue:Sugiura Comunity Care Research Center in Kyoto University  

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  • 適応型ソフトセンサーにおけるハイパーパラメータ設計の高速化

    船津 公人

    化学工学会 第81年会  2016.3 

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    Venue:関西大学千里山キャンパス  

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  • Strategy of Structure Generation within Applicability Domains. International conference

    K. Funatsu

    PACIFICHEM 2015  2015.12 

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    Venue:Hawaii, U.S.A.  

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  • Strategy of Structure Generation within Applicability Domains International conference

    H. Kaneko, K. Funatsu

    PACIFICHEM 2015  2015.12 

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    Venue:Hawaii, U.S.A  

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  • プラントのビッグデータを活用するソフトセンサー技術および製造プロセス管理手法 Invited

    金子 弘昌

    ISPE日本本部2015年度冬季大会  2015.12 

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    Venue:メルパルク大阪  

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  • 低コストで運用可能な赤外分光法を用いたプロセス監視手法の開発

    柴山 翔二郎, 船津 公人

    日本PDA製薬学会第22回年会  2015.12 

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    Venue:タワーホール船堀  

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  • Iterative Optimization Technologyとスペクトルの低次元化・波長選択とを組み合わせたPAT手法の開発

    船津 公人

    日本PDA製薬学会第22回年会  2015.12 

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    Venue:タワーホール船堀  

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  • Ensemble Locally-weighted Partial Least Squares Model and Its Application to Industrial Plants. International conference

    K. Funatsu

    2015 AIChE Annual Meeting  2015.11 

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    Language:English   Presentation type:Poster presentation  

    Venue:Salt Lake City, U.S.A  

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  • Ensemble Locally-weighted Partial Least Squares Model and Its Application to Industrial Plants International conference

    H. Kaneko, K. Funatsu

    2015 AIChE Annual Meeting  2015.11 

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    Venue:Salt Lake City, U.S.A.  

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  • データベース更新によるJust-In-Timeモデルの予測精度の改善

    田中 健一, 船津 公人

    第38回ケモインフォマティクス討論会  2015.10 

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    Venue:東京大学  

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  • Inverse-QSPRを利用した分子設計に関する研究

    宮尾 知幸, 船津 公人

    第38回ケモインフォマティクス討論会  2015.10 

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    Venue:東京大学  

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  • オーバーフィッティングは本当に問題か?

    船津 公人

    第38回ケモインフォマティクス討論会  2015.10 

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    Venue:東京大学  

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  • 目的領域に多様な構造を生成する化学空間探索型構造ジェネレータの開発

    武田 俊一, 船津 公人

    第38回ケモインフォマティクス討論会  2015.10 

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    Venue:東京大学  

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  • 赤外スペクトルデータを用いた混合溶液における純物質の濃度予測モデル構築手法の開発

    柴山 翔二郎, 船津 公人

    第38回ケモインフォマティクス討論会  2015.10 

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    Venue:東京大学  

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  • 適応的実験計画手法を活用したリチウムイオン二次電池の最適化

    中尾 篤之, 船津 公人

    第38回ケモインフォマティクス討論会  2015.10 

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    Venue:東京大学  

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  • Noise Reduction of Operating Date Using Savizky-Golay Filters for Soft Sensors. International conference

    K. Funatsu

    The 16th Asia Pacific Confederation of Chemical Engineering Congress.  2015.9 

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    Venue:Melbourne, Australia  

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  • Noise Reduction of Operating Data Using Savitzky?Golay Filters for Soft Sensors International conference

    H. Kaneko, K. Funatsu

    The 16th Asia Pacific Confederation of Chemical Engineering Congress  2015.9 

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    Venue:Melbourne, Australia  

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  • Just-in-timeモデルおよびアンサンブル学習を活用した適応型ソフトセンサー手法

    船津 公人

    化学工学会第47回秋季大会  2015.9 

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    Venue:北海道大学  

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  • ケモインフォマティクス技術を活用した推定制御手法および医薬品製造プロセスへの応用 Invited

    金子 弘昌

    2015年度第1回CACフォーラムセミナー  2015.7 

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    Venue:日本化学会化学会館  

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  • Selection of Comprehensive Data from a Large Amount of Data using a Genetic Algorithm. International conference

    K. Funatsu

    PSE2015/ESCAPE25  2015.6 

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    Venue:Copenhagen, Denmark  

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  • Selection of Comprehensive Data from a Large Amount of Data using a Genetic Algorithm International conference

    H. Kaneko, K. Funatsu

    PSE2015/ESCAPE25  2015.6 

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    Venue:Copenhagen, Denmark  

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  • De-novoデザインのためのring-systemに基づいた化学構造創出

    宮尾 知幸, 船津 公人

    日本薬学会第135年会  2015.3 

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    Venue:神戸学院大学  

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  • 化学プラントにおける制御性能向上のための推定制御手法に関する研究

    化学工学会 第80年会  2015.3 

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    Venue:芝浦工業大学  

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  • Generative topographic mapping and graph theory combined approach for non-linear fault identification and diagnosis

    M.S. Escobar, K. Funatsu

    2015.3 

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  • 数理モデルを活用したMBRにおける運転条件の最適化の検討

    船津 公人

    化学工学会 第80年会  2015.3 

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    Venue:芝浦工業大学  

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  • 膜ファウリングの長期予測モデルの構築およびMBRの管理への応用

    CREST「持続可能な水利用を実現する革新的な技術とシステム」研究領域シンポジウム 「新たな水処理システムを目指した技術開発:バイオフィルムと膜ファウリングへの挑戦」  2015.3 

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    Venue:アルカディア市ヶ谷  

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  • The development of widely applied long-term transmembrane pressure prediction model for membrane bioreactors. International conference

    H. Oishi, K. Funatsu

    3W Expo 2015 + CPPE Expo 2015  2015.1 

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    Venue:Bangkok, Thailand  

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  • Application of Ensemble Online Support Vector Regression to the Prediction of Fouling in Membrane Bioreactors International conference

    H. Kaneko, K. Funatsu

    3W Expo 2015 + CPPE Expo 2015  2015.1 

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    Venue:Bangkok, Thailand  

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  • The development of widely applied long-term transmembrane pressure prediction model for membrane bioreactors International conference

    H. Oishi, H. Kaneko, K. Funatsu

    3W Expo 2015 + CPPE Expo 2015  2015.1 

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    Venue:Bangkok, Thailand  

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  • Application of Ensemble Online Support Vector Regression to the Prediction of Fouling in Membrane Bioreactors. International conference

    K. Funatsu

    3W Expo 2015 + CPPE Expo 2015  2015.1 

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    Venue:Bangkok, Thailand  

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  • クラス分類および回帰分析におけるモデルの適用範囲

    船津 公人

    第37回情報化学討論会  2014.11 

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    Venue:豊橋技術科学大学  

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  • Improvement of Iterative Optimization Technology (Calibration-Free/ Minimum Approach) with Dimensionality Reduction of Spectra. International conference

    K. Muteki, D.O. Blackwood, Y.A. Liu, S. S.Sekulic, K. Funatsu

    2014 AIChE Annual Meeting  2014.11 

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    Venue:Atlanta, U.S.A.  

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  • Improvement of Iterative Optimization Technology (Calibration-Free/ Minimum Approach) with Dimensionality Reduction of Spectra International conference

    H. Kaneko, K. Muteki, D.O. Blackwood, Y.A. Liu, S. S.Sekulic, K. Funatsu

    2014 AIChE Annual Meeting  2014.11 

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    Venue:Atlanta, U.S.A.  

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  • Process Control Method Based on the Inverse Analysis of Soft Sensors Considering Controllability International conference

    I. Kimura, H. Kaneko, K. Funatsu

    2014 AIChE Annual Meeting  2014.11 

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    Venue:Atlanta, U.S.A.  

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  • Process Control Method Based on the Inverse Analysis of Soft Sensors Considering Controllability. International conference

    I. Kimura, K. Funatsu

    2014 AIChE Annual Meeting  2014.11 

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    Venue:Atlanta, U.S.A.  

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  • データ密度に基づく異常検出モデルを用いた異常原因の診断

    船津 公人

    第57回自動制御連合講演会  2014.11 

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    Venue:群馬伊香保 ホテル天坊  

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  • GTL プラント運転データの測定間隔と適応型ソフトセンサーの予測精度

    大石 隼人, 船津 公人

    第57回自動制御連合講演会,  2014.11 

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    Venue:群馬伊香保 ホテル天坊  

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  • 膜分離活性汚泥法におけるファウリング予測モデルおよびモデルを用いた運転条件の検討 Invited

    金子 弘昌

    先端膜工学研究推進機構秋季講演会  2014.9 

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    Venue:神戸大学  

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  • Non-linear data visualization and networks combined approach for monitoring of process data

    M.S. Escobar, K. Funatsu

    化学工学会第46回秋季大会  2014.9 

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    Venue:九州大学  

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  • Moving windowモデルおよびアンサンブル学習を活用した適応型ソフトセンサー手法

    船津 公人

    化学工学会第46回秋季大会  2014.9 

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    Venue:九州大学  

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  • ファウリング予測モデルの最適運用および長期膜差圧予測

    大石 隼人, 船津 公人

    化学工学会第46回秋季大会  2014.9 

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    Venue:九州大学  

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  • Fast Optimization of Hyperparameters of Support Vector Regression Model Considering its Predictive Ability. International conference

    K. Funatsu

    4th International Conference on Engineering Optimization(EngOpt2014)  2014.9 

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    Venue:Lisbon, Portugal  

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  • Fast Optimization of Hyperparameters of Support Vector Regression Model Considering its Predictive Ability International conference

    H. Kaneko, K. Funatsu

    4th International Conference on Engineering Optimization  2014.9 

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    Venue:Lisbon, Portugal  

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  • Development of an adaptive experimental design method based on probabilities to achieve requirements and quantity of information on next experiments International conference

    A. Nakao, H. Kaneko, K. Funatsu

    4th International Conference on Engineering Optimization  2014.9 

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    Venue:Lisbon, Portugal  

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  • Development of an adaptive experimental design method based on probabilities to achieve requirements and quantity of information on next experiments. International conference

    A. Nakao, K. Funatsu

    EngOpt2014  2014.9 

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    Venue:Lisbon, Portugal  

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  • Semi-supervised learning state discrimination and regression modeling for dynamic data. International conference

    M.S. Escobar, K. Funatsu

    The 5th French-Japanese Workshop on Computational Methods in Chemistry  2014.6 

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    Venue:Strasbourg, France  

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  • Semi-supervised learning state discrimination and regression modeling for dynamic data International conference

    M.S. Escobar, H. Kaneko, K. Funatsu

    The 5th French-Japanese Workshop on Computational Methods in Chemistry  2014.6 

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    Venue:Strasbourg, France  

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  • Efficient molecular structure enumeration algorithm for inverse-QSPR/QSAR International conference

    T. Miyao, H. Kaneko, K. Funatsu

    The 5th French-Japanese Workshop on Computational Methods in Chemistry  2014.6 

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    Venue:Strasbourg, France  

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  • Efficient molecular structure enumeration algorithm for inverse-QSPR/QSAR. International conference

    T. Miyao, K. Funatsu

    The 5th French-Japanese Workshop on Computational Methods in Chemistry  2014.6 

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    Venue:Strasbourg, France  

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  • Virtual Sensors Predicting Drug Product Quality with Chemoinformatic Techniques. Invited

    JCUP V  2014.6 

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    Venue:Asahi Seimei Building, Japan.  

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  • Virtual Sensors Predicting Drug Product Quality with Chemoinformatic Techniques Invited International conference

    H. Kaneko

    JCUP V  2014.6 

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    Venue:Asahi Seimei Building  

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  • Automatic Database Monitoring for Process Control Systems. International conference

    K. Funatsu

    The 27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE2014).  2014.6 

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    Venue:Kaohsiung, Taiwan  

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  • Automatic Database Monitoring for Process Control Systems International conference

    H. Kaneko, K. Funatsu

    The 27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems  2014.6 

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    Venue:Kaohsiung, Taiwan  

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  • Adaptive regression model for nonlinear and time-varying systems International conference

    H. Kaneko, K. Funatsu

    The 5th French-Japanese Workshop on Computational Methods in Chemistry  2014.6 

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    Venue:Strasbourg, France  

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  • Adaptive regression model for nonlinear and time-varying systems. International conference

    K. Funatsu

    The 5th French-Japanese Workshop on Computational Methods in Chemistry.  2014.6 

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    Venue:Strasbourg, France  

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  • プラントの運転データを最大限に活用するためのソフトセンサーおよびプロセス管理手法 Invited

    金子 弘昌

    プラントオペレーション分科会 第131回研究会  2014.5 

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    Venue:大阪科学技術センター  

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  • Analysis of a Transmembrane Pressure (TMP) Jump Prediction Model for Preventing TMP Jumps. International conference

    K. Funatsu

    DESALINATION, FOR THE ENVIRONMENT  2014.5 

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    Venue:Limassol, Cyprus  

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  • Analysis of a Transmembrane Pressure (TMP) Jump Prediction Model for Preventing TMP Jumps International conference

    H. Kaneko, K. Funatsu

    DESALINATION, FOR THE ENVIRONMENT  2014.5 

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    Venue:Limassol, Cyprus  

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  • ソフトセンサーにおけるデータベース管理のための自動的パラメータ選択

    船津 公人

    化学工学会 第79年会  2014.3 

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    Venue:岐阜大学  

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  • GTM, GMM and SSPCR combined approach applied for semi-supervised state recognition

    M.S. Escobar, K. Funatsu

    2014.3 

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  • Membrane bioreactorにおける水質を考慮した更新型長期膜差圧予測手法の開発

    大石 隼人, 船津 公人

    化学工学会 第79年会  2014.3 

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    Venue:岐阜大学  

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  • ソフトセンサーと逆解析を利用した新規プロセス制御手法の開発

    木村 一平, 船津 公人

    化学工学会 第79年会  2014.3 

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    Venue:岐阜大学  

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  • Prediction models of transmembrane pressure (TMP) and timing of TMP jumps for efficient fouling control in distributed MBR systems. International conference

    K. Funatsu

    MBR for the Next Generation and Waste-to-Energy Conversion.  2014.2 

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    Venue:Gunsan, Korea  

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  • Prediction models of transmembrane pressure (TMP) and timing of TMP jumps for efficient fouling control in distributed MBR systems International conference

    H. Kaneko, K. Funatsu

    MBR for the Next Generation and Waste-to-Energy Conversion  2014.2 

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    Venue:Gunsan, Korea  

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  • Adaptive Soft Sensor Model Using Online Support Vector Regression and the Time Variable International conference

    H. Kaneko, K. Funatsu

    2013 AIChE Annual Meeting  2013.11 

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    Venue:San Francisco  

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  • GTM Semi-supervised Approach for State Recognition in Dynamic Data International conference

    M.S. Escobar, H. Kaneko, K. Funatsu

    2013 AIChE Annual Meeting  2013.11 

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    Venue:San Francisco  

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  • Adaptive Soft Sensor Model Using Online Support Vector Regression and the Time Variable. International conference

    K. Funatsu

    2013 AIChE Annual Meeting  2013.11 

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    Venue:San Francisco, U.S.A.  

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  • GTLプラント運転データの統計的処理と性状推定(ソフトセンサー)

    大石 隼人, 坂本 克, 木村 としや, 船津 公人

    第56回自動制御連合講演会  2013.11 

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    Venue:新潟大学  

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  • GTM Semi-supervised Approach for State Recognition in Dynamic Data. International conference

    M.S. Escobar, K. Funatsu

    2013 AIChE Annual Meeting  2013.11 

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    Venue:San Francisco, U.S.A  

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  • Membrane bioreactor における長期的ファウリング予測モデルの開発

    大石 隼人, 船津 公人

    第36回情報化学討論会  2013.11 

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    Venue:筑波大学  

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  • プロセス制御におけるソフトセンサーの新たな利用方法の開発

    木村 一平, 船津 公人

    第36回情報化学討論会  2013.11 

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    Venue:筑波大学  

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  • 化学空間上の目的領域内を探索する構造ジェネレータの開発

    三島 和晃, 船津 公人

    第36回情報化学討論会  2013.11 

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    Venue:筑波大学  

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  • 膜分離活性汚泥法における膜差圧急上昇予測モデルの開発

    船津 公人

    日本コンピュータ化学会2013秋季年会  2013.10 

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    Venue:九州大学  

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  • Model for Predicting Transmembrane Pressure Jump for Various Membrane Bioreactors International conference

    H. Kaneko, K. Funatsu

    Engineering with Membranes Towards a Sustainable Future - EWM2013  2013.9 

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    Venue:France  

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  • Adaptive Soft Sensor Model Using Online Support Vector Regression with the Time Variable and Discussion on Appropriate Parameter Settings International conference

    H. Kaneko, K. Funatsu

    17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems - KES2013  2013.9 

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    Venue:Japan  

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  • Adaptive Soft Sensor Model Using Online Support Vector Regression with the Time Variable and Discussion on Appropriate Parameter Settings. International conference

    K. Funatsu

    17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems - KES2013  2013.9 

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    Venue:Japan  

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  • Membrane bioreactorにおける更新型長期膜差圧予測モデルの開発

    大石 隼人, 船津 公人

    化学工学会第45回秋季大会  2013.9 

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    Venue:岡山大学  

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  • Model for Predicting Transmembrane Pressure Jump for Various Membrane Bioreactors. International conference

    K. Funatsu

    Engineering with Membranes Towards a Sustainable Future - EWM2013  2013.9 

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    Venue:Saint-Pierre D'Oléron, France  

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  • ソフトセンサーを活用したプラントにおける異常の早期検出

    増田 泰之, 船津 公人

    化学工学会第45回秋季大会  2013.9 

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    Venue:岡山大学  

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  • プロセス制御におけるソフトセンサーの新たな利用方法の開発

    木村 一平, 船津 公人

    化学工学会第45回秋季大会  2013.9 

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    Venue:岡山大学  

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  • 適応型ソフトセンサーのためのデータベース管理指標の開発

    船津 公人

    化学工学会第45回秋季大会  2013.9 

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    Venue:岡山大学  

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  • High Predictive Soft Sensors Based on Time Difference (TD) Models and the Selection of Optimal TD Intervals. International conference

    K. Funatsu

    9th World Congress of Chemical Engineering  2013.8 

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    Venue:Seoul, Korea  

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  • High Predictive Soft Sensors Based on Time Difference (TD) Models and the Selection of Optimal TD Intervals International conference

    H. Kaneko, K. Funatsu

    9th World Congress of Chemical Engineering  2013.8 

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    Venue:Korea  

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  • Physical and Statistical Model of Transmembrane Pressure Jump and Visualization of the Model. International conference

    K. Funatsu

    The 6th International Conference on Process Systems Engineering (PSE ASIA).  2013.6 

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    Venue:Kuala Lumpur, Malaysia  

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  • Physical and Statistical Model of Transmembrane Pressure Jump and Visualization of the Model International conference

    H. Kaneko, K. Funatsu

    The 6th International Conference on Process Systems Engineering  2013.6 

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    Venue:Malaysia  

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  • Virtual sensors with chemoinformatic techniques Invited International conference

    H. Kaneko

    Asian International Symposium ?Theoretical Chemistry, Chemoinformatics, Computational Chemistry? in the 93th CSJ Annual Meeting  2013.5 

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    Venue:Ritsumeikan University Biwako-Kusatsu Campus, Japan  

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  • 適応型非線型回帰分析手法の開発およびソフトセンサーへの応用

    船津 公人

    日本コンピュータ化学会2013年春季年会  2013.5 

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    Venue:東京工業大学  

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  • 化学空間の可視化を利用した化学構造ジェネレータの開発

    三島 和晃, 船津 公人

    日本コンピュータ化学会2013年春季年会  2013.5 

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    Venue:東京工業大学  

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  • Virtual sensors with chemoinformatic techniques Invited

    Asian International Symposium-Theoretical Chemistry,Chemoinformatics, Computational Chemistry-in the 93th CSJ Annual Meeting.  2013.3 

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    Venue:Ritsumeikan University Biwako-Kusatsu Campus, Japan  

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  • 目的変数の測定回数削減およびソフトセンサーモデルの劣化低減への試み

    加藤 正朗, 船津 公人

    化学工学会 第78年会  2013.3 

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    Venue:大阪大学  

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  • Online support vector regressionを応用したソフトセンサーモデルの劣化低減手法の開発

    船津 公人

    第13回計測自動制御学会制御部門大会  2013.3 

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    Venue:アクロス福岡  

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  • ソフトセンサーを用いた新規プロセス制御手法の開発

    木村 一平, 船津 公人

    第13回計測自動制御学会制御部門大会  2013.3 

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    Venue:アクロス福岡  

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  • データ密度を考慮したソフトセンサーモデルの予測誤差の推定

    船津 公人

    化学工学会 第78年会  2013.3 

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    Venue:大阪大学  

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  • 実験回数の軽減を目的とした材料設計手法の開発および多次元空間における新規候補探索

    岸尾 拓也, 船津 公人

    化学工学会 第78年会  2013.3 

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    Venue:大阪大学  

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  • Development of a New Regression Method Considering Predictability and Molecular Design of Amine Compounds for CO2 Separation and Recovery

    Mishima Kazuaki, Kaneko Hiromasa, Funatsu Kimito

    Journal of Computer Aided Chemistry  2013 

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    In carbon dioxide capture and storage (CCS), the chemical absorption method with amine compounds has been widely investigated as a method for capturing CO2. In this way, amine compounds with high performances of CO2 absorption and desorption are required for cost reduction of CO2 separation and recovery. One of the approaches to find amine compounds with high performances is molecular design with quantitative structure-property relationships (QSPR) models and structure generators. In this study, ensemble learning and genetic algorithm-based partial least squares (GAPLS), which is a variable selection method, were combined to construct predictive regression models. This method is named ensemble GAPLS (EGAPLS). In ensemble learning, prediction results from multi-models are integrated to give a better result than those of each single model. Moreover, considering the variance of the predicted values, it is possible to evaluate the reliability of the final prediction result. We constructed the QSPR models and evaluated the predictive accuracy of these models by cross-model validation (CMV) with the data of absorption rate and desorption capacity with tertiary amine compounds. The modeling results showed that the EGAPLS models had the highest predictive accuracy. The constructed EGAPLS models were applied to molecular design, and accordingly, promising chemical structures were obtained for CO2 separation and recovery.

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  • Construction of two-dimensional quantitative structure retention relationship models and structure elucidation based on inverse analysis with QSRR models

    Kamma Koji, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2013 

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    Gas chromatography (GC) and two-dimensional GC (GC-GC) are widely used for separation, structure elucidation and quantitative analysis. In GC and GC-GC, the chemical structure is elucidated by comparing the measured retention time of each compound with the database. But, structure elucidation is infeasible if the retention time is not available from the database. Thus, quantitative structure retention relationship (QSRR) is proposed to predict the retention time from the structure. Some researchers constructed the QSRR models specialized for the limited types of compounds. In this study, we aim to construct the QSRR models that can predict the retention time of various compounds in GC-GC with high accuracy. In addition, we propose a structure elucidation method based on the inverse analysis with the models. First, the objective value of the retention time is set. Then, structure elucidation is accomplished by comparing the objective value with the predicted retention time of new structures. The prediction errors can be a problem in comparison between the predicted and objective values. We deal with this problem by setting an acceptable error for each compound based on the reliability of the predicted value. The analysis with the measurement data proved the effectiveness of the proposed method.

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  • Development of a new criterion for evaluating the predictive ability of nonlinear regression models without cross-validation

    Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2013 

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    Many kinds of nonlinear regression methods have been developed to construct predictive models even in the existence of the nonlinear relationship between objective variables and explanatory variables. However, even when very accurate regression models are constructed, the constructed models exhibit poor predictive performance for new data. Therefore, these regression models must be validated to quantify their predictive ability and allow the appropriate model and hyperparameters to be selected. We propose predictive performance criteria for nonlinear regression models without cross-validation. The proposed criteria are the determination coefficient and the root-mean-square error for the midpoints between k-nearest-neighbor data points. These criteria can be used to evaluate predictive ability after the regression models are updated, whereas cross-validation cannot be performed in such a situation. The proposed method is effective and helpful in handling big data when cross-validation cannot be applied. We analyze numerical simulation data, aqueous solubility data and toxicity data, and confirm that the proposed criteria enable the predictive ability of the nonlinear regression models to be appropriately quantified.

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  • Development of a Structure Generator to Explore a Target Area on Chemical Spaces

    Mishima Kazuaki, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2013 

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    On the first stage of development of new drugs, various lead compounds with high activity are required. To design such compounds, we focus on chemical spaces defined by structural descriptors. New compounds close to areas around which highly active compounds exist will show the same degree of activity. Therefore we have been developing a new system of structure generation for searching a target area in chemical spaces. First, highly active compounds are manually selected as initial seeds. Then, those seeds are entered to our generator and structures slightly different from the structures of the seeds are generated and pooled. Next seeds are selected from the new structure pool with the scores based on distance from target on the map. In this study, we used GVK data of ligand-binding affinity to verify the advantage of our generator. Visualization of the chemical space and structure generation were performed, and then the outputs were compared with test data. As a result, our generat or could produce many structures similar to the test data, which exist near the target area. This result shows that exploration of the target area on the chemical space was performed.

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  • Design of transfection reagents in RNAi therapeutics by chemoinformatics approach

    Sakai Yu, Kaneko Hiromasa, Ohta Seiichi, Itoh Taichi, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2013 

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    RNAi is a natural biological process involving gene silencing or regulation with siRNA and expected to be applied in the therapeutics of gene disorders. The delivery of siRNA into the cell is demonstrated using cationic lipid. The lipid is called 'Transfection reagents'and the development of new reagents has recently advanced. Some relation exists between the chemical structures of the reagents and their properties, but it is not clear yet quantitatively and the development of new reagents depends on the previous studies. In this study we collected the data of the transfection reagents from literature and constructed statistical models of QSAR models of transfection reagents in order to predict the chemical structures of high efficient reagents in future. The descriptors calculated for chemical structures and experimental parameters were used as explanatory variables, and their rate of gene expression was used as an objective variable. In this study we calculated descriptors for the structures of amines and carbon chains separately and constructed the regression models predicting the rate of gene expression. The value of q2 improved in PLS and SVR models compared with the value of q<sup>2</sup> calculated with the descriptors for the structures of transfection reagents. The descriptors derived from carbon chains had high correlations with the rate of gene expression compared with the correlations of the descriptors derived from amines.

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  • The development of a model predicting long-term fouling for membrane bioreactors

    Oishi Hayato, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2013 

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    A membrane bioreactor (MBR) is a wastewater treatment process which uses activated sludge to remove organic substances from wastewater. The membrane is used to separate activated sludge from treated water in MBRs. The concentration of mixed liquor suspended solids (MLSS) is important for long-term fouling prediction and process control in MBRs, but it requires much time and cost to measure the concentration of MLSS. In this study, we developed a regression models between the concentration of MLSS and other variables such as operating conditions and water quality variables. To develop a widely-used and accurate model, we analyzed three data sets measured in different MBR plants. The model constructed with three data sets could achieve high predictive performance. From the results of the variable selection using a genetic algorithm-based partial least squares (GAPLS) method and the comparison of GAPLS models with different explanatory variables, it was suggested that the viscosity in the membrane tank and treated water quality variables are important for prediction of the concentration of MLSS.

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  • Development of inverse soft sensor-based feed forward control.

    Kimura Ippei, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2013 

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    Model predictive control is widely used as a process control method for a complicated multivariable process. However, optimization of control parameters is complicated and a data set for system identification cannot always be obtained in a real process. In order to solve these problems and perform more effective control, we propose a new process control method using soft sensor models. We refer to this method as inverse soft sensor-based feed forward (ISFF) control. Soft sensor models are constructed between a controlled variable (y) as an objective variable, and manipulated variables (U) and other process variables (X) as explanatory variables. The optimal control strategy of U which optimizes the objective function including y is determined with inverse analysis on the soft sensor models while considering X variables. The proposed method was applied to the change of a set point of a simulated CSTR system and the optimization of y of a simulated fed-batch fermentation process, and the validity of ISFF was confirmed.

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  • Development of a generic experimental design based on a probability that satisfy target

    Nakao Atsuyuki, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2013 

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    In the previous study, the experimental design to satisfy the desired property in developing products based on probability that satisfy target was developed, but previous studies is only suitable when do one experiment at the same time. In this study, we developed the experimental design which can use when do some experiments at the same time based on previous studies. For avoiding to select candidates which are similar to experimental parameters which do experiment at same time, we select candidates at regular intervals. Changing the interval of candidates, the proposed method is applied to simulation dates and we compared the interval of candidates and the number of experiments which need satisfy target. The interval of candidates and the number of experiment have no clear relation, but the relationships the interval of candidates ,the speed of improving predictability and the predictability which need satisfy target have same tendencies in the all data.

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  • 膜分離活性汚泥法における長期ファウリング予測モデルの開発 Invited

    金子 弘昌

    第29回ニューメンブレンテクノロジーシンポジウム2012  2012.11 

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    Venue:三田NNホール  

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  • Discussion on Time Difference Models for Application of Soft Sensors.

    K. Funatsu

    19th Regional Symposium on Chemical Engineering (RSCE2012).  2012.11 

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    Venue:Bali, Indonesia  

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  • Discussion on Time Difference Models for Application of Soft Sensors International conference

    H. Kaneko, K. Funatsu

    19th Regional Symposium on Chemical Engineering  2012.11 

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    Venue:Bali, Indonesia  

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  • モデルの信頼性および適用範囲を考慮したケモメトリックス解析 Invited

    金子 弘昌

    2012年度 CACフォーラム一泊研修会  2012.10 

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    Venue:宮島グランドホテル  

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  • 産業プロセスにおけるケモメトリックス技術としてのソフトセンサー Invited

    金子 弘昌

    2011年度 第2回CACフォーラムセミナー  2012.10 

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    Venue:日本化学会化学会館  

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  • 頑健かつ高精度なモデルの構築を目指した新規近赤外スペクトル解析手法の提案,

    菅間 幸司, 船津 公人

    第35回情報化学討論会,  2012.10 

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    Venue:広島大学  

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  • Soft Sensor Models Based On a Process Variable and Dynamics Selection Method and Support Vector Regression International conference

    H. Kaneko, K. Funatsu

    AIChE 2012 Annual Meeting  2012.10 

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    Venue:U.S.A.  

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  • Estimation of Predictive Accuracy of Soft Sensor Models Based on One-Class Support Vector Machine International conference

    H. Kaneko, K. Funatsu

    The 11th International Symposium on Process Systems Engineering  2012.10 

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    Venue:Singapore  

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  • One-class support vector machineを用いたソフトセンサーモデルの予測誤差の推定,

    船津 公人

    日本コンピュータ化学会2012年秋季年会  2012.10 

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    Venue:山形大学  

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  • Strategic Search for Experimental Conditions for Efficient Product Design International conference

    T. Kishio, H. Kaneko, K. Funatsu

    AIChE 2012 Annual Meeting  2012.10 

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    Venue:U.S.A.  

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  • Strategic Search for Experimental Conditions for Efficient Product Design. International conference

    T. Kishio, K. Funatsu

    AIChE 2012 Annual Meeting  2012.10 

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    Venue:Pittsburgh, U.S.A  

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  • Soft Sensor Models Based On a Process Variable and Dynamics Selection Method and Support Vector Regression. International conference

    K. Funatsu

    AIChE 2012 Annual Meeting  2012.10 

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    Venue:Pittsburgh, U.S.A.  

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  • 目的変数間の関係を活用したモデル劣化低減手法の開発

    加藤 正朗, 船津 公人

    化学工学会第44回秋季大会  2012.9 

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    Venue:東北大学  

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  • Prediction Model of Transmembrane Pressure Jump for Membrane Bioreactor Using Physical and Statistical Approaches International conference

    H. Kaneko, K. Funatsu

    Euromembrane 2012  2012.9 

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    Venue:London, U.K.  

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  • ソフトセンサーモデルの劣化の分類と各適応的モデルに関する考察

    船津 公人

    化学工学会第44回秋季大会  2012.9 

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    Venue:東北大学  

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  • Prediction Model of Transmembrane Pressure Jump for Membrane Bioreactor Using Physical and Statistical Approaches. International conference

    K. Funatsu

    Euromembrane 2012  2012.9 

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    Venue:London, U.K.  

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  • 分離プロセスにおいて圧力の変化を考慮して共沸予測を行う統計モデルの構築

    金 泰亨, 山城 直也, 船津 公人

    化学工学会第44回秋季大会  2012.9 

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    Venue:東北大学  

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  • データ特性を踏まえた戦略的な実験水準決定手法の開発

    岸尾 拓弥, 船津 公人

    化学工学会第44回秋季大会  2012.9 

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    Venue:東北大学  

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  • Estimation of Predictive Accuracy of Soft Sensor Models Based on One-Class Support Vector Machine

    K. Funatsu

    The 11th International Symposium on Process Systems Engineering.  2012.7 

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    Venue:National University of Singapore, Singapore  

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  • Soft Sensors Supporting Efficient Plant Operations with Chemoinformatic Techniques Invited International conference

    H. Kaneko

    The 4th French-Japanese Workshop on Computational Methods in Chemistry  2012.5 

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    Venue:Kyukamura hotel, Japan  

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  • 非線型変数領域選択手法の開発およびソフトセンサーへの応用

    船津 公人

    日本コンピュータ化学会2012年春季年会  2012.5 

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    Venue:東京工業大学  

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  • A Statistical Approach to Prediction of Transmembrane Pressure in Membrane Bioreactors. International conference

    K. Funatsu

    AIChE 2012 Spring Meeting  2012.4 

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    Venue:Houston, Texas, U.S.A  

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  • Soft Sensors Supporting Efficient Plant Operations with Chemoinformatic Techniques Invited International conference

    The 4th French-Japanese Workshop on Computational Methods in Chemistry  2012.3 

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    Venue:Kyukamura hotel, Japan  

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  • A Statistical Approach to Prediction of Transmembrane Pressure in Membrane Bioreactors International conference

    H. Kaneko, K. Funatsu

    AIChE 2012 Spring Meeting  2012.3 

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    Venue:Houston, Texas, U.S.A.  

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  • 産業プロセスにおけるケモメトリックス技術としてのソフトセンサー Invited

    2011年度 第2回CACフォーラムセミナー  2012.3 

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    Venue:日本化学会化学会館  

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  • 効率的な実験計画のための戦略的なパラメータ探索手法の開発

    岸尾 拓弥, 船津 公人

    化学工学会 第77年会  2012.3 

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    Venue:工学院大学  

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  • Strategic Parameter Search Method for Efficient Experimental Design. International conference

    T. Kishio, K. Funatsu

    The 4th French-Japan Workshop on Computational Methods in Chemistry 2012  2012.3 

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    Venue:Kyukamura hotel, Japan  

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  • Strategic Parameter Search Method for Efficient Experimental Design International conference

    T. Kishio, H. Kaneko, K. Funatsu

    The 4th French-Japan Workshop on Computational Methods in Chemistry 2012  2012.3 

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    Venue:Japan  

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  • ケモインフォマティックス手法を用いた膜分離活性汚泥法における膜差圧予測モデルの構築

    船津 公人

    化学工学会 第77年会  2012.3 

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    Venue:工学院大学  

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  • Support vector regressionを応用した変数領域選択手法の開発

    船津 公人

    第12回計測自動制御学会制御部門大会  2012.3 

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    Venue:奈良県文化会館  

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  • 分散型膜分離活性汚泥法のための長期膜差圧予測モデルの構築

    成 敬模, 船津 公人

    化学工学会 第77年会  2012.3 

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    Venue:工学院大学  

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  • プラントの運転状況を考慮した実用的ソフトセンサーメンテナンス法の開発

    岡田 剛嗣, 船津 公人

    化学工学会 第77年会  2012.3 

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    Venue:工学院大学  

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  • Development of New Soft Sensor Methods for Selecting Process Variables with Consideration of Process Dynamics International conference

    H. Kaneko, K. Funatsu

    The 13th Asia Pacific Confederation of Chemical Engineering Congress  2012.2 

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    Venue:Suntec City, Singapore  

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  • Development of Statistical Models for Predicting Presence of Azeotropy based on Molecular Charge

    Kim Taehyung, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2012 

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    It is important to know whether or not mixtures form an azeotrope for the design of distillation processes since azeotropes cannot be separated by ordinary distillation. Thus, there have been many methods to predict the presence of azeotropy, e.g. equation of state, quantitative structure-property relationships, non-random two-liquid model, UNIFAC, UNIQUAC, thermodynamic equations and so on. In this study, to construct a model predicting the azeotropic formation and improve the prediction performance, we propose a novel method based on σ-profiles. The σ-profiles are major information of a COSMO-RS theory and the distribution of molecular surface areas which have charge density σ. Since the σ-profiles are characteristic of not only an objective molecule but also a relationship between the molecule and a solvent, they will be able to be used as descriptors to express interaction between solvents. The model predicting the presence of azeotropic formation is constructed with support vector machine (SVM).Through the analysis of data from the Dortmund Data Bank, it was confirmed that the proposed model has higher prediction performance compared to previous ones where structure descriptors are used as explanatory variables. Moreover, the performance of the SVM model was improved by adding structure descriptors to σ-profile descriptors.

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  • Development of a Structure Generator to Efficiently Generate New Structure in Target Areas on Chemical Spaces

    Mishima Kazuaki, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2012 

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    Computational drug design is a growing field in recent drug discovery research. For example, lead compounds, which will have high activity, can be computationally discovered in efficient ways on the first stage of development of new drugs. In this case, various lead compounds are needed since some of them may not satisfy the conditions as medical supplies such as ADMET. Thus we focus on chemical spaces around which highly active compounds exist. New compounds close to those spaces will show the same degree of activity as the highly active compounds. Therefore we develop a new system of structure generation for searching a target area in chemical spaces, which are defined by structural descriptors. First, highly active compounds are manually selected as initial seeds. Then, those seeds are entered to our generator and structures slightly different from the seeds are generated and pooled. Next seeds are selected from the new structure pool with the scores calculated from two indices for each pooled structure. One index is distance from the center of the compounds surrounding a target area, and the other is predicted activity. We used GVK data of ligand binding affinity and showed that the proposed system performed appropriate structure generation.

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  • Consideration of Soft Sensor Methods Based on Time Difference and Discussion on Intervals of Time Difference

    Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2012 

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    In chemical plants, soft sensors are widely used to estimate process variables that are difficult to measure online. The predictive accuracy of soft sensors decreases over time because of changes in the state of chemical plants, and soft sensor models based on time difference (TD) have been constructed to reduce the effects of deterioration with time, such as drift. However, many details of models based on TD remain to be clarified. In this study, TD models are discussed in terms of noise in data, auto-correlation in process variables, and degree of model accuracy, among others. We theoretically clarify and formulate the differences of predictive accuracy between normal models and TD models. The relationships and the formulas of TD were verified by analyzing simulation data. Furthermore, we analyzed data obtained by dynamic simulation of an existing full-scale depropanizer distillation column and examined various TD intervals and the predictive ability of TD models. It was confirmed that the predictive accuracy of TD models increased when TD intervals were optimized.

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  • Proposal of a novel near-infrared spectral analysis method for constructing robust and high-precision models

    Kamma Koji, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2012 

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    Non-destructive testing of food quality with Near-Infrared Spectroscopy (NIR) is becoming common. The prediction models are constructed between NIR spectra and quality parameters. Many investigations have been done for the construction of high predictive models. Although some models indeed have suitably predictive accuracy, those models work well in only limited data domains and the accuracy decreases with time. Hence the models should be reconstructed with new data by wasting samples of objective foods and measuring the quality. To perform both the reduction of the loss of the food and the high performance of the models, overlapped peaks of NIR spectra should be considered because the overlapped peaks make relationships between NIR spectra and quality parameters unclear. Derivation of spectra is generally used to solve this problem. An adequate order of derivative changes depending on how peaks are overlapping, but the dependence of an adequate derivative order on the number of training samples remains to be clarified. Therefore we propose a method using some kinds of derivative order of spectra according to the number of samples for the construction of regression models. The effectiveness of the proposed method was confirmed thorough the analyses of simulation data and real data.

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  • Development of a strategic parameter search method considering data characteristics for efficient product design

    Kishio Takuya, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2012 

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    In experimental design for functional molecules, materials or products, complicated relationships exist between various experimental parameters and objective physical and chemical properties. Regression analysis with experimental data is a useful way for understanding those relationships, and a constructed regression model can be used to search for functional products effectively. However, although those products can be found in domains out of existing data, the predictive ability of the model tends to be low in regions where data density is low, and new candidates whose predicted values of a property are unreliable will not achieve desired values of the property. Therefore to search for new candidates in appropriate extrapolation domains, we consider the probability that a new candidate will have intended values of a property and the reliability of a predicted value of the property for the candidate. The probability is calculated from a predicted value and its estimated prediction error, and the reliability is based on data density. The proposed method is applied to simulation data and aqueous solubility data, and the efficiency of the method could be confirmed. In addition, we could automatically select an appropriate regression method in each step of the search according to features of existing data.

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  • ソフトセンサーモデルの予測性能および適用範囲の検証 Invited

    金子 弘昌

    測自動制御学会 2011年度産業応用部門大会 産業システムシンポジウム『統計的アプローチにより現場を見る!診る!看る!』  2011.11 

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    Venue:東京工業大学大岡山キャンパス  

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  • Classification of the Degradation of Soft Sensor Models and Discussion on Adaptive Models International conference

    H. Kaneko, K. Funatsu

    TIChE International Conference 2011  2011.11 

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    Venue:Hatyai, Thailand  

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  • Development of a Model Selection Method Based on Reliability of a Soft Sensor Model International conference

    T. Okada, H. Kaneko, K. Funatsu

    TIChE International Conference 2011  2011.11 

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    Venue:Hatyai, Thailand  

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  • Novel Approach to Predict the Azeotropy at any Pressure Using Classification by Subgroups International conference

    T. H. Kim, H. Kaneko, N. Yamashiro, K. Funatsu

    TIChE International Conference 2011  2011.11 

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    Venue:Hatyai, Thailand  

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  • Improvement and Estimation of Prediction Accuracy of Soft Sensor Models Based on Time Difference International conference

    H. Kaneko, K. Funatsu

    The Twenty-fourth International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems  2011.6 

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    Venue:Syracuse, New York, U.S.A.  

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  • A chemoinformatics approach to predicting rate of gene silencing on transfection reagent of RNAi therapeutics

    Sakai Yu, Kaneko Hiromasa, Ito Taichi, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2011 

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    RNAi is a specific gene silencing mechanism triggered by siRNA. The application of RNAi for gene diseases requires the development of safe and effective delivery systems. For this reason, development of transfection reagents has been expanding. A transfection reagent is a lipid which introduces siRNA into a cell efficiently. We need to predict adequate structures for development of efficient reagents, because the efficiency of introducing siRNA into a cell and the rate of gene silencing differ depending on the structures of reagents. However, the structure-activity relationship has not been clear yet. We therefore proposed a chemoinformatics approach predicting the rate of gene silencing on transfection reagents from the structure by using a regression model constructed with database of transfection reagents. In this study, descriptors as explanatory variables in construction of the model were calculated with the softwares, DRAGON and CODESSA, and an objective variable was their Luciferase expression in the HeLa cell. By using a genetic algorithm based-partial least squares method, which is one of the variable selection methods, a high predictive model could be constructed with a small number of explanatory variables.

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  • Development of Soft Sensor Methods Based on Wavelength Region Selection Methods

    Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2011 

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    Soft sensors have been widely used in industrial plants to estimate process variables that are difficult to measure online. Soft sensor models predicting an objective variable should be constructed with only important explanatory variables in terms of predictive ability, better interpretation of models and lower measurement costs. Besides, some process variables can affect an objective variable with time-delays. In some studies, soft sensor models are constructed using process dynamics and in others, the selection of process variables is used to increase the predictive accuracy. No one, however, has yet realized the optimization of both considerations in process dynamics and process variable selection. We therefore have proposed the methods for selecting important process variables and optimal time-delays of each variable simultaneously, by modifying the wavelength selection methods in spectrum analysis such as stacked partial least squares (PLS), searching combination moving window PLS, and genetic algorithm-based wavelength selection. The proposed methods can select time-regions of process variables as a unit by using process data that includes process variables that are delayed for a duration ranging from 0 through some time. The case study with real industrial data confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed methods.

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  • Development of an adaptive soft sensor method considering prediction confidence of models

    Okada Takeshi, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2011 

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    Soft sensors are widely used to realize highly efficient operation in chemical process because every important variable such as product quality is not measured online. By using soft sensors, such a difficult-to-measure variable y can be estimated with other process variables which are measured online. For estimating y without degradation of a soft sensor model, a time difference (TD) model was developed previously. Though a TD model has high predictive ability, it does not function well when a process is operated under conditions that have never been observed. In order to cope with this problem, a soft sensor model can be updated with newest data. But, updating a model needs time and effort for plant operators. We therefore developed an online monitoring system to judge whether a TD model can predict y accurately or an updating model should be used for both reducing maintenance cost and improving predictive accuracy of soft sensors. The monitoring system is based on a support vector machine or standard deviation of y-values estimated from various intervals of time difference. We confirmed that the proposed system has functioned successfully in a distillation column with real industrial data.

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  • Development of Statistical Models for Predicting Presence of Azeotropy at any Pressure

    Kim Taehyung, Kaneko Hiromasa, Yamashiro Naoya, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2011 

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    Distillation is one of the dominating separation processes, but there are some problems that inseparable mixtures are formed in some cases. This phenomenon is called as azeotropy. It is essential to understand azeotropy in any distillation processes since azeotropes, i.e. inseparable mixtures, cannot be separated by ordinary distillation. In this study, to construct a model which predicts the azeotropic formation at any pressure, a novel approach is presented with support vector machine (SVM). The SVM method is used to classify data in the two classes, that is, azeotropes and nonazeotropes. 13 variables including pressure data were used as explanatory variables. From the result of comparing the SVM models which were constructed with data measured at 1atm and data measured at any pressure, the 1atm model shows a higher prediction performance to the data measured at 1atm than the any pressure model. Thus, for improving the performance of the any pressure model, we focused on intermolecular forces of solvents. The SVM models were constructed with only data of the solvents having same subgroups. The accuracy of the model increased and it is expected that this proposed method will be used to predict azeotropic formation at any pressure with high accuracy.

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  • Construction of QSPR Models on Properties of Amine Compounds Based on Semi-Empirical Quantum Chemical Calculation and Suggestion of New Amine Compounds

    Mishima Kazuaki, Kaneko Hiromasa, Yamashiro Naoya, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2011 

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    Carbon dioxide capture and storage (CCS) is widely performed as one of measures against global warming. In the present CCS, however, cost of carbon dioxide capture is high, and therefore it is strongly required to develop more effective absorbing method. Chemical absorption method with amine compounds which have hydroxyl grope is one of the potent methods for carbon dioxide capture. For decreasing the cost of carbon dioxide capture, novel amine compounds with high capability of absorption and diffusion is required. Hence, the objective of this study is to explore such amine compounds. To construct regression models for predicting the absorption rate and the amount of diffusion of amine compounds, we evaluated the amine compounds whose properties are measured, applying semi-empirical molecular orbital method. By using this method, steric effect and electric nature can be considered. After constructing regression models by the GAPLS (genetic algorithm based-partial least squares) method in which choice of predictive variables is carried out, we evaluated amine compounds which are virtually generated with a structure generator. By using their properties estimated with the GAPLS models, we explored novel amine compounds with desirable properties.

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  • Construction of transmembrane pressure estimation model for membrane bioreactor

    Sung Kyung-mo, Kaneko Hiromasa, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2011 

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    A membrane bioreactor (MBR) is the equipment to filtrate sewage or factory disposal water by MF/UF membrane. Activated sludge is used to remove organic substances metabolically and filtrated to membrane by transmembrane pressure (TMP). Though MBR is able to remove activated sludge, other particles of raw water, or both of them effectively, increase of TMP as a result of fouling growth brought by deposition of particles is one of the biggest problems for improving its performance. Membrane needs to be washed when TMP reaches about 25-30 kPa. The focus of this study is to estimate TMP with statistical prediction models and also know when the membrane wash-up will become necessary for the realization of distributed MBR and its unattended operation. In this study, two statistical models were constructed between explanatory variables related to fouling and an objective variable, i.e., membrane resistance (R) or deposition rate of foulant (DR). It is able to predict TMP because R or DR could be converted into TMP. As results of TMP prediction with real industrial data, usage of DR as an objective variable improved the accuracy of TMP prediction.

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  • Development of the time difference model capturing nonlinear relationships between variables and the application to soft sensors

    Hiromasa Kaneko, Kimito Funatsu

    Symposium on Chemical Information and Computer Sciences  2010 

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    In industrial plants, soft sensors are widely used to estimate process variables that are difficult to measure online. Though regression models are reconstructed with new data in order to maintain their high accuracy, some problems remain in practice. Hence, it is attempted to construct soft sensor models based upon the time difference of an objective variable and that of explanatory variables for reducing the effects of deterioration with age such as the drift and gradual changes in the state of plants. However, the time difference model cannot account for the nonlinearity in process variables. Therefore, we have proposed to construct time difference models after modeling nonlinear relationship between and among process variables. Variables obtained by physical models or those calculated by statistical nonlinear regression methods are used to consider the nonlinearity, and then, a time difference model is constructed including these variables. We applied the proposed methods to the actual industrial data obtained during an industrial polymer process. The proposed models achieved high predictive accuracy and eased the bias of the prediction errors for both density and melt flow rate. We confirmed the usefulness of the proposed methods without reconstruction of soft sensor models.

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  • Relationships between applicability domain and accuracy of prediction of regression models

    Kaneko Masahiro, Arakawa Masamoto, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2009 

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    Multivariate regression methods such as a partial least squares (PLS) method and support vector regression (SVR) are powerful tools for handling several problems in chemoinformatics. Attempts to construct models having high predictive accuracy have been made by using those methods, and then significant results have been produced. On the other hand, because predictability of constructed models differ among query samples predicted with these models, it is important to estimate prediction errors of these samples. Therefore, in this study, we tried to quantify relationships of applicability domains (AD) of regression models and prediction errors. The larger distances to models (DM) are, the lower the accuracy of prediction would be estimated. We used Euclidean distances to an average of training data and ones to the nearest sample in training data as DM, and PLS and SVR as methods constructing regression model. The proposed method were applied to quantitative structural-property relationships (QSPR) and soft sensor analyses. Estimate accuracy of prediction errors increased in QSPR analysis. In soft sensor analysis, higher fault detection ability was achieved than a traditional method.

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  • Development of a statistical process control method using independent component analysis and support vector machine

    kaneko hiromasa, arakawa masamoto, funatsu kimito

    Symposium on Chemical Information and Computer Sciences  2008 

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    Soft sensors are widely used to estimate a process variable which is difficult to measure online. One of the crucial difficulties of soft sensors is that predictive accuracy drops due to changes of state of chemical plants, catalyst performance loss, sensor and process drifting, and so on. In order to cope with this problem, a regression model can be updated with a new sample. However, if the model is updated with an abnormal sample, the predictive ability can deteriorate. We have applied the independent component analysis (ICA) method and support vector machine (SVM) to the soft sensor in order to increase fault detection and diagnosis ability. Then, we have tried to increase the predictive accuracy. By using the ICA and SVM based fault detection and diagnosis model, the objective variable can be predicted, updating the regression model appropriately. We analyzed real industrial data as the application of the proposed method. RMSE of a traditional soft sensor model are 0.511, and that of the proposed soft sensor model are 0.200. The proposed method achieved higher predictive and fault detection accuracy than the traditional one.

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  • Development of the new soft sensor method and the application to process control

    Hiromasa Kaneko, Masamoto Arakawa, Kimito Funatsu

    Symposium on Chemical Information and Computer Sciences  2007 

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    Soft sensors are widely used in chemical plants to estimate a process variable which is difficult to measure online. An inferential model is constructed between variables which are easy to measure online and one which is difficult to measure online, and an objective variable is estimated by the model. However, soft sensors have some practical difficulties. One of the crucial difficulties is that predictive accuracy drops due to changes of state of chemical plants, sensor and process drifting and so on. If a problem of degradation of soft sensors is not solved, it is difficult to identify reasons of abnormal situations. There is no effective method to solve these difficulties under the circumstances. In this study, we have developed the new soft sensor method that combines independent component analysis (ICA) and PLS. ICA is a method that is used in many fields such as signal processing. We can comprehend the state of plant by an ICA model and estimate an objective variable by a PLS model, updating PLS model appropriately. We showed the superiority of this method over a traditional one with real industrial data.

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  • Development of the new regression analysis method using independent component analysis and genetic algorithm

    Kaneko Hiromasa, Arakawa Masamoto, Funatsu Kimito

    Symposium on Chemical Information and Computer Sciences  2006 

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    In this research, independent component analysis (ICA) and regression analysis are combined to extract significant components. ICA is a method that extracts mutually independent components from explanatory variables. We propose a new method that selects combination of independent components by using genetic algorithm (GA). It can construct a PLS model that has high predictive accuracy. This method is named ICA-GAPLS. In order to verify the superiority of ICA-GAPLS, this method is applied to QSPR analysis of aqueous solubility. The result of comparison with PLS and other regression methods is shown. For example, R2 and Q2 value in the first component of the PLS model are 0.427 and 0.421, respectively. These values in the ICA-GAPLS model are 0.945 and 0.857. ICA-GAPLS model achieves high predictive accuracy with less number of components than PLS model. ICA-GAPLS shows better result regarding the maximum of R2 and Q2 value than other methods. It is thought that ICA-GAPLS can extract effective components from explanatory variables and can construct the regression model having high predictive accuracy.

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Awards

  • Symposium Award, International Congress on Pure & Applied Chemistry

    2023.9  

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  • Best Lecture Award

    2018.4   International Congress on Pure & Applied Chemistry   Measure of Regression Model Accuracy for Quantitative Structure-Activity(Property) Relationship Considering Applicability Domains

    金子 弘昌

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  • 論文審査貢献賞

    2017.4   化学工学会  

    金子 弘昌

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  • JCAC論文賞

    2016.9   日本化学会 情報化学部会   A Mini-review on Chemoinformatics Approaches for Drug Discovery

    川下 理日人, 山崎 広之, 宮尾 知幸, 河合 健太, 榮 慶丈, 石川 岳志, 森 健一, 中村 真也, 金子弘昌

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  • 化学工学会第48回秋季大会 SIS部会 部会技術賞

    2016.9   化学工学会 SIS部会   適応型ソフトセンサーおよび推定値の平滑化を実現するソフトセンサーツールの開発

    金子 弘昌, 大寳 茂樹, 松本 卓也, 船津 公人

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  • JCAC論文賞

    2016.9   日本化学会 情報化学部会   A Mini-review on Chemoinformatics Approaches for Drug Discovery

    N. Kawashita, H. Yamasaki, T. Miyao, K. Kawai, Y. Sakae, T. Ishikawa, K. Mori, S. Nakamura, H. Kaneko

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    Award type:Honored in official journal of a scientific society, scientific journal 

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  • 化学工学会 研究奨励賞【内藤雅喜記念賞】

    2015.3   化学工学会   化学プラントにおける制御性能向上のための推定制御手法に関する研究

    金子 弘昌

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  • JCAC論文賞

    2013.11   日本化学会 情報化学部会   時間差分に基づくソフトセンサー手法に関する考察および時間差分間隔の検討

    金子 弘昌, 船津 公人

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  • Computers & Chemical Engineering, Most Cited Articles, 2010-2012

    2013.10   Elsevier   Novel soft sensor method for detecting completion of transition in industrial polymer processes

    H. Kaneko, M. Arakawa, K. Funatsu

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  • 化学工学会第78年会 SIS部会 研究奨励賞

    2013.5   化学工学会 SIS部会   データ密度を考慮したソフトセンサーモデルの予測誤差の推定

    金子 弘昌

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  • 計測自動制御学会産業応用部門・奨励賞

    2012.11   計測自動制御学会   ソフトセンサーモデルの予測性能および適用範囲の検証

    金子 弘昌

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  • 日本コンピュータ化学会吉田賞(論文賞)

    2012.5   日本コンピュータ化学会   Genetic Algorithm-based WaveLength SelectionとSupport Vector Regressionを組み合わせた変数領域選択手法の開発

    金子 弘昌

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  • 東京大学工学系研究科長賞

    2012.3   東京大学  

    金子 弘昌

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  • Best Presentation(優秀講演賞)

    2009.9   東京コンファレンス   効率的なプラント管理へ向けた高精度ソフトセンサー手法の開発

    金子 弘昌

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  • 東京大学工学系研究科長賞

    2009.3   東京大学  

    金子 弘昌

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  • JCAC論文賞

    2008.11   日本化学会 情報化学部会   独立成分分析と遺伝的アルゴリズムを用いた新規回帰分析手法の開発

    金子 弘昌, 荒川 正幹, 船津 公人

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  • 第29回情報化学討論会 ポスター賞

    2006.11   日本化学会 情報化学部会   独立成分分析と遺伝的アルゴリズムを組み合わせた新規回帰分析手法の開発

    金子 弘昌

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Research Projects

  • Fabrication of laminar composite membranes for selective separation of ions

    Grant number:24K01234  2024.4 - 2028.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Grant amount:\18460000 ( Direct Cost: \14200000 、 Indirect Cost:\4260000 )

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  • 流体科学における結果から原因を直接予測する数理モデル逆解析法の開発

    Grant number:24K08152  2024.4 - 2027.3

    日本学術振興会  科学研究費助成事業  基盤研究(C)

    金子 弘昌

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    Grant amount:\4550000 ( Direct Cost: \3500000 、 Indirect Cost:\1050000 )

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  • 分子の物理・化学吸着による炭素表面での自在ナノ構造作成と機能開拓

    Grant number:20H02553  2020.4 - 2025.3

    日本学術振興会  科学研究費助成事業  基盤研究(B)

    田原 一邦, 金子 弘昌

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    Grant amount:\17940000 ( Direct Cost: \13800000 、 Indirect Cost:\4140000 )

    炭素材料表面をナノレベルで自在修飾する方法の開発は、それら材料の電子状態や物理的性質の精密制御に重要である。分子による表面修飾法には、物理吸着法と化学吸着(共有結合形成)法がある。本課題では、設計した有機分子によりこの二つの修飾法を深化させ、融合させた独自のナノレベル修飾法を確立する。具体的には、溶媒とグラファイトやグラフェンとの界面において、物理吸着による数十nmの周期を持つ階層的な自己集合単分子膜の作成指針の確立と、自己集合単分子膜が示すキラリティーの高度制御とその利用を第一の目的とする。第二に、物理吸着による自己集合単分子膜を鋳型としたグラファイトやグラフェンへの化学吸着をナノレベルで制御する独自の修飾法を発展させる。最終的には新規修飾グラフェンによる電子デバイスへの応用を目指す。
    1. 二次元分子集合体の高度構造制御と機能開拓 三角形のπ共役コアを持ち、キラルな長鎖アルキル基とヒドロキシ基の直交する官能基を持つ分子が、動的な立体配座選択により、階層的かつホモキラルな二次元分子集合体を形成することを明らかにした。
    その他にも、階層的な自己集合単分子膜形成を狙い、二等辺三角形コアを持つ分子や、七角形状を持つ分子を新たに合成してそれらの二次元分子集合体を調べた。
    2.炭素表面の共有結合形成を伴う周期的な化学修飾 新たな鋳型として、菱形のパイ共役コアに六つのアルキル基が置換した分子が作るカゴメ型の二次元分子集合体や、平行四辺形状の空孔を含む多孔性の二次元分子集合体を用いて、グラファイト表面を化学修飾した。その結果、空孔のサイズや、分子集合体の周期性が反映した、多様な周期修飾表面を作ることができた。
    その他に、アルカン誘導体が作るラメラ型分子集合体を鋳型にして化学修飾により作られた直線修飾されたグラファイト表面に対して、他の分子の自己集合へ与える影響を調べた。

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  • 分子の物理・化学吸着による炭素表面での自在ナノ構造作成と機能開拓

    Grant number:23K20271  2020.4 - 2025.3

    日本学術振興会  科学研究費助成事業  基盤研究(B)

    田原 一邦, 金子 弘昌

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    Grant amount:\17940000 ( Direct Cost: \13800000 、 Indirect Cost:\4140000 )

    炭素材料表面をナノレベルで自在修飾する方法の開発は、それら材料の電子状態や物理的性質の精密制御に重要である。分子による表面修飾法には、物理吸着法と化学吸着法がある。本課題では、設計した有機分子によりこの二つの修飾法を深化させ、融合させた独自のナノレベル修飾法を確立する。具体的には、溶媒とグラファイトやグラフェンとの界面において、物理吸着による数十nmの周期を持つ階層的な自己集合単分子膜の作成指針の確立と、自己集合単分子膜が示すキラリティーの高度制御とその利用を第一の目的とする。第二に、物理吸着による自己集合単分子膜を鋳型としたグラファイトやグラフェンへの化学吸着をナノレベルで制御する独自の修飾法を発展させる。最終的には新規修飾グラフェンによる電子デバイスへの応用を目指す。
    1. 二次元分子集合体の高度構造制御と機能開拓
    長鎖アルキル基とヒドロキシ基の直交する官能基を持つ三方型分子が動的な立体配座選択により、階層的な二次元分子集合体を形成することを以前に報告している。長鎖アルキル基にキラリティーを導入した分子や、溶媒にキラルなカルボン酸を用いることで、階層的な分子集合体のキラリティー制御を実現したことを論文としてまとめた。
    その他にも、二等辺三角形コアを持つ分子の有機溶媒とグラファイト界面における相挙動や、七角形状を持つ分子の二次元分子集合体が七角形タイルの充填に相当することを明らかにし、論文として報告した。
    2.炭素表面の共有結合形成を伴う周期的な化学修飾
    多様な周期修飾表面を用いてその分子認識能を評価した。特に、ホモキラルに周期修飾された表面を用いて、そのキラリティーの伝搬について継続して調査した。その結果、ホモキラルに周期修飾された表面が複数のアキラル分子の集合体に影響することを明らかにした。さらにその要因が構造の周期性に起因することを明らかにした。

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  • 実験と計算科学との融合による生命機能を備えたテーラード人工骨の開発

    Grant number:20H04538  2020.4 - 2024.3

    日本学術振興会  科学研究費助成事業  基盤研究(B)

    相澤 守, 金子 弘昌, 松本 守雄

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    Grant amount:\17550000 ( Direct Cost: \13500000 、 Indirect Cost:\4050000 )

    本研究では、バイオマテリアルのなかで「人工骨」などとして臨床応用されている「バイオセラミックス」をベースとし、生命現象に積極的に働きかける「生命機能マテリアル」を実験と計算科学の融合により開発する。ここでは、生体骨を直接結合する「水酸アパタイトHAp)」および生体内で吸収置換される「リン酸三カルシウム(TCP)」を対象とする。
    より具体的には、実験系研究者からの良質な実験データおよび機械学習などにより収集した情報をもとに「生命機能推定モデル」を構築する。そのモデルを逆解析することにより創り出される「設計図」をもとに生命機能を自在に制御した「革新的バイオマテリアル」を創出する。本研究で対象とする「生命機能」は、骨形成能・生体吸収性・耐感染性(抗菌性)・血管形成能の4つであり、材料単独での高いパフォーマンスを発揮しうる「テーラード型人工骨」を開発し、我が国の「健康寿命の延伸」に貢献する。
    2021年度の取り組みにより、材料特性を十分に把握した人工骨材料の骨形成率を画像解析により定量化し、良質なデータを新たに収集した。それらのデータを特徴量Xおよび目的変数Yとして、骨形成能を予測する「テーラード人工骨創製に資する生命機能予測モデル(Y=F(X))」の構築に成功した。また、2021年度の後半には、このモデルの逆解析により提示された作製条件での材料合成も手掛けている。2022年度は計算結果の予測精度と実際の実験結果(材料特性や生物学的特性)との整合性を向上させる取り組みを推進し、骨形成予測モデルの逆解析による設計図を活用したテーラード型人工骨を創製し、その機能評価を実施する。

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  • Research on inverse analysis and scientific interpretation of property prediction models

    Grant number:19K15352  2019.4 - 2023.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Early-Career Scientists

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    Grant amount:\4160000 ( Direct Cost: \3200000 、 Indirect Cost:\960000 )

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  • 適応的実験計画法による高効率な結晶育成プロセスの条件探索

    2019.4 - 2021.3

    COI若手連携研究ファンド・デジタル分野連携研究 

    金子弘昌, 林文隆

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    Grant type:Competitive

    Grant amount:\234 ( Direct Cost: \234 )

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  • 石油精製工場におけるビッグデータを活用した安全かつ効率的なプラント管理手法の確立

    2016

    一般財団法人 石油エネルギー技術センター 

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    Grant type:Competitive

    Grant amount:\3543725 ( Direct Cost: \3543725 )

    一般財団法人 石油エネルギー技術センター 「革新的石油精製技術のシーズ発掘」

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  • 産業プラントにおける異常検出および異常原因診断システムの開発

    2015 - 2016

    財団法人みずほ学術振興財団  研究助成金

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    Grant type:Competitive

    Grant amount:\2000000 ( Direct Cost: \2000000 )

    財団法人みずほ学術振興財団第58回工学研究助成金

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  • 安定的で効率的な推定制御のための適応型非線型回帰分析手法の開発

    2012 - 2014

    科学研究費補助金・若手研究(B) 

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    Grant type:Competitive

    Grant amount:\4160000 ( Direct Cost: \3380000 、 Indirect Cost:\780000 )

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  • 産業プラントの制御性能向上のための新規推定制御手法の開発

    2012 - 2013

    財団法人みずほ学術振興財団  研究助成金

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    Grant type:Competitive

    Grant amount:\2000000 ( Direct Cost: \2000000 )

    財団法人みずほ学術振興財団第55回工学研究助成金

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  • 効率的な材料設計のための戦略的材料探索手法の開発

    2012

    公益財団法人豊田理化学研究所 

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    Grant type:Competitive

    Grant amount:\700000 ( Direct Cost: \700000 )

    公益財団法人豊田理化学研究所 平成24年度「豊田理研 スカラー」

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Social Activities

  • プロセス・マテリアルズ・ケモインフォマティクスオンラインサロン (金子研オンラインサロン)

    2018.4

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    Audience: College students, Graduate students, Researchesrs, General, Company

    Type:Other

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  • 第39回ケモインフォマティクス討論会 若手の会セッション

    Role(s): Planner, Organizing member

    静岡大学浜松キャンパス佐鳴会館  2016.9

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  • 日本化学会情報化学部会 第四回ケモインフォマティクス若手の会

    Role(s): Planner, Organizing member

    静岡大学浜松キャンパス佐鳴会館  2016.9

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  • 第2,3,4回ケモインフォマティクス入門講座実行委員

    2016.5

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    Type:Other

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  • ケモメトリックス初学者のためのR言語講習会~インストールからPLS・SVMモデルの構築まで~

    Role(s): Planner, Organizing member

    第4回ケモインフォマティクス入門講座(第13回情報化学入門講座)  日本化学会 化学会館  2016.5

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  • 第38回ケモインフォマティクス討論会 実行委員

    2015.10

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  • 生命・化学・医療情報学は連携できるのか?

    Role(s): Planner, Organizing member

    生命医薬情報学連合大会2015年大会 若手合同セッション  京都大学 宇治キャンパス おうばくプラザ  2015.10

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