Updated on 2024/06/05

写真a

 
YUN Yeboon
 
Organization
Faculty of Environmental and Urban Engineering Professor
Title
Professor
Contact information
メールアドレス
External link

Degree

  • Doctor of Engineering ( Osaka University )

  • Master of Science ( Pukyong University )

Research Interests

  • システム工学

  • System Engineering and Optimization

Research Areas

  • Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Design engineering

  • Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Machine elements and tribology

  • Social Infrastructure (Civil Engineering, Architecture, Disaster Prevention) / Social systems engineering

  • Social Infrastructure (Civil Engineering, Architecture, Disaster Prevention) / Safety engineering

Education

  • Osaka University   Graduate School, Division of Engineering

    - 2000

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  • Pukyong University, Graduate School   Graduate School, Division of Natural Science   Applied Mathematics

    1996

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    Country: Korea, Republic of

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  • Pukyong University   Faculty of Science   Applied Mathematics

    - 1994

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    Country: Korea, Republic of

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  • Osaka University   Graduate School, Division of Engineering

    2000

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Research History

  • - 香川大学工学部, 准教授

    2007.4 - 2010.3

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  • - Faculty of Engneering, Kagawa University, Associate Professor

    2007.4 - 2010.3

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  • Kagawa University   Faculty of Engineering

    2006 - 2007

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  • Faculty of Engneering, Kagawa University, Associate Professor

    2006 - 2007

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  • Faculty of Engneering, Kagawa University, Assistant Professor

    2000 - 2006

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  • Kagawa University   Faculty of Engineering

    2000 - 2006

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Professional Memberships

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Committee Memberships

  •   Evolutionary Multi-Criterion Optimization, 5th International Conference on Evolutionary Multi-Criterion Optimization (EMO2009), Programming Committee  

    2009.4   

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  •   International Conference on Optimization: Techniques and Applications, The 7th International Conference on Optimization: Techniques and Applications, Organizer and Chairman of Session  

    2007.12   

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  •   Evolutionary Multi-Criterion Optimization, Fourth International Conference on Evolutionary Multi-Criterion Optimization (EMO2007), Programming Committee  

    2007.3   

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Papers

  • Determination of Reinforcement Degree of Large-Scale Structures in Construction Using Multiclass Linear Support Vector Piecewise-Large-scale Structures by using Multiclass Support Vector Machines Reviewed

    K.Tatsumi, S.Maruyama, Y.Yun

    pp.619 - pp.620   2024.5

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    Publishing type:Research paper (conference, symposium, etc.)  

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  • 地質帯を考慮した機械学習によるNATMトンネル支保パターン評価に関する研究 Reviewed

    長江 謙吾, 尹 禮分, 西尾 彰宣, 楠見 晴重

    第50回岩盤力学に関するシンポジウム講演集2024   pp.226-231   2024.1

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  • 深層学習によるトンネル切羽岩盤の亀裂,風化評価及び支保パターン決定への適用に関する研究 る研究 Reviewed

    中田 真成, 尹 禮分, 西尾 彰宣, 楠見 晴重

    第 50 回岩盤力学に関するシンポジウム講演論文集2024   pp.220-225   2024.1

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  • Application of Convolution Neural Networks to Genetic Algorithms for Solving Constrained Optimization Problems

    YUN Yeboon

    pp.112-116   2023.12

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  • Rock Evaluation of NATM Tunnel Face Using Deep Learning Reviewed

    M. Nakata, K. D. Halim, Y.B. Yun, H. Kusumi, A. Nishio

    15th ISRM Congress 2023 & 72nd Geomechanics Colloquium. Schubert & Kluckner (eds.)   pp.721-726   2023.10

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  • Determination of Reinforcement Degrees Based on Natural Conditions in Infrastructure Construction Using Recurrent Neural Networks Reviewed

    K. Tatsumi, H. Miyahara, Y.B. Yun

    Proceedings of the 23rd Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty   pp.15-16   2023.9

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  • Proposal of Efficiency Evaluation Method in Generalized Data Envelopment Analysis Reviewed

    YUN Yeboon

    2F3-2 pp.626-629   2023.9

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  • Application of Convolution Neural Networks to Genetic Algorithms for Solving Constrained Optimization Problems Reviewed

    K.Fujita, Y.B.Yun

    2F3-1   pp.622 - pp.625   2023.9

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  • 機械学習によるNATMトンネルにおける支保パターン判定 Reviewed

    K. D. Halim, 長江 謙吾, 尹 禮分, 楠見 晴重, 西尾 彰宣

    第 49 回岩盤力学に関するシンポジウム講演集   pp.121-126   2023.1

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  • 深層学習による NATM トンネル切羽面の岩盤評価 Reviewed

    中田真成, 梶山くるみ, 楠見晴重, 尹禮分, 西尾彰宣

    第 49 回岩盤力学に関するシンポジウム講演集   pp.127-132   2023.1

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  • Development of Fitness and Interactive Decision Making in Multi-Objective Optimization Reviewed

    Y.B. Yun, D. J. Park, M. Yoon

    Journal of Korean Society of Industrial and Systems Engineering   Vol. 45, No. 4 pp.109-117   2022.12

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  • 人口社会モデルによる都市部の各種交通政策導入効果の検討

    井ノ口 弘昭, 秋山 孝正, 尹 禮分

    第38回ファジィシシテムシンポジウム講演論文集   pp.167-170   2022.9

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  • Determination of Reinforcement Degrees in Constructing Large-scale Structures by using Multiclass Support Vector Machines Reviewed

    K. Tatsumi, S. Tsujioka, R. Masui, Y. Kusunoki, Y.B. Yun

    Knowledge-Based Systems   Vol.249 pp.1-11   108807 - 108807   2022.8

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    Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    DOI: 10.1016/j.knosys.2022.108807

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  • 緊急救命避難支援システムのための疎構造学習を用いた災害時行動状態の特徴分析 Reviewed

    尹 禮分, 和田 友孝

    信学技報   vol.122 No.142 pp.16-21   2022.7

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  • Evaluation of NATM Tunnrl Cutting Face in Japan Using Machine Learning Reviewed

    K.D.Halim, Y.B.Yun, H.Kusumi

    ARMA   22-0344   2022.6

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  • 機械学習を用いた災害時における行動状態の変化検知に関する検討 Reviewed

    尹 禮分, 和田 友孝

    第26回関西大学先端科学技術シンポジウム   pp.86-89   2022.1

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  • 機械学習による暑さ指数(WBGT)の予測に関する検討 Reviewed

    路 暢, 尹 禮分, 尹 敏

    第26回関西大学先端科学技術シンポジウム   pp.46-49   2022.1

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  • Convolutional Neural Network を用いたトンネル切羽面の岩盤亀裂評価への適用性に関する研究 Reviewed

    榎並 大希, 尹 禮分, 西尾 彰宣, 楠見 晴重

    第 48 回岩盤力学に関するシンポジウム講演集   p.214-218   2022.1

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  • Meta-Learning of RBF Networks in Sequential Approximate Optimization Reviewed

    Y.B. Yun, M.Yoon

    Journal of Nonlinear and Convex Analysis   Vol.22 pp.1-16   2021.12

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  • 自然状況を考慮したSVMによる大型構造物の補強度合判定 Reviewed

    増井 遼太, 巽 啓司, 楠木 祥文, 尹 禮分

    第64回自動制御連合講演会   pp.172-175   2021.11

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  • 機械学習による揚水に伴う水源揚水井の地下水運転水位の将来予測に関する研究 Reviewed

    平川 将寛, 尹 禮分, 楠見 晴重

    Kansai Geo Symposium 2021 ―地下水地盤環境・防災・計測技術に関するシンポジウム―   pp.213-217   2021.11

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  • Multiclass Support Vector Machines for Determination of Reinforcement Degree in Constructing Large-scale Structures Based on Data Characteristics Reviewed

    K. Tatsumi, R.Masui, S. Tsujioka, Y.Kusunoki, Y.B. Yun

    2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)   pp.1513-1518   2021.10

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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/smc52423.2021.9658763

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  • On Selecting Hyper-Parameters In RBF Network Reviewed

    Y.B.Yun, S. Yoshida, M. Yoon

    Yokohama Publishers   pp.1-16   2021.5

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  • Proposal of Meta-Learning in Support Vector Machines Reviewed

    Y.B. Yun, M.Yoon

    JOURNAL OF THE KOREAN DATA ANALYSIS SOCIETY   VOLUME 23NUMBER 1   2021.2

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  • 地震被害リスクを考慮した対策優先トンネルの選定 Reviewed

    浦川佳樹, 林久資, 尹禮分, 進士正人

    トンネル工学報告集第30巻   Ⅰ-29(7pages)   2020.11

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  • A Confirmatory Factor Analysis for Quality Competitiveness Excellence Company Evaluation Indicators Reviewed

    D. J. Park, Y.B. Yun, M. Yoon

    Journal of Society of Korea Industrial and Systems Engineering   Vol.43 No.3 pp.101-111   2020.9

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  • 機械学習によるトンネル切羽面の岩盤評価と支保設計への適用性に関する研究 Reviewed

    榎並大希, 金子元紀, 尹禮分, 楠見晴重, 西尾彰宣

    令和2年度土木学会全国大会第75回年次学術講演会講演概要集   Ⅲ-239(2pages)   2020.9

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  • NATMにおける機械学習による支保パターン決定に関する研究 Reviewed

    金子元紀, 榎並大希, 尹禮分, 楠見晴重, 西尾彰宣

    第47回岩盤力学に関するシンポジウム講演集   pp.57-61   2020.1

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  • グラフィカルラッソの糖尿病診断への適用性に関する検証 Reviewed

    金ヨンキョン, 尹禮分, M. Yoon, 中山弘隆

    第60回土木計画学研究発表会講演集   Vol.60,No.42-5, pp.1-4   2019.12

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  • データ構造を考慮した多クラスSupport Vector Machine Reviewed

    辻岡竣祐, 巽啓司, 楠木祥文, 尹禮分

    第62回自動制御連合講演会論文集   NO.2M1-01(5 pages)   2019.11

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  • 機械学習によるトンネル切羽の岩盤判定と支保パターン決定に関する研究 Reviewed

    金子元紀, 楠見晴重, 尹禮分, 西尾彰宣

    地下水地盤環境・防災・計測技術に関するシンポジウム 論文集   pp.227-232   2019.11

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  • Meta-Learning of Selecting Optimal Hyperparameters for RBF Networks Reviewed

    S.Yoshida, Y.B. Yun, H. Nakayama, M.Yoon

    第62回自動制御連合講演会論文集   2M1-02(2 pages)   2019.11

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  • Design for Support Patterns of NATM Tunnel using Machine Learning Reviewed

    Y.B. Yun, G.Kaneko, H.Kusumi, A.Nishio, T.Kurotani

    ICITG 2019: Information Technology in Geo-Engineering,Springer   pp.376-382   2019.9

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  • A Characteristic Analysis for Quality Competitiveness Excellent Company Reviewed

    D.J.Park, Y.B. Yun, I.S. Kang

    Journal Society Korea Industrial and Systems Engineering   Vol.42,No.3,pp.95-108   2019.9

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  • 機械学習によるトンネル切羽の岩盤判定と支保パターン決定に関する研究 Reviewed

    金子元紀, 楠見晴重, 尹禮分, 西尾彰宣

    令和元年度土木学会全国大会第74回年次学術講演会論文集   VI-385(2 pages)   2019.9

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  • エージェントモデルを用いた地方都市における環境未来都市の形成過程に関する考察 Reviewed

    井ノ口弘昭, 秋山孝正, 尹禮分

    知能と情報・日本知能情報ファジィ学会誌   Vol.31, No,6,pp.501-512   2019.6

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  • 機械学習を用いた多目的モデル予測制御 Reviewed

    尹禮分, 中山弘隆, 尹敏

    第61回自動制御連合講演会論文集   pp.1337-1340   2018.11

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  • Multi-Objective Model Predictive Control Reviewed

    Y.B. Yun, H. Nakayama, M.Yoon

    Proceedings of 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems   pp.304-308   2018.11

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  • 下水道管渠維持管理問題へグラフィカルラッソの適用に関する検討 Reviewed

    尹禮分, 中山弘隆, 尹敏

    第 34 回ファジィシステムシンポジウム講演論文集   pp.159-162   2018.9

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  • Intelligent multi-objective model predictive control applied to steam turbine start-up Reviewed

    Masakazu Shirakawa, Yeboon Yun, Masao Arakawa

    Journal of Advanced Mechanical Design, Systems and Manufacturing   Vol.12,No.1,pp.1-19 ( 1 )   2018.1

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    This study proposes an intelligent multi-objective model predictive control method in which an artificial neural network and a genetic algorithm are used to realize satisficing decision-making, which is an interactive multi-objective programming technique. We considered model predictive optimization under a dynamic environment with multiple objectives. To predict nonlinear function forms with dynamic plant characteristics, we applied a recurrent radial basis function network, which is a type of artificial neural network. For optimization with multiple objectives, we applied a satisficing trade-off method along with metaheuristic optimization in the form of genetic algorithms. The features of this control method are as follows. (1) Several conflicting control objectives can be optimized in online control based on multi-objective evaluation through human-computer interaction and (2) an optimal and flexible plant control can be performed within a restrained practical computing time for real-Time applications, with acceptable control quality using online adaptive model prediction. This study demonstrates the success of model prediction using computational intelligence combined with an interactive optimization technique for multi-objective model predictive control problems by applying the proposed method to steam turbine start-up control with multiple objectives consisting of the start-up time and rotor thermal stress of the steam turbine. The dynamic simulation results showed an effective control performance within a reasonable computing time.

    DOI: 10.1299/jamdsm.2018jamdsm0007

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  • Ensembled Support Vector Machines for Meta-Modeling Reviewed

    Y.B. Yun, H.Nakayama

    Communications in Computer and Information Science: Knowledge and Systems Sciences,Springer,   Vol.660, pp. 203-212   2016.11

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  • Generation of Pareto Optimal Solutions Using Generalized GDEA and PSO Reviewed

    Y.B. Yun, H. Nakayama, M. Yoon

    Journal of Global Optimization   Vol.64,No.1,pp. 49-61   2016.1

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  • Generation of Pareto Optimal Solutions Using Generalized DEA and PSO Reviewed

    Y.B.Yun, H.Nakayama

    Journal of Global Optimization・DOI 10.1007/ s10898-015-0314-3 (published online)   13 pages   2015.6

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  • Effective learning method for tuning parameters in SVM Reviewed

    Y.B.Yun, H.Nakayama

    The 9th International Symposium in Science and Technology   129-132頁   2014.8

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  • 需要予測の不確実性を考慮したロバスト運転計画

    西口純也, 黒崎淳, 綛田長生, 北山哲士, 荒川雅生, 中山弘隆, 尹禮分

    システム制御情報学会誌   Vol.27, No.5, 200-206頁   2014.5

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  • Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms

    Yeboon Yun, Hirotaka Nakayama

    JOURNAL OF GLOBAL OPTIMIZATION   57 ( 2 )   367 - 384   2013.10

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

    Meta-heuristic methods such as genetic algorithms (GA) and particle swarm optimization (PSO) have been extended to multi-objective optimization problems, and have been observed to be useful for finding good approximate Pareto optimal solutions. In order to improve the convergence and the diversity in the search of solutions using meta-heuristic methods, this paper suggests a new method to make offspring by utilizing the expected improvement (EI) and generalized data envelopment analysis (GDEA). In addition, the effectiveness of the proposed method will be investigated through several numerical examples in comparison with the conventional multi-objective GA and PSO methods.

    DOI: 10.1007/s10898-013-0038-1

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  • Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms

    Y.B.Yun, H.Nakayama

    Journal of Global Optimization   Vol.57, Issue2, 367-384頁   2013.10

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  • Generating of Pareto frontiers using machine learning

    Y.B.Yun, N.Y.Jung, M.Yoon

    Journal of Korean Data and Information Science Society   Vol.24, No.3, 495-504頁   2013.5

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  • Multicriteria Decision Aid and Artificial Intelligence: Links, Theory, and Applications Reviewed

    Y.B.Yun, H.Nakayama

    John Wiley & Sons   209-234頁(edited by Doumpos and Grigoroudis)   2013.4

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  • Comparison of customer classification performance using machine learning Reviewed

    J.G.Eom, H.S.Seo, Y.B.Yun, M.Yoon

    Journal of the Korean Data Analysis Society   Vol.14,No.5(B),2441-2450頁   2012.10

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  • 期待改善量と一般化包絡分析法を用いたパレート最適解の生成法 Reviewed

    尹禮分, 中山弘隆

    システム制御情報学会論文誌   Vol.25,No.8,189-195頁   2012.8

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  • Prediction of bankruptcy data by machine learning techniques Reviewed

    D.J.Park, Y.B.Yun, M.Yoon

    Korean Data and Information Science Society   Vol.23,No.3,569 -577頁   2012.5

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  • Optimal inventory control for deteriorative goods considering delays of deliveries Reviewed

    Y.B.Yun, S.Osako, T.Nakai

    Journal of Information and Optimization Sciences   Vol. 33,No. 1,103-114頁   2012.1

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  • Game theoretical approaches concerning R&D Reviewed

    Y.B.Yun, Y.Hisata, T.Nakai

    Journal of Information and Optimization Sciences   Vol. 33,No. 1,115-133頁   2012.1

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  • 一般化包絡分析法を用いた多目的PSO法 Reviewed

    尹禮分, 中山弘隆

    システム制御情報学会論文誌   Vol.23,No.9   2010.9

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  • ロジスティック回帰モデルを用いた災害発生危険度の活用に関する研究 Reviewed

    篠崎嗣浩, 芦田悠輔, 朴東俊, 尹敏, 尹禮分, 大石博之, 古川浩平

    砂防学会誌   Vol.63・No.1・14-21項   2010.5

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  • Multi-objective model predictive control

    H.Nakayama, Y.B.Yun, M.Shirakawa

    MCDM for Sustainable Energy and Transportation Systems   Vol.634・277-288頁   2010.1

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  • 局地的な集中豪雨に対応した逐次更新型の降雨時列車運行規制基準に関する研究 Reviewed

    三輪一弘, 荒木義則, 竹本大昭, 清家礼雄, 尹禮分, 中山弘隆, 古川浩平

    土木学会論文集F   Vol.65, No.4・485-494頁   2009.11

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  • Multi-objective optimization based on meta-modeling by using support vector regression

    Yeboon Yun, Min Yoon, Hirotaka Nakayama

    OPTIMIZATION AND ENGINEERING   10 ( 2 )   167 - 181   2009.6

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

    Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.

    DOI: 10.1007/s11081-008-9063-1

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  • Practical Approach to Outlier Detection Using Support Vector Regression

    Junya Nishiguchi, Chosei Kaseda, Hirotaka Nakayama, Masao Arakawa, Yeboon Yun

    ADVANCES IN NEURO-INFORMATION PROCESSING, PT I   5506   995 - +   2009

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:SPRINGER-VERLAG BERLIN  

    For precise estimation with soft sensors, it is necessary to remove outliers from the measured raw data before constructing the model. Conventionally, visualization and maximum residual error have been used for outlier detection, but they often fail to detect outliers for nonlinear function with multidimensional input. In this paper we propose a practical approach to outlier detection using Support Vector Regression, which reduces computational cost and defines outlier threshold appropriately. We apply this approach to both test and industrial datasets for validation.

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  • 計算知能を用いた逐次近似多目的最適化手法

    尹禮分, 中山弘隆, 尹敏

    計測自動制御学会論文集   Vol.43,No.8・672-678頁   2007.8

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  • 事業優先順位設定手法の適用事例

    佐藤丈晴, 尹禮分, 古川浩平

    日本地すべり学会誌   Vol.44,No.1・46-49頁   2007.5

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  • サポートベクターマシンによる対策工効果を考慮した土石流危険渓流の危険度評価

    大石博之, 尹禮分, 中山弘隆, 古川浩平

    砂防学会誌   Vol.60・No.1・3-10頁   2007.5

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  • OLDFとSVMの比較研究-種々のデータによるSVMとの比較- Reviewed

    新村秀一 尹禮分

    成蹊大学経済学部論集   Vol.37・No.2・89-119頁   2007.3

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  • サポートベクターマシンによる対策工効果を考慮した斜面災害危険度の設定

    大石博之, 小林央宜, 尹禮分, 田中浩一, 中山弘隆, 古川浩平

    土木学会論文集   Vol.63・No.1・107-118頁   2007.1

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  • Sequential approximation method in multi-objective optimization using aspiration level approach

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS   4403   317 - +   2007

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:SPRINGER-VERLAG BERLIN  

    One of main issues in multi-objective optimization is to support for choosing a final solution from Pareto frontier which is the set of solution to problem. For generating a part of Pareto optimal solution closest to an aspiration level of decision maker, not the whole set of Pareto optimal solutions, we propose a method which is composed of two steps; i) approximate the form of each objective function by using support vector regression on the basis of some sample data, and ii) generate Pareto frontier to the approximated objective functions based on given the aspiration level. In addition, we suggest to select additional data for approximating sequentially the forms of objective functions by relearning step by step. Finally, the effectiveness of the proposed method will be shown through some numerical examples.

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  • MOP/GP models for machine learning

    H Nakayama, YB Yun, T Asada, M Yoon

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   166 ( 3 )   756 - 768   2005.11

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    Techniques for machine learning have been extensively studied in recent years as effective tools in data mining. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problems with two class sets, its idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. This task is performed by solving a quadratic programming problem in a traditional formulation, and can be reduced to solving a linear programming in another formulation. However, the idea of maximal margin separation is not quite new: in the 1960s the multi-surface method (MSM) was suggested by Mangasarian. In the 1980s, linear classifiers using goal programming were developed extensively.
    This paper presents an overview on how effectively MOP/GP techniques can be applied to machine learning such as SVM, and discusses their problems. (c) 2004 Elsevier B.V. All rights reserved.

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  • Genetic algorithm for multi-objective optimization using GDEA Reviewed

    Y.B.Yun, M.Yoon, H.Nakayama

    Advances in Natural Computation   Part III,Vol.3612・409-416頁   2005.9

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  • Multiple criteria decision making with generalized DEA and an aspiration level method Reviewed

    Y.B.Yun, H.Nakayama, M.Arakawa

    European Journal of Operational Research   Vol.15・No.3・697-706頁   2004.11

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  • Pattern classification by goal programming and support vector machines

    T.Asada, Y.B.Yun, H.Nakayama, T.Tanino

    Computational Management Sciences   Vol.1・No.3・211-230頁   2004.10

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  • A generalized model for data envelopment analysis Reviewed

    Y.B.Yun, H.Nakayama, T.Tanino

    European Journal of Operational Research   Vol.157,No.1・87-105頁   2004.8

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  • Total margin algorithms in support vector machines Reviewed

    M.Yoon, Y.B.Yun, H.Nakayama

    IEICE Transactions on Information and Systems   Vol.E87・No.5・1223-1230頁   2004.5

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  • Prediction of business failure by total margin support vector machines

    Y Yun, M Yoon, H Nakayama, W Shiraki

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS   3213   441 - 448   2004

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:SPRINGER-VERLAG BERLIN  

    The analysis and management of business failure has been recognized to be important in the area of financial management in the evaluation of firms' performance and the assessment of their viability. To this end, effective failure-prediction models are needed. This paper describes a new approach to prediction of business failure using the total margin algorithm which is a kind of support vector machine. It will be shown that the proposed method can evaluate the risk of failure better than existing methods through some real data.

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  • Goal programming approaches to support vector machines

    H.Nakayama, Y.B.Yun, T.Asada, M.Yoon

    Knowledge-Based Intelligent Information and Engineering Systems   Part I,Vol.2773・356-363頁   2003.10

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  • A soft margin algorithm controlling tolerance directly

    M.Yoon, H.Nakayama, Y.B.Yun

    Multi-Objective Programming and Goal Programming: Theory and Applications   Vol.21・281-288頁   2003.7

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  • Support Vector Machines Controlling Noise Influence Directly

    YOON Min, NAKAYAMA Hirotaka, YUN Yeboon

    Vol.39・No.1・82-84頁 ( 1 )   82 - 84   2003.1

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    CiNii Books

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  • Dual approach to generalized data envelopment analysis based on production possibility Reviewed

    Y.B.Yun, H.Nakayama, T.Tanino

    Multi-Objective Programming and Goal Programming:Recent Developments   Vol.12・196-205頁   2002.3

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  • Generation of efficient frontiers in multi-objective optimization problems by generalized data envelopment analysis Reviewed

    Y.B.Yun, H.Nakayama, T.Tanino, M.Arakawa

    European Journal of Operational Research   Vol.129・No.3・586-595頁   2001.3

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  • 一般化包絡分析法への双対アプローチ Reviewed

    尹禮分, 中山弘隆, 谷野哲三

    計測自動制御学会論文集   Vol.36・No.9・804-809頁   2000.9

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  • On efficiency of data envelopment analysis

    Y.B.Yun, H.Nakayama, T.Tanino

    Research and Practice in Multiple Criteria Decision Making   Vol.487・208-217頁   2000.6

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  • 一般化包絡分析法と遺伝アルゴリズムによる多目的最適化の一手法 Reviewed

    尹禮分, 中山弘隆, 谷野哲三, 荒川雅生

    システム制御情報学会論文誌   Vol.13,No.4・179-185頁   2000.4

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  • 包絡分析法(DEA)モデルの一般化 Reviewed

    尹禮分, 中山弘隆, 谷野哲三

    計測自動制御学会論文集   Vol.35・No.8・1813-1818頁   1999.8

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  • Multi objective symmetric duality with cone constraints

    D.S. Kim, Y.B.Yun

    1998.6

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  • Second-order symmetric and self duality in multiobjective programming

    DS Kim, YB Yun, H Kuk

    APPLIED MATHEMATICS LETTERS   10 ( 2 )   17 - 22   1997.3

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:PERGAMON-ELSEVIER SCIENCE LTD  

    We suggest the second-order symmetric and self dual programs in multiobjective nonlinear programming. For these second-order symmetric dual programs, we prove the weak, strong, and converse duality theorems under convexity and concavity conditions. Also, we prove the self duality theorem for these second-order self dual programs and illustrate its example.

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Books

  • Generalized data envelopment analysis and computational intelligence in multiple criteria decision making Reviewed

    Y.B. Yun, H. Nakayama( Role: Joint author)

    Multicriteria Decision Aid and Artificial Intelligence: Links, Theory, and Applications, John Wiley & Sons (Eds: M. Doumpos, E. Grigoroudis)  2013.4 

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  • 計算知能の逐次近似多目的最適化への応用 Reviewed

    中山弘隆, 尹禮分( Role: Joint author)

    オペレーションズ・リサーチ誌  2012.5 

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  • メタモデルと多目的最適化手法 Reviewed

    尹 禮分( Role: Sole author)

    システム制御情報学会学会誌  2011.9 

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  • Computational Intelligence in Expensive Optimization Problems (Adaptation, Learning, and Optimization) Reviewed

    H.Nakayama, Y.B.Yun, M.Shirakawa( Role: Joint author)

    Springer  2010.5 

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  • Sequential Approximate Multi-Objective Optimization using Computational Intelligence Reviewed

    H.Nakayama, Y.B.Yun, M.Yoon( Role: Joint author)

    Springer  2009.5 

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  • 多目的最適化と工学設計 -しなやかシステム工学アプローチ- Reviewed

    中山弘隆, 岡部達哉, 荒川雅生, 尹 禮分( Role: Joint author)

    現代図書  2007.12 

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  • 多目的最適化と工学設計

    現代図書  2007 

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  • 多目的最適化と工学設計

    現代図書  2007 

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  • Multi-Objective Machine Learning:Studies in Computational Intelligence Reviewed

    H.Nakayama, Y.B.Yun( Role: Joint author)

    Springer  2006.3 

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  • Multiple Criteria Optimization -State of the Art Annotated Bibliographic Surveys- Reviewed

    H.Nakayama, M.Arakawa, Y.B.Yun( Role: Joint author)

    Kluwer  2002.6 

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  • New Frontiers of Decision Making for the Information Technology Era Reviewed

    H.Nakayama, T.Tanino, Y.B.Yun( Role: Joint author)

    World Scientific Publishing Co. Pte. Ltd.  2000.5 

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  • Sequential Approximate Multiobjective Optimization Using Computational Intelligence

    Springer 

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MISC

  • Multi-objective optimization based on meta-modeling by using support vector regression

    Yeboon Yun, Min Yoon, Hirotaka Nakayama

    OPTIMIZATION AND ENGINEERING   10 ( 2 )   167 - 181   2009.6

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    Language:English   Publisher:SPRINGER  

    Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.

    DOI: 10.1007/s11081-008-9063-1

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  • Multi-objective optimization based on meta-modeling by using support vector regression

    Yeboon Yun, Min Yoon, Hirotaka Nakayama

    OPTIMIZATION AND ENGINEERING   10 ( 2 )   167 - 181   2009.6

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    Language:English   Publisher:SPRINGER  

    Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.

    DOI: 10.1007/s11081-008-9063-1

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  • Practical approach to outlier detection using support vector regression

    Junya Nishiguchi, Chosei Kaseda, Hirotaka Nakayama, Masao Arakawa, Yeboon Yun

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   5506 ( 1 )   995 - 1001   2009

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    For precise estimation with soft sensors, it is necessary to remove outliers from the measured raw data before constructing the model. Conventionally, visualization and maximum residual error have been used for outlier detection, but they often fail to detect outliers for nonlinear function with multidimensional input. In this paper we propose a practical approach to outlier detection using Support Vector Regression, which reduces computational cost and defines outlier threshold appropriately. We apply this approach to both test and industrial datasets for validation. © 2009 Springer Berlin Heidelberg.

    DOI: 10.1007/978-3-642-02490-0_121

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  • Practical approach to outlier detection using support vector regression

    Junya Nishiguchi, Chosei Kaseda, Hirotaka Nakayama, Masao Arakawa, Yeboon Yun

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   5506 ( 1 )   995 - 1001   2009

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    For precise estimation with soft sensors, it is necessary to remove outliers from the measured raw data before constructing the model. Conventionally, visualization and maximum residual error have been used for outlier detection, but they often fail to detect outliers for nonlinear function with multidimensional input. In this paper we propose a practical approach to outlier detection using Support Vector Regression, which reduces computational cost and defines outlier threshold appropriately. We apply this approach to both test and industrial datasets for validation. © 2009 Springer Berlin Heidelberg.

    DOI: 10.1007/978-3-642-02490-0_121

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  • Multi-objective Model Predictive Optimization using Computational Intelligence

    Hirotaka Nakayama, Yeboon Yun

    Artifical Intelligence in Theory and Practice II   1   319 - 328   2008

  • Multi-objective Model Predictive Optimization using Computational Intelligence

    Hirotaka Nakayama, Yeboon Yun

    Artifical Intelligence in Theory and Practice II   1   319 - 328   2008

  • 計算知能を用いた逐次近似多目的最適化手法 Reviewed

    Yeboon Yun, 中山 弘隆, 尹 敏

    計測自動制御学会論文集   Vol.43,No.8・672-678頁 ( 8 )   672 - 678   2007.8

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    Publisher:The Society of Instrument and Control Engineers  

    Many decision making problems are formulated as multi-objective optimization problems so as to satisfy diverse demands of decision maker. One of main issues in multi-objective optimization is how to find a Pareto optimal solution which meets decision maker's demands. To the end, interactive optimization methods, for example aspiration level methods, have been developed. On the other hand, several methods using genetic algorithm have been researched for generating the whole set of Pareto optimal solutions. However, those conventional methods have some problems when applying them to real practical problems considering the number of function evaluations. In many engineering design problems, generally, there are black-box objective functions whose forms are not explicitly known in terms of design variables. Under this circumstance, given design variables, the values of objective function can be obtained by sampled real/computational experiments such as structural analysis, fluid-mechanical analysis, thermodynamic analysis, and so on. These analyses are expensive and time consuming, and it is an important issue in real application problems how to make the number of necessary analyses as few as possible. In this paper, we suggest a sequential approximation method for solving multi-objective optimization problems, which is composed of two stages; i) the first stage is to predict the form of each objective function by using support vector regression on the basis of some experimental data, ii) the second stage is to find the Pareto optimal solution closest to the given aspiration level of decision maker using thepredicted objective functions, and in parallel, to generate the whole set of Pareto optimal solutions. Also, we discuss a way how to select additional experimental data for revising the form of objective function by relearning step by step. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.

    DOI: 10.9746/ve.sicetr1965.43.672

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  • Sequential Approximation Method in Multi-objective Optimization by Using Computational Intelligence Reviewed

    YUN Yeboon, NAKAYAMA Hirotaka, YOON Min

    Transactions of the Society of Instrument and Control Engineers   Vol.43,No.8・672-678頁 ( 8 )   672 - 678   2007.8

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    Publisher:The Society of Instrument and Control Engineers  

    Many decision making problems are formulated as multi-objective optimization problems so as to satisfy diverse demands of decision maker. One of main issues in multi-objective optimization is how to find a Pareto optimal solution which meets decision maker's demands. To the end, interactive optimization methods, for example aspiration level methods, have been developed. On the other hand, several methods using genetic algorithm have been researched for generating the whole set of Pareto optimal solutions. However, those conventional methods have some problems when applying them to real practical problems considering the number of function evaluations. In many engineering design problems, generally, there are black-box objective functions whose forms are not explicitly known in terms of design variables. Under this circumstance, given design variables, the values of objective function can be obtained by sampled real/computational experiments such as structural analysis, fluid-mechanical analysis, thermodynamic analysis, and so on. These analyses are expensive and time consuming, and it is an important issue in real application problems how to make the number of necessary analyses as few as possible. In this paper, we suggest a sequential approximation method for solving multi-objective optimization problems, which is composed of two stages; i) the first stage is to predict the form of each objective function by using support vector regression on the basis of some experimental data, ii) the second stage is to find the Pareto optimal solution closest to the given aspiration level of decision maker using thepredicted objective functions, and in parallel, to generate the whole set of Pareto optimal solutions. Also, we discuss a way how to select additional experimental data for revising the form of objective function by relearning step by step. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.

    DOI: 10.9746/ve.sicetr1965.43.672

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  • A case of the priority level method Reviewed

    SATO Takeharu, YUN Yeboon, FURUKAWA Kohei

    Landslides   Vol.44,No.1・46-49頁 ( 1 )   46 - 49   2007.5

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    Language:Japanese   Publisher:The Japan Landslide Society  

    DOI: 10.3313/jls.44.46

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    Other Link: https://jlc.jst.go.jp/DN/JALC/00295452737?from=CiNii

  • 事業優先順位設定手法の適用事例 Reviewed

    佐藤丈晴, 尹禮分, 古川浩平

    日本地すべり学会誌   Vol.44,No.1・46-49頁 ( 1 )   46 - 49   2007.5

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    Language:Japanese   Publisher:The Japan Landslide Society  

    DOI: 10.3313/jls.44.46

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  • サポートベクターマシンによる対策工効果を考慮した土石流危険渓流の危険度評価 Reviewed

    大石博之, 尹禮分, 中山弘隆, 古川浩平

    砂防学会誌   Vol.60・No.1・3-10頁 ( 1 )   3 - 10   2007.5

  • サポートベクターマシンによる対策工効果を考慮した土石流危険渓流の危険度評価 Reviewed

    大石博之, 尹禮分, 中山弘隆, 古川浩平

    砂防学会誌   Vol.60・No.1・3-10頁 ( 1 )   3 - 10   2007.5

  • サポートベクターマシンによる対策工効果を考慮した斜面災害危険度の設定 Reviewed

    大石博之, 小林央宜, 尹禮分, 田中浩一, 中山弘隆, 古川浩平

    土木学会論文集   Vol.63・No.1・107-118頁 ( 1 )   107 - 118   2007.1

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    Language:Japanese   Publisher:Japan Society of Civil Engineers  

    It is one of the important problems to push forward a disaster prevention business effectively to evaluate danger of dangerous spots for sediment-related disasters. Although it is originally desirable to perform examination based on ground engineering about this, it is very difficult to perform detailed examination individually so that an object point becomes an enormous number. Therefore this study decides to utilize mathematic technique: Support Vector Machine (SVM) and tries to evaluate a risk by learning data of each slope. As a result of having analyzed slope data using SVM, it becomes clear that can evaluate a risk with high precision than a conventional rating method. In addition, we suggest it about a method to evaluate an effect of construction measures using SVM. These results are thought to be very useful in anti-disaster measures.

    DOI: 10.2208/jscejf.63.107

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  • EVALUATION OF THE DANGER OF SLOPES IN VIEW OF THE EFFECT VALUE OF MEASURE CONSTRUCTIONS USING SUPPORT VECTOR MACHINE Reviewed

    OISHI Hiroyuki, KOBAYASHI Hiroki, YUN Yeboon, TANAKA Hirokazu, NAKAYAMA Hirotaka, FURUKAWA Kohei

    Journal of Japan Society of Civil Engineers, Ser. F1 (Tunnel Engineering)   Vol.63・No.1・107-118頁 ( 1 )   107 - 118   2007.1

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    Language:Japanese   Publisher:Japan Society of Civil Engineers  

    It is one of the important problems to push forward a disaster prevention business effectively to evaluate danger of dangerous spots for sediment-related disasters. Although it is originally desirable to perform examination based on ground engineering about this, it is very difficult to perform detailed examination individually so that an object point becomes an enormous number. Therefore this study decides to utilize mathematic technique: Support Vector Machine (SVM) and tries to evaluate a risk by learning data of each slope. As a result of having analyzed slope data using SVM, it becomes clear that can evaluate a risk with high precision than a conventional rating method. In addition, we suggest it about a method to evaluate an effect of construction measures using SVM. These results are thought to be very useful in anti-disaster measures.

    DOI: 10.2208/jscejf.63.107

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  • Multi-Objective Optimization Based on Meta-Modeling by Using Support Vector Machine

    Yeboon Yun, Min Yoon, Hirotaka Nakayama

    Proceedings of The 7th International Conference on Optimization:Techniques and Applications   2007

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  • Multi-Objective Optimization Based on Meta-Modeling by Using Support Vector Machine

    Yeboon Yun, Min Yoon, Hirotaka Nakayama

    Proceedings of The 7th International Conference on Optimization:Techniques and Applications   2007

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  • 客観的斜面崩壊危険度評価を用いた事業優先順位設定手法の応用事例

    佐藤丈晴, 尹禮分, 古川浩平

    第46回日本地すべり学会研究発表会講演集   345 - 348   2007

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  • Sequential Approximation Method in Multi-Objective Optimization using Aspiration Level Approach

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    Lecture Notes in Computer science   LNCS 4403   317 - 329   2007

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  • Sequential Approximation Method in Multi-Objective Optimization using Aspiration Level Approach

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    Lecture Notes in Computer science   LNCS 4403   317 - 329   2007

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  • Combining Aspiration Level Methods in Multi-objective Programming and Sequential Approximate Optimization using Computational Intelligence

    Hirotaka nakayama, Yeboon Yun, Min Yoon

    Proceedings of First IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making   2007

  • Combining Aspiration Level Methods in Multi-objective Programming and Sequential Approximate Optimization using Computational Intelligence

    Hirotaka nakayama, Yeboon Yun, Min Yoon

    Proceedings of First IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making   2007

  • 客観的斜面崩壊危険度評価を用いた事業優先順位設定手法の応用事例

    佐藤丈晴, 尹禮分, 古川浩平

    第46回日本地すべり学会研究発表会講演集   345 - 348   2007

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  • Computational Intelligence Method in Multi-Objective Optimization

    Yeboon Yun, Min Yoon, Hirotaka nakayama

    Proceedings of SICE-ICASE (Society of Instrument and Control Engineers - Institute of Control, Automation and Systems Engineers) International Joint Conference   CD-ROM   2006

  • Computational Intelligence Method in Multi-Objective Optimization

    Yeboon Yun, Min Yoon, Hirotaka nakayama

    Proceedings of SICE-ICASE (Society of Instrument and Control Engineers - Institute of Control, Automation and Systems Engineers) International Joint Conference   CD-ROM   2006

  • Generating support vector machines using multi-objective optimization and goal programming

    Hirotaka Nakayama, Yeboon Yun

    Studies in Computational Intelligence   16   173 - 198   2006

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    Support Vector Machine (SVM) is gaining much popularity as one of effective methods for machine learning in recent years. In pattern classification problems with two class sets, it generalizes linear classifiers into high dimensional feature spaces through nonlinear mappings defined implicitly by kernels in the Hilbert space so that it may produce nonlinear classifiers in the original data space. Linear classifiers then are optimized to give the maximal margin separation between the classes. This task is performed by solving some type of mathematical programming such as quadratic programming (QP) or linear programming (LP). On the other hand, from a viewpoint of mathematical programming for machine learning, the idea of maximal margin separation was employed in the multi-surface method (MSM) suggested by Mangasarian in 1960's. Also, linear classifiers using goal programming were developed extensively in 1980's. This chapter introduces a new family of SVM using multi-objective programming and goal programming (MOP/GP) techniques, and discusses its effectiveness throughout several numerical experiments. © 2006 Springer-Verlag Berlin Heidelberg.

    DOI: 10.1007/11399346_8

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  • Generating support vector machines using multi-objective optimization and goal programming

    Hirotaka Nakayama, Yeboon Yun

    Studies in Computational Intelligence   16   173 - 198   2006

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    Support Vector Machine (SVM) is gaining much popularity as one of effective methods for machine learning in recent years. In pattern classification problems with two class sets, it generalizes linear classifiers into high dimensional feature spaces through nonlinear mappings defined implicitly by kernels in the Hilbert space so that it may produce nonlinear classifiers in the original data space. Linear classifiers then are optimized to give the maximal margin separation between the classes. This task is performed by solving some type of mathematical programming such as quadratic programming (QP) or linear programming (LP). On the other hand, from a viewpoint of mathematical programming for machine learning, the idea of maximal margin separation was employed in the multi-surface method (MSM) suggested by Mangasarian in 1960's. Also, linear classifiers using goal programming were developed extensively in 1980's. This chapter introduces a new family of SVM using multi-objective programming and goal programming (MOP/GP) techniques, and discusses its effectiveness throughout several numerical experiments. © 2006 Springer-Verlag Berlin Heidelberg.

    DOI: 10.1007/11399346_8

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  • Multi-objective optimization based on aspiration levels and approximation of Pareto frontier

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    CJK-OSM 4: The Fourth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems   491 - 496   2006

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    Language:English   Publisher:DALIAN UNIV TECHNOL PRESS  

    An ultimate aim in multi-objective optimization is to support for choosing a final solution from Pareto frontier. For generating a part of Pareto frontier closest to an aspiration level of decision maker, this paper proposes a new method which approximates the form of each objective function by using support vector regression on the basis of some sample data, and generates Pareto frontier to the approximated objective functions based on a given aspiration level. In addition, we suggest to select additional data for revising the forms of objective functions by relearning step by step. Finally, the effectiveness of the proposed method will be shown through some numerical examples.

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  • 土砂災害防止施設の施工に関する客観的な優先順位設定手法の開発

    佐藤丈晴, 尹禮分, 古川浩平

    第45回日本地すべり学会研究発表会講演集   311 - 314   2006

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  • Regression by Support Vector Machines and its Applications to Engineering Design

    Hirotaka nakayama, Yeboon Yun

    Proceedings of The Fourth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical System   391 - 396   2006

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  • Support Vector regression Based on Goal Programming and Multi-Objective Programming

    Yeboon Yun, Hirotaka Nakayama

    World Congress on Computational Intelligence;IJCNN   2006

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  • Regression by support vector machines and its applications to engineering design

    Hirotaka Nakayama, Yeboon Yun

    CJK-OSM 4: THE FOURTH CHINA-JAPAN-KOREA JOINT SYMPOSIUM ON OPTIMIZATION OF STRUCTURAL AND MECHANICAL SYSTEMS   391 - 396   2006

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    Language:English   Publisher:DALIAN UNIV TECHNOL PRESS  

    Support vector machine (SVM) has been recognized as a powerful machine learning technique SVM was originally developed for pattern classification and later extended to regression (Vapnik et al 1995) In pattern classification problems with two class sets. it generalizes linear classifiers into high dimensional feature spaces through nonlinear mappings defined implicitly by kernels in the Hilbert space so that it may produce nonlinear classifiers in the original data space. Linear classifiers then are optimized to give the maximal margin separation between the classes. This task is performed by solving some type of mathematical programming such as quadratic programming (QP) or linear programming (LP). On the other hand. from a viewpoint of mathematical programming for machine learning, the idea of maximal margin separation was employed in the multi-surface method (MSM) suggested by Mangasarian in 1960's. Also. linear classifiers using goal programming were developed extensively in 1980's The authors have developed several varieties of SVM using multi-objective programming and goal programming (MOP/GP) techniques. This paper extends the family of SVM for classification to regression. and discusses their characteristics and abilities through numerical experiments in engineering design problems.

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  • Multi-objective optimization based on aspiration levels and approximation of Pareto frontier

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    CJK-OSM 4: The Fourth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems   491 - 496   2006

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    An ultimate aim in multi-objective optimization is to support for choosing a final solution from Pareto frontier. For generating a part of Pareto frontier closest to an aspiration level of decision maker, this paper proposes a new method which approximates the form of each objective function by using support vector regression on the basis of some sample data, and generates Pareto frontier to the approximated objective functions based on a given aspiration level. In addition, we suggest to select additional data for revising the forms of objective functions by relearning step by step. Finally, the effectiveness of the proposed method will be shown through some numerical examples.

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  • 包絡分析を応用した事業優先度設定事例

    佐藤丈晴, 尹禮分, 古川浩平

    第45回日本地すべり学会研究発表会講演集   413 - 414   2006

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  • Support Vector regression Based on Goal Programming and Multi-Objective Programming

    Yeboon Yun, Hirotaka Nakayama

    World Congress on Computational Intelligence;IJCNN   2006

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  • 計算知能を用いたハイブリッド型多目的最適化法

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    第7回最適化シンポジウム講演論文集 2006   111 - 116   2006

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  • 計算知能を用いたハイブリッド型多目的最適化法

    尹禮分, 中山弘隆, 尹敏

    第7回最適化シンポジウム講演論文集 2006   111 - 116   2006

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  • MOP/GP models for machine learning

    Hirotaka Nakayama, Ye Boon Yun, Takeshi Asada, Min Yoon

    European Journal of Operational Research   166 ( 3 )   756 - 768   2005.11

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    Techniques for machine learning have been extensively studied in recent years as effective tools in data mining. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming
    MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problems with two class sets, its idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. This task is performed by solving a quadratic programming problem in a traditional formulation, and can be reduced to solving a linear programming in another formulation. However, the idea of maximal margin separation is not quite new: in the 1960s the multi-surface method (MSM) was suggested by Mangasarian. In the 1980s, linear classifiers using goal programming were developed extensively. This paper presents an overview on how effectively MOP/GP techniques can be applied to machine learning such as SVM, and discusses their problems. © 2004 Elsevier B.V. All rights reserved.

    DOI: 10.1016/j.ejor.2004.03.043

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  • MOP/GP models for machine learning

    H Nakayama, YB Yun, T Asada, M Yoon

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   166 ( 3 )   756 - 768   2005.11

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    Language:English   Publisher:ELSEVIER SCIENCE BV  

    Techniques for machine learning have been extensively studied in recent years as effective tools in data mining. Although there have been several approaches to machine learning, we focus on the mathematical programming (in particular, multi-objective and goal programming; MOP/GP) approaches in this paper. Among them, Support Vector Machine (SVM) is gaining much popularity recently. In pattern classification problems with two class sets, its idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. This task is performed by solving a quadratic programming problem in a traditional formulation, and can be reduced to solving a linear programming in another formulation. However, the idea of maximal margin separation is not quite new: in the 1960s the multi-surface method (MSM) was suggested by Mangasarian. In the 1980s, linear classifiers using goal programming were developed extensively.
    This paper presents an overview on how effectively MOP/GP techniques can be applied to machine learning such as SVM, and discusses their problems. (c) 2004 Elsevier B.V. All rights reserved.

    DOI: 10.1016/j.ejor.2004.03.043

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  • Genetic algorithm using GDEA in multi-objective optimization problems

    Yeboon Yun, Hirotaka nakayama, Min Yoon

    Proceeding of The Sixth Metaheuristics International Conference   CD-ROM   2005

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  • Genetic algorithm using GDEA in multi-objective optimization problems

    Yeboon Yun, Hirotaka nakayama, Min Yoon

    Proceeding of The Sixth Metaheuristics International Conference   CD-ROM   2005

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  • Genetic algorithm for multi-objective optimization using GDEA

    Y Yun, M Yoon, H Nakayama

    ADVANCES IN NATURAL COMPUTATION, PT 3, PROCEEDINGS   3612   409 - 416   2005

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    Recently, many genetic algorithms (CAs) have been developed as an approximate method to generate Pareto frontier (the set of Pareto optimal solutions) to multi-objective optimization problem. In multi-objective GAs, there are two important problems : how to assign a fitness for each individual, and how to make the diversified individuals. In order to overcome those problems, this paper suggests a new multi-objective CA using generalized data envelopment analysis (GDEA). Through numerical examples, the paper shows that the proposed method using CDEA can generate well-distributed as well as well-approximated Pareto frontiers with less number of function evaluations.

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  • Genetic algorithm for multi-objective optimization using GDEA

    Yeboon Yun, Min Yoon, Hirotaka nakayama

    Lecture Notes in Computer Science:Advances in Natural Computation   Part III, 3612   409 - 416   2005

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  • Multiple criteria decision making with generalized DEA and an aspiration level method

    YB Yun, H Nakayama, M Arakawa

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   158 ( 3 )   697 - 706   2004.11

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    In this paper, we suggest an aspiration level approach using generalized data envelopment analysis (GDEA) and genetic algorithms (GA) in multiple criteria decision making such as engineering design problems. It will be shown that several Pareto optimal solutions close to an aspiration level of decision maker can be listed up as candidates of a final decision making solution by the proposed method. Through the robust design problem, it will be proved also that the aspiration level method using GDEA is useful for supporting a decision making of complex system. (C) 2003 Elsevier B.V. All rights reserved.

    DOI: 10.1016/S0377-2217(03)00375-8

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  • Multiple criteria decision making with generalized DEA and an aspiration level method

    YB Yun, H Nakayama, M Arakawa

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   158 ( 3 )   697 - 706   2004.11

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    In this paper, we suggest an aspiration level approach using generalized data envelopment analysis (GDEA) and genetic algorithms (GA) in multiple criteria decision making such as engineering design problems. It will be shown that several Pareto optimal solutions close to an aspiration level of decision maker can be listed up as candidates of a final decision making solution by the proposed method. Through the robust design problem, it will be proved also that the aspiration level method using GDEA is useful for supporting a decision making of complex system. (C) 2003 Elsevier B.V. All rights reserved.

    DOI: 10.1016/S0377-2217(03)00375-8

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  • Pattern classification by goal programming and support vector machines Reviewed

    Takeshi Asada, Yeboon Yun, Hirotaka Nakayama, Tetsuzo Tanino

    Computational Management Sciences   Vol.1・No.3・211-230頁 ( 3 )   211 - 230   2004.10

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  • Pattern classification by goal programming and support vector machines Reviewed

    T.Asada, Y.B.Yun, H.Nakayama, T.Tanino

    Computational Management Sciences   Vol.1・No.3・211-230頁 ( 3 )   211 - 230   2004.10

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  • A generalized model for data envelopment analysis

    Y. B. Yun, H. Nakayama, T. Tanino

    European Journal of Operational Research   157 ( 1 )   87 - 105   2004.8

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    Data envelopment analysis (DEA) is a method to estimate a relative efficiency of decision making units (DMUs) performing similar tasks in a production system that consumes multiple inputs to produce multiple outputs. So far, a number of DEA models have been developed: The CCR model, the BCC model and the FDH model are well known as basic DEA models. These models based on the domination structure in primal form are characterized by how to determine the production possibility set from a viewpoint of dual form
    the convex cone, the convex hull and the free disposable hull for the observed data, respectively. In this study, we suggest a model called generalized DEA (GDEA) model, which can treat the above stated basic DEA models in a unified way. In addition, by establishing the theoretical properties on relationships among the GDEA model and those DEA models, we prove that the GDEA model makes it possible to calculate the efficiency of DMU incorporating various preference structures of decision makers. Furthermore, we propose a dual approach to GDEA, GDEA D and also show that GDEAD can reveal domination relations among all DMUs. © 2003 Elsevier B.V. All rights reserved.

    DOI: 10.1016/S0377-2217(03)00140-1

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  • A generalized model for data envelopment analysis

    YB Yun, H Nakayama, T Tanino

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   157 ( 1 )   87 - 105   2004.8

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    Data envelopment analysis (DEA) is a method to estimate a relative efficiency of decision making units (DMUs) performing similar tasks in a production system that consumes multiple inputs to produce multiple outputs. So far, a number of DEA models have been developed: The CCR model, the BCC model and the FDH model are well known as basic DEA models. These models based on the domination structure in primal form are characterized by how to determine the production possibility set from a viewpoint of dual form; the convex cone, the convex hull and the free disposable hull for the observed data, respectively.
    In this study, we suggest a model called generalized DEA (GDEA) model, which can treat the above stated basic DEA models in a unified way. In addition, by establishing the theoretical properties on relationships among the GDEA model and those DEA models, we prove that the GDEA model makes it possible to calculate the efficiency of DMU incorporating various preference structures of decision makers. Furthermore, we propose a dual approach to GDEA, GDEAD and also show that GDEAD can reveal domination relations among all DMUs. (C) 2003 Elsevier B.V. All rights reserved.

    DOI: 10.1016/S0377-2217(03)00140-1

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  • Total margin algorithms in support vector machines

    M Yoon, YB Yun, H Nakayama

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E87D ( 5 )   1223 - 1230   2004.5

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    Support vector algorithms try to maximize the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithms which consider the distance between all data points and the separating hyperplane. The method extends and modifies the existing algorithms. Experimental studies show that the total margin algorithms provide good performance comparing with the existing support vector algorithms.

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  • Total margin algorithms in support vector machines

    M Yoon, YB Yun, H Nakayama

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E87D ( 5 )   1223 - 1230   2004.5

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    Support vector algorithms try to maximize the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithms which consider the distance between all data points and the separating hyperplane. The method extends and modifies the existing algorithms. Experimental studies show that the total margin algorithms provide good performance comparing with the existing support vector algorithms.

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  • Fitness evaluation using generalized data envelopment analysis in MOGA

    Y Yun, H Nakayama, M Arakawa

    CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2   Vol.1   464 - 471   2004

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    Most of practical problems are formulated as multiobjective optimization problems (MOP) so as to meet the diversified demands of a decision maker. Usually, there is a tradeoff relation among objective functions, and thus there does not necessarily exist the solution that optimizes all objective functions simultaneously in MOP. Therefore, Pareto optimal solution is used as a definition of solution to MOP. Recently, evolutionary algorithms, for example, genetic algorithms, have been developed remarkably in order to obtain approximate solutions to optimization problems. Particularly, multi-objective genetic algorithms (MOGA) have been developed for generating Pareto optimal solutions. However, there are two problems in MOGA: how to assign the fitness to individuals, and how to keep the diversification of individuals. Many of existing MOGAs have made an effort in order to overcome these problems, and so does this paper. First, this paper suggests a fitness function in MOGA using generalized data envelopment analysis (GDEA) which was suggested for evaluating the relative efficiency of individuals under several items of assessment in management science. It is shown that the GDEA method can approximate Pareto optimal solutions more effectively and faster than the ranking method which is mostly used in MOGA, and generate well-distributed Pareto optimal solutions. Furthermore, this paper suggests the aspiration-level based GDEA method to generate the most interesting part (not the whole of Pareto optimal solutions) to an aspiration level of decision maker for choosing a final solution from many Pareto optimal solutions. Finally, this paper illustrates the effectiveness of the methods using GDEA through several numerical examples.

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  • Using support vector machines in multi-objective optimization

    YB Yun, H Nakayama, M Arakawa

    2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS   223 - 228   2004

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    In many practical engineering design problems, the form of objective functions is not given explicitly in terms of design variables. Given the value of design variables, under this circumstance, the values of objective functions are obtained by real/computational experiments such as structural analysis, fluid-mechanical analysis, thermodynamic analysis, and so on. Since these experiments are considerably expensive and also time consuming, thus it is actually almost impossible to find the exact solution to those problems by using conventional optimization methods. Recently, approximation methods using computational intelligence, for example, evolutionary algorithms and neural networks have been developed remarkably. Even those algorithms need a tremendous number of experiments to obtain an approximate solution. Furthermore, most engineering design problems should be formulated as multi-objective optimization problems so as to meet the diversified demands of designer. This paper suggests applying the support vector machines (SVM) in order to make the number of experiments for finding the solution of problem with multi-objective functions as few as possible. It is shown that the proposed method can approximate Pareto frontiers in multi-objective optimization problems effectively by employing support vectors in SVM. Finally, the effectiveness of our method will be illustrated through numerical examples.

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  • Support vector machines using MOP/GP techniques

    Hirotaka Nakayama, Yeboon Yun, T. Asada, Min yoon

    European Congress on Computational Methods in Applied Sciences and Engineering (edited by P. Neittaanmaki, T. Rossi, S. Korotov, E. Onate, J. Periaux & D. Knorzer)   2004

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  • A family of support vector machines using MOP/GP

    Hirotaka Nakayama, Yeboon Yun, Takeshi Asada, Min Yoon

    Proceedings of The 17th International Conference on Multiple Criteria Decision Making   2004

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  • Multi-objective optimization technique using computational intelligence

    Yeboon Yun, Hirotaka Nakayama, Masao Arakawa, Shiraki Wataru, Hiroshi Ishikawa

    Proceedings of International conference on Intelligent Mechatronics and Automation   2004

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  • Generation of Pareto frontiers using support vector machine

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    Proceedings of The 17th International Conference on Multiple Criteria Decision Making   2004

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  • A family of support vector machines using MOP/GP

    Hirotaka Nakayama, Yeboon Yun, Takeshi Asada, Min Yoon

    Proceedings of The 17th International Conference on Multiple Criteria Decision Making   2004

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  • Multi-objective optimization technique using computational intelligence

    Yeboon Yun, Hirotaka Nakayama, Masao Arakawa, Shiraki Wataru, Hiroshi Ishikawa

    Proceedings of International conference on Intelligent Mechatronics and Automation   2004

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  • Fitness evaluation using generalized data envelopment analysis in MOGA

    Y Yun, H Nakayama, M Arakawa

    CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2   Vol.1   464 - 471   2004

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    Language:English   Publisher:IEEE  

    Most of practical problems are formulated as multiobjective optimization problems (MOP) so as to meet the diversified demands of a decision maker. Usually, there is a tradeoff relation among objective functions, and thus there does not necessarily exist the solution that optimizes all objective functions simultaneously in MOP. Therefore, Pareto optimal solution is used as a definition of solution to MOP. Recently, evolutionary algorithms, for example, genetic algorithms, have been developed remarkably in order to obtain approximate solutions to optimization problems. Particularly, multi-objective genetic algorithms (MOGA) have been developed for generating Pareto optimal solutions. However, there are two problems in MOGA: how to assign the fitness to individuals, and how to keep the diversification of individuals. Many of existing MOGAs have made an effort in order to overcome these problems, and so does this paper. First, this paper suggests a fitness function in MOGA using generalized data envelopment analysis (GDEA) which was suggested for evaluating the relative efficiency of individuals under several items of assessment in management science. It is shown that the GDEA method can approximate Pareto optimal solutions more effectively and faster than the ranking method which is mostly used in MOGA, and generate well-distributed Pareto optimal solutions. Furthermore, this paper suggests the aspiration-level based GDEA method to generate the most interesting part (not the whole of Pareto optimal solutions) to an aspiration level of decision maker for choosing a final solution from many Pareto optimal solutions. Finally, this paper illustrates the effectiveness of the methods using GDEA through several numerical examples.

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  • Multi-objective optimization by using machine learning algorithm and evolutionary algorithm

    Yeboon Yun, Hirotaka Nakayama, Masao Arakawa, Shiraki Wataru, Hiroshi Ishikawa

    Proceedings of The third China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical System   2004

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  • Prediction of business failure by total margin support vector machines

    Yeboon Yun, Min Yoon, Hirotaka Nakayama, Wataru Shiraki

    Knowledge-Based Intelligent Information & Engineering Systems (edited by V. Palade, R.J. Howlett & L. Jain), Springer-Verlag   Vol.1   2004

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  • Generation of Pareto frontiers using support vector machine

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    Proceedings of The 17th International Conference on Multiple Criteria Decision Making   2004

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  • Multi-objective optimization by using machine learning algorithm and evolutionary algorithm

    Yeboon Yun, Hirotaka Nakayama, Masao Arakawa, Shiraki Wataru, Hiroshi Ishikawa

    Proceedings of The third China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical System   2004

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  • Prediction of business failure by total margin support vector machines

    Y Yun, M Yoon, H Nakayama, W Shiraki

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS   3213   441 - 448   2004

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    The analysis and management of business failure has been recognized to be important in the area of financial management in the evaluation of firms' performance and the assessment of their viability. To this end, effective failure-prediction models are needed. This paper describes a new approach to prediction of business failure using the total margin algorithm which is a kind of support vector machine. It will be shown that the proposed method can evaluate the risk of failure better than existing methods through some real data.

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  • Using support vector machines in multi-objective optimization,

    Yeboon Yun, Hirotaka Nakayama, Min Yoon

    Proceedings of International Joint Conference on Neural Networks   2004

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  • Support vector machines using MOP/GP techniques

    Hirotaka Nakayama, Yeboon Yun, T. Asada, Min yoon

    European Congress on Computational Methods in Applied Sciences and Engineering (edited by P. Neittaanmaki, T. Rossi, S. Korotov, E. Onate, J. Periaux & D. Knorzer)   2004

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  • Support vector machines controlling noise influence directly Reviewed

    Min Yoon, Yeboon Yun, Hirotaka Nakayama

    計測自動制御学会論文集   Vol.39・No.1・82-84頁 ( 1 )   82 - 84   2003.1

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  • Support Vector Machines Controlling Noise Influence Directly Reviewed

    YOON Min, NAKAYAMA Hirotaka, YUN Yeboon

    Vol.39・No.1・82-84頁 ( 1 )   82 - 84   2003.1

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  • A Role of total margin in support vector machines

    Min Yoon, Yeboon Yun, Hirotaka Nakayama

    Proc. of International Joint Conference on Neural Networks, IEEE and International Neural Network Society   2003

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  • A soft margin algorithm controlling tolerance directly

    M Yoon, H Nakayama, Y Yun

    MULTI-OBJECTIVE PROGRAMMING AND GOAL PROGRAMMING   281 - 287   2003

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    Generalization error bounds in Support Vector Machines are based on the minimum distance between training points and the separating hyperplane. The error of soft margin algorithm can be bounded by a target margin and some norms of the slack vector. In this paper, we propose a new method controlling allowable error and formulate considering the contamination by noise in data directly. The method can provide desirable separating hyperplanes easily by controlling a restricted slack parameter. Additionally, through an artificial numerical example, we compare the proposed method with a conventional soft margin algorithm.

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  • Goal programming approaches to support vector machines

    H Nakayama, Y Yun, T Asada, M Yoon

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS   2773   356 - 363   2003

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    Language:English   Publisher:SPRINGER-VERLAG BERLIN  

    Support vector machines (SVMs) are gaining much popularity as effective methods in machine learning. In pattern classification problems with two class sets, their basic idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. However, the idea of maximal margin separation is not quite new: in 1960's the multi-surface method (MSM) was suggested by Mangasarian. In 1980's, linear classifiers using goal programming were developed extensively. This paper considers SVMs from a viewpoint of goal programming, and proposes a new method based on the total margin instead of the shortest distance between learning data and separating hyperplane.

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  • A soft margin algorithm controlling tolerance directly

    M Yoon, H Nakayama, Y Yun

    MULTI-OBJECTIVE PROGRAMMING AND GOAL PROGRAMMING   281 - 287   2003

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    Generalization error bounds in Support Vector Machines are based on the minimum distance between training points and the separating hyperplane. The error of soft margin algorithm can be bounded by a target margin and some norms of the slack vector. In this paper, we propose a new method controlling allowable error and formulate considering the contamination by noise in data directly. The method can provide desirable separating hyperplanes easily by controlling a restricted slack parameter. Additionally, through an artificial numerical example, we compare the proposed method with a conventional soft margin algorithm.

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  • A Role of total margin in support vector machines

    Min Yoon, Yeboon Yun, Hirotaka Nakayama

    Proc. of International Joint Conference on Neural Networks, IEEE and International Neural Network Society   2003

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  • Goal programming approaches to support vector machines

    H Nakayama, Y Yun, T Asada, M Yoon

    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS   2773   356 - 363   2003

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    Language:English   Publisher:SPRINGER-VERLAG BERLIN  

    Support vector machines (SVMs) are gaining much popularity as effective methods in machine learning. In pattern classification problems with two class sets, their basic idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. However, the idea of maximal margin separation is not quite new: in 1960's the multi-surface method (MSM) was suggested by Mangasarian. In 1980's, linear classifiers using goal programming were developed extensively. This paper considers SVMs from a viewpoint of goal programming, and proposes a new method based on the total margin instead of the shortest distance between learning data and separating hyperplane.

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  • Support vector classification considering total margin

    Yeboon Yun, Min Yoon, Takeshi Asada, Hirotaka Nakayama

    Proceedings of IASTED International Conference on Artificial Intelligence and Soft Computing   2003

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  • Metaheuristics, Generalized DEA and aspiration-based method for multi-objective optimization

    Yeboon Yun, Hirotaka Nakayama, Masao Arakawa, Hiroshi Ishikawa

    Proceedings of The fifth Metaheuristics International Conference   2003

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  • Support vector classification considering total margin

    Yeboon Yun, Min Yoon, Takeshi Asada, Hirotaka Nakayama

    Proceedings of IASTED International Conference on Artificial Intelligence and Soft Computing   2003

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  • Metaheuristics, Generalized DEA and aspiration-based method for multi-objective optimization

    Yeboon Yun, Hirotaka Nakayama, Masao Arakawa, Hiroshi Ishikawa

    Proceedings of The fifth Metaheuristics International Conference   2003

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  • Multiple criteria decision making by generalized DEA introducing aspiration level method

    Yeboon Yun, Hirotaka Nakamaya, Masao Arakawa, Hiroshi Ishikawa

    Proceedings of The second China-Japan-Korea Joint Symposium on Optimization on Structural and Mechanical System   2002

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  • Data Envelopment Analysis in Multicriteria Decision Making

    NAKAYAMA H.

    Multiple Criteria Optimization : State of the Art Annotated Bibliographic Surveys   333 - 368   2002

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  • Multiple criteria decision making by generalized DEA introducing aspiration level method

    Yeboon Yun, Hirotaka Nakamaya, Masao Arakawa, Hiroshi Ishikawa

    Proceedings of The second China-Japan-Korea Joint Symposium on Optimization on Structural and Mechanical System   2002

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  • Data Envelopment Analysis in Multicriteria Decision Making

    Hirotaka Nakamaya, Masao Arakawa, Yeboon Yun

    Multiple Criteria Optimization, State of Art, Annotated Bibliographic Survey   333 - 368   2002

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  • Generation of efficient frontiers in multi-objective optimization problems by generalized data envelopment analysis

    Y. B. Yun, H. Nakayama, T. Tanino, M. Arakawa

    European Journal of Operational Research   129 ( 3 )   586 - 595   2001.3

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    In many practical problems such as engineering design problems, criteria functions cannot be given explicitly in terms of design variables. Under this circumstance, values of criteria functions for given values of design variables are usually obtained by some analyses such as structural analysis, thermodynamical analysis or fluid mechanical analysis. These analyses require considerably much computation time. Therefore, it is not unrealistic to apply existing interactive optimization methods to those problems. On the other hand, there have been many trials using genetic algorithms (GA) for generating efficient frontiers in multi-objective optimization problems. This approach is effective in problems with two or three objective functions. However, these methods cannot usually provide a good approximation to the exact efficient frontiers within a small number of generations in spite of our time limitation. The present paper proposes a method combining generalized data envelopment analysis (GDEA) and GA for generating efficient frontiers in multi-objective optimization problems. GDEA removes dominated design alternatives faster than methods based on only GA. The proposed method can yield desirable efficient frontiers even in non-convex problems as well as convex problems. The effectiveness of the proposed method will be shown through several numerical examples.

    DOI: 10.1016/S0377-2217(99)00469-5

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  • Generation of efficient frontiers in multi-objective optimization problems by generalized data envelopment analysis

    YB Yun, H Nakayama, T Tanino, M Arakawa

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   129 ( 3 )   586 - 595   2001.3

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    Language:English   Publisher:ELSEVIER SCIENCE BV  

    In many practical problems such as engineering design problems, criteria functions cannot be given explicitly in terms of design variables. Under this circumstance, values of criteria functions for given values of design variables are usually obtained by some analyses such as structural analysis, thermodynamical analysis or fluid mechanical analysis. These analyses require considerably much computation time. Therefore, it is not unrealistic to apply existing interactive optimization methods to those problems. On the other hand, there have been many trials using genetic algorithms (GA) for generating efficient frontiers in multi-objective optimization problems. This approach is effective in problems with two or three objective functions. However, these methods cannot usually provide a good approximation to the exact efficient frontiers within a small number of generations in spite of our time limitation. The present paper proposes a method combining generalized data envelopment analysis (GDEA) and GA for generating efficient frontiers in multiobjective optimization problems. GDEA removes dominated design alternatives faster than methods based on only GA. The proposed method can yield desirable efficient frontiers even in non-convex problems as well as convex problems. The effectiveness of the proposed method will be shown through several numerical examples. (C) 2001 Elsevier Science B.V. All rights reserved.

    DOI: 10.1016/S0377-2217(99)00469-5

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  • Reading required characters in market of products by using data envelopment analysis

    Masao Arakawa, Hirotaka Nakayama, Yeboon Yun, Hiroshi Ishikawa

    Proceedings of Design Technical Conference ASME   2001

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  • Dual approach to generalized data envelopment analysis based on production possibility

    Yeboon Yun, Hirotaka Nakayama, Tetuzo Tanino

    Advances in Soft Computing:Multi-Objective Programming and Goal Programming:Recent Developments (edited by T. Traskalik & J. Michnik)   2001

  • Dual approach to generalized data envelopment analysis based on production possibility

    Yeboon Yun, Hirotaka Nakayama, Tetuzo Tanino

    Advances in Soft Computing:Multi-Objective Programming and Goal Programming:Recent Developments (edited by T. Traskalik & J. Michnik)   2001

  • Reading required characters in market of products by using data envelopment analysis

    Masao Arakawa, Hirotaka Nakayama, Yeboon Yun, Hiroshi Ishikawa

    Proceedings of Design Technical Conference ASME   2001

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  • Optimum design using radial basis function networks by adaptive range genetic algorithms (determination of radius in radial basis function networks)

    Masao Arakawa, Hirotaka Nakayama, Yeboon Yun, Hiroshi Ishikawa

    Proceedings of IEEE International Conference on Industrial Electronics, Control and Instrumentation;21st Century Technology and Industrial Opportunities   2000

  • Optimum design using radial basis function networks by adaptive range genetic algorithms (determination of radius in radial basis function networks)

    Masao Arakawa, Hirotaka Nakayama, Yeboon Yun, Hiroshi Ishikawa

    Proceedings of IEEE International Conference on Industrial Electronics, Control and Instrumentation;21st Century Technology and Industrial Opportunities   2000

  • 一般化包絡分析法と遺伝的アルゴリズムによる多目的最適化の一手法

    尹禮分, 中山弘隆, 谷野哲三, 荒川 雅生

    システム制御情報学会論文誌   13 ( 4 )   179 - 185   2000

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    In this paper, a method using generalized data envelopment analysis and genetic algorithms is proposed for finding efficient frontiers in multi-objective optimization problems. The proposed method can yield desirable efficient frontiers even in nonconvex cases. It will be proved that the proposed method overcomes shortcomings of existing methods through several numerical examples.

    DOI: 10.5687/iscie.13.4_179

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  • 一般化包絡分析法と遺伝的アルゴリズムによる多目的最適化の一手法

    尹禮分, 中山弘隆, 谷野哲三, 荒川 雅生

    システム制御情報学会論文誌   13 ( 4 )   179 - 185   2000

  • Generalized DEA for multiple criteria decision making

    Yeboon Yun, Hirotaka Nakayama, Masao Arakawa, Hiroshi Ishikawa

    Proceedings of Konan-IIASA Joint Workshop on Natural Environment Management and Applied Systems Analysis   2000

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  • On efficiency of Data Envelopment Analysis

    YB Yun, H Nakayama, T Tanino

    RESEARCH AND PRACTICE IN MULTIPLE CRITERIA DECISION MAKING   487   208 - 217   2000

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    In this paper, we suggest a new concept of "Value Free Efficiency" which does not introduce any value judgment for outputs and inputs. That is, similarly to the usual multiple criteria decision analysis, a Decision Making Unit (DMU) is defined to be efficient if there is no unit that consumes less inputs and produces more outputs than the DMU. In addition, we propose a generalized DEA model for estimating value free efficiency, ratio value efficiency proposed by Charnes, Cooper and Rhodes [4], and sum value efficiency proposed by Belton [2] and Belton and Vickers [3] as special cases. An illustrative example compares these concepts of efficiency.

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  • A Dual approach to generalized data envelopment analysis

    Yeboon Yun, Nakayama Hirotaka, Tetsuzo Tanino

    Proceedings of International Conference on 2000 INFORMS/KORMS-Information and Knowledge Management in the 21st century-   2000

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  • On efficiency of data envelopment analysis

    Yeboon Yun, Nakayama Hirotaka, Tetsuzo Tanino

    Lecture Notes in Economics and Mathematical Systems:Research and Practice in Multiple Criteria Decision Making (edited by Y.Y. Haimes & R.E. Steuer)   2000

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  • A Dual approach to generalized data envelopment analysis

    Yeboon Yun, Nakayama Hirotaka, Tetsuzo Tanino

    Proceedings of International Conference on 2000 INFORMS/KORMS-Information and Knowledge Management in the 21st century-   2000

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  • Generalized DEA for multiple criteria decision making

    Yeboon Yun, Hirotaka Nakayama, Masao Arakawa, Hiroshi Ishikawa

    Proceedings of Konan-IIASA Joint Workshop on Natural Environment Management and Applied Systems Analysis   2000

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  • 一般化包絡分析法への双対アプローチ

    尹 禮分, 中山 弘隆, 谷野 哲三

    計測自重制御学会論文集   36 ( 9 )   804 - 809   2000

  • 一般化包絡分析法への双対アプローチ

    尹 禮分, 中山 弘隆, 谷野 哲三

    計測自重制御学会論文集   36 ( 9 )   804 - 809   2000

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    Publisher:The Society of Instrument and Control Engineers  

    In DEA (Data Envelopment Analysis), CCR model, BCC model and FDH model are known as representative models incorporating preference structure of decision makers. These DEA models differ in production possibility set when considering dual problems. In this paper, we formulate dual problem (GDEA<sub><i>D</i></sub>) to generalized data envelopment analysis (GDEA) and define the concept of "α<sub><i>D</i></sub>-efficiency" for the problem (GDEA<sub><i>D</i></sub>). Furthermore, we establish theoretical properties on relationships between GDEA<sub><i>D</i></sub>, model and existing DEA models. Finally, through a numerical example, we show a dominant relation among decision making units with varying α in the problem (GDEA<sub><i>D</i></sub>) and interpret an implication of the optimal solution to the problem (GDEA<sub><i>D</i></sub>).

    DOI: 10.9746/sicetr1965.36.804

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  • 包絡分析法(DEA)モデルの一般化

    尹 禮分, 中山 弘隆, 谷野 哲三

    計測自動制御学会   35 ( 8 )   1813 - 1818   1999

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    Language:Japanese   Publisher:The Society of Instrument and Control Engineers  

    So far, there have been developed several kinds of DEA models, say, CCR, BCC, FDH and so on, for evaluating the relative efficiency of decision making units. This paper suggests a new model called a generalized DEA (GDEA) model, which can treat these existing DEA models in a unified way. Theoretical properties on relationships among these DEA models are given first. Next, the efficiencies for these DEA models are compared and clarified by a numerical example with real data.

    DOI: 10.9746/sicetr1965.35.1113

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  • 包絡分析法(DEA)モデルの一般化

    尹 禮分, 中山 弘隆, 谷野 哲三

    計測自動制御学会   35 ( 8 )   1813 - 1818   1999

  • Multiobjective symmetric duality with cone constraints

    DS Kim, YB Yun, WJ Lee

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   107 ( 3 )   686 - 691   1998.6

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    Language:English   Publisher:ELSEVIER SCIENCE BV  

    We formulate a pair of multiobjective symmetric dual programs for pseudo-invex functions and arbitrary cones. Our model is unifying the Wolfe vector symmetric dual and the Mond-Weir vector symmetric dual models. We establish the weak, strong, converse and self duality theorems for our pair of dual models. Nanda and Das' results (Optimization 28 (1994) 267; fur. J. Oper. Res. 88 (1996) 572) are obtained as special cases. (C) 1998 Elsevier Science B.V. All rights reserved.

    DOI: 10.1016/S0377-2217(97)00322-6

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  • Multiobjective symmetric duality with cone constraints

    DS Kim, YB Yun, WJ Lee

    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH   107 ( 3 )   686 - 691   1998.6

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    Language:English   Publisher:ELSEVIER SCIENCE BV  

    We formulate a pair of multiobjective symmetric dual programs for pseudo-invex functions and arbitrary cones. Our model is unifying the Wolfe vector symmetric dual and the Mond-Weir vector symmetric dual models. We establish the weak, strong, converse and self duality theorems for our pair of dual models. Nanda and Das' results (Optimization 28 (1994) 267; fur. J. Oper. Res. 88 (1996) 572) are obtained as special cases. (C) 1998 Elsevier Science B.V. All rights reserved.

    DOI: 10.1016/S0377-2217(97)00322-6

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  • Second-order symmetric and self duality in multiobjective programming

    DS Kim, YB Yun, H Kuk

    APPLIED MATHEMATICS LETTERS   10 ( 2 )   17 - 22   1997.3

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    Language:English   Publisher:PERGAMON-ELSEVIER SCIENCE LTD  

    We suggest the second-order symmetric and self dual programs in multiobjective nonlinear programming. For these second-order symmetric dual programs, we prove the weak, strong, and converse duality theorems under convexity and concavity conditions. Also, we prove the self duality theorem for these second-order self dual programs and illustrate its example.

    DOI: 10.1016/S0893-9659(97)00004-9

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  • Second-order symmetric and self duality in multiobjective programming

    DS Kim, YB Yun, H Kuk

    APPLIED MATHEMATICS LETTERS   10 ( 2 )   17 - 22   1997.3

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    Language:English   Publisher:PERGAMON-ELSEVIER SCIENCE LTD  

    We suggest the second-order symmetric and self dual programs in multiobjective nonlinear programming. For these second-order symmetric dual programs, we prove the weak, strong, and converse duality theorems under convexity and concavity conditions. Also, we prove the self duality theorem for these second-order self dual programs and illustrate its example.

    DOI: 10.1016/S0893-9659(97)00004-9

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Presentations

  • Proposal on Model for Predicting the Risk of Heatstroke by Machine Learning basedon Data Driven Approach

    尹 禮分, 馮 劍飛

    第28回関西大学先端科学技術シンポジウム  2024.1 

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    Venue:関西大学 千里山キャンパス  

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  • 中国のSNSからみる日本観光に対する需要変化とトレンド分析

    姜 子辰, 尹 禮分, 山本 雄平

    第28回関西大学先端科学技術シンポジウム  2024.1 

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    Venue:関西大学 千里山キャンパス  

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  • Extension of DEA Model based on Tchebyshev Distance for a Decision Making

    J.Su, Y.B.Yun, Y.Yamamoto

    第28回関西大学先端科学技術シンポジウム  2024.1 

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    Venue:関西大学 千里山キャンパス  

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  • On Applying a Graph Convolutional Neural Network to Genetic Algorithm in Constrained Optimization

    K.Fujita, Y.B.Yun, Y.Yamamoto

    第28回関西大学先端科学技術シンポジウム  2024.1 

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    Venue:関西大学 千里山キャンパス  

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  • 機械学習による複数の揚水井が密集した被圧観測井水位の将来予測

    森谷 将成, 尹 禮分, 楠見 晴重

    令和5年度土木学会全国大会  2023.9 

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    Venue:広島大学 東広島キャンパス  

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  • 機械学習および回帰分析併用による被圧帯水層の水源揚水井水位の将来予測

    大西望央, 尹 禮分, 楠見晴重

    令和5年度土木学会全国大会  2023.9 

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    Venue:広島大学 東広島キャンパス  

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  • 機械学習による地質帯を考慮した NATM トンネル切羽面の岩盤評価

    長江 謙吾, K.D. Halim, 尹 禮分, 楠見 晴重, 西尾 彰宣

    令和5年度土木学会全国大会  2023.9 

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    Venue:広島大学 東広島キャンパス  

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  • 深層学習による NATM トンネルにおける切羽面の岩盤評価

    中田 真成, 尹 禮分, 楠見 晴重

    令和5年度土木学会全国大会  2023.9 

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    Venue:広島大学 東広島キャンパス  

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  • インフラ構造物建設時の補強度合決定問題への機械学習の適用

    巽 啓司, 宮原 春久, 増井 遼太, 尹 禮分

    公益社団法人計測自動制御学会  2023.3 

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    Venue:島根県松江テルサ  

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  • リカレントニューラルネットワークを用いた大型構造物の補強度合の決定

    宮原 春久, 増井 遼太, 巽 啓司, 尹 禮分

    計測自動制御学会・情報部門学術講演会2022  2022.11 

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    Venue:近畿大学東大阪キャンパス  

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  • 満足度と制約条件の難易度に基づく多目的遺伝的アルゴリズムにおける個体評価法の提案

    藤田 耕平, 尹 禮分, 尹 敏

    計測自動制御学会システム・情報部門学術講演会2022  2022.11 

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    Venue:近畿大学東大阪キャンパス  

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  • 機械学習による密集した揚水井の揚水に伴う観測井水位の将来予測

    澤田 雅言, 尹 禮分, 楠見 晴重

    令和4年度土木学会全国大会第77回年次学術講演会  2022.9 

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    Venue:京都大学  

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  • 機械学習による NATM 工法トンネルにおける切羽面の岩盤評価 機械学習による NATM 工法トンネルにおける切羽面の岩盤評価

    K. D. Halim, 尹 禮分, 楠見 晴重

    令和4年度土木学会全国大会第77回年次学術講演会  2022.9 

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    Venue:京都大学  

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  • 深層学習による NATM トンネルにおける切羽面の岩盤不連続面に関する定量的評価法

    中田真成, 榎並大希, 尹 禮分, 楠見晴重

    2022年度土木学会関西支部年次学術講演会  2022.5 

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    Venue:関西大学  

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  • 機械学習による京都盆地における被圧帯水層の水源揚水井水位の将来予測

    大西望央, 平川将寛, 尹 禮分, 楠見晴重

    2022年度土木学会関西支部年次学術講演会  2022.5 

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    Venue:関西大学  

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  • 達成度に基づいた多目的遺伝的アルゴリズムの個体評価法に関する研究

    曲 錚, 路 暢, 尹 禮分, 尹 敏

    2021.11 

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  • Application of Machine Learning to the Prediction WBGT

    C. Lu, Y.B. Yun, M. Yoon

    2021.9 

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  • Prediction of Wet Bulb Globe Temperature using Machine Learning

    C. Lu, Y.B. Yun, M. Yoon

    2021.7 

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  • 観測データ間の関係を考慮した決定木による構造物の補強度合の判定

    増井 遼太, 辻岡 竣祐, 巽 啓司, 楠木 祥文, 尹 禮分

    2021.3 

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  • Analysis for Diabetes using Graphical lasso

    Y. Kim, Y.B. Yun, M. Yoon, H. Nakayama

    2019.8 

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  • On Selecting Hyper-Parameters in RBF Networks

    S. Yoshida, Y.B. Yun, H. Nakayama, M. Yoon

    2019.8 

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  • Multi-objective model predictive control and its applications

    Yeboon Yun,, Hirotaka Nakayama, Min Yoon

    2018.8 

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  • Model Predictive Control with Multiple Objectives

    Y.E. Yun, H. Nakayama

    2018.8 

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    Venue:Cheng Shiu University  

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  • On Disposal planning of debris and waste for large-scale disasters

    Yeboon Yun, Takamasa Akiyama, Hiroaki Inokuchi, Min Yoon

    2017.7 

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  • バギングとブースティングの併用によるSVMにおける効率的な学習法の提案

    尹禮分, 中山弘隆

    2014.12 

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  • ラフ集合を用いた交通機関選択モデルの提案

    尹禮分, 秋山孝正, 井ノ口弘昭, 中山弘隆

    2014.11 

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  • ラフ集合を用いたコミュニティバス需要推計モデル

    井ノ口弘昭, 秋山孝正, 尹禮分

    2014.9 

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  • サポートベクトル回帰による複数応答曲面の連結

    西口純也, 綛田長生, 尹禮分, 中山弘隆, 荒川雅生, 北山哲士

    2014.5 

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  • On Tuning Parameters in SVM/RBFN for Regression Problems

    Y.B.Yun, H.Nakayama, M.Yoon

    Proceedings of the 8th China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems  2014.5 

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    Venue:Gyeongju, Korea  

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  • Sequential Learning with Support Vector Machines

    Y.B.Yun, H.Nakayama, M.Yoon

    Proceedings of International Conference on Optimization: Techniques and Applications (ICOTA9)  2013.12 

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    Venue:Taipei, Taiwan  

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  • 社会基盤施設の評価におけるデータ包絡分析法の活用

    尹禮分, 中山弘隆, 尹敏

    第29回 ファジィ システム シンポジウム  2013.9 

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  • On the use of data envelopment analysis in evaluation of sewerage systems

    Y.B.Yun, H.Nakayama, M.Yoon

    Proceedings of the 5th International Conference on Optimization and Control with Applications  2012.12 

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    Venue:Beijing, China  

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  • Evaluation of sewerage systems using data envelopment analysis

    Y.B.Yun, T.Iida, K.Furukawa, H.Nakayama

    Proceedings of The 6th International Conference on Soft Computing and Intelligent Systems/The 13th International Symposium on Advanced Intelligent Systems  2012.11 

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    Venue:Kobe, Japan  

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  • Parameter tuning of large scale support vector machines using ensemble learning with applications to imbalnced data sets

    H.Nakayama, Y.B.Yun, Y.Uno

    Proceedings of IEEE Systems,Man,Cybernetics Conference  2012.10 

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    Venue:Seoul, Korea  

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  • Evaluation of sewerage systems by using data envelopment analysis

    Y.B. Yun

    Proceedings of The 7th International Symposium in Science and Technology  2012.8 

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    Venue:Penang, Malaysia  

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  • 多目的最適化問題における満足化トレードオフ法を用いたロバスト設計

    田中聡, 中山弘隆, 尹禮分

    第54回自動制御連合講演会  2011.11 

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  • Evolutionary multi- objective optimization using expected improvement and generalized DEA

    Y.B.Yun, H.Nakayama, M.Yoon

    2011 IEEE Systems,Man,Cybernetics Conference  2011.10 

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  • Utilizing expected improvement and generalized data envelopment analysis in multi-objective genetic algorithms

    Y.B.Yun, H.Nakayama, M.Yoon

    The 21st International Conference on Multiple Criteria Decision Making  2011.6 

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  • 能動学習型最適化を用いたビル空調制御

    西口純也, 近田智洋, 中山弘隆, 尹禮分, 荒川雅生

    第55回システム制御情報学会研究発表講演会  2011.5 

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  • An Efficient learning method in support vector regression for large-scale data set with outliers

    R.Suzuki, H.Nakayama, Y.B.Yun

    International Conference on Optimization: Techniques and Applications (ICOTA08)  2010.12 

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  • Prediction on the Collapse of Sewerage Systems by using Support Vector Machines

    Y.B.Yun, R.Emori, T.Iida, K.Furukawa

    International Conference on Optimization: Techniques and Applications (ICOTA08)  2010.12 

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  • Support vector machines and its application to drain maintenance

    Y.B.Yun, R.Emori, T.Iida, K.Furukawa

    5th International Symposium in Science and Technology  2010.8 

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  • Modified Support Vector Regression in Outlier Detection

    J.Nishiguchi,C.Kaseda,H.Nakayama,M.Arakawa,Y.B.Yun

    World Congress on Computational Intelligence-IJCNN  2010.7 

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  • Combining Predetermined Models and SVM/RBFN for Regression Problems

    H.Nakayama,Y.B.Yun,Y.Uno

    The 6th China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems  2010.6 

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  • Particle Swarm Optimization for Multi-objective Optimization with Generalized Data Envelopment Analysis

    Y.B.Yun,H.Nakayama

    The 6th China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems  2010.6 

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  • 大規模データに対するサポートベクター回帰における異常値検出

    鈴木僚, 中山弘隆, 伊賀和博, 稲葉庸介, 尹禮分

    第54回システム制御情報学会研究発表講演会(SCI'10)  2010.5 

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  • 多目的Particle Swarm OptimizationにおけるGDEAの適用

    尹 禮分,中山弘隆

    第52回自動制御連合講演会  2009.11 

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  • Multiobjective Robust Optimization

    H.Nakayama,Y.B.Yun,K.Iga, Y.Inaba

    The 22th Workshop on Complex Systems Modeling  2009.9 

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  • Particle Swarm Optimization for Multi-Objective Optimization

    Y.B.Yun H.Nakayama

    The 22th Workshop on Complex Systems Modeling  2009.9 

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  • Sequential approximate multi-objective optimization using aspiration level approach and expected improvement

    Y.B.Yun,H.Nakayama,M.Yoon

    Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis  2008.12 

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  • Multi-objective model predictive optimization using computational Intelligence

    H.Nakayama,Y.B.Yun

    IFIP World Computer Congress:Artificial Intelligence in Theory and Practice  2008.9 

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  • 包絡分析法を応用した砂防事業における優先順位設定手法の提案

    佐藤丈晴,尹禮分,古川浩平

    平成20年度砂防学会研究発表会  2008.5 

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  • サポートベクトル回帰を用いた実用的な外れ値検出方法

    第52回システム制御情報学会研究発表講演会  2008.5 

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  • Practical Approach to Outlier Detection Using Support Vector Regression

    15th International Conference on Neural Information Processing  2008 

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  • Multi-objective Model Predictive Optimization using Computational Intelligence

    Second IFIP International Conference on Artificial Intelligence in Theory and Practice (IFIP AI 2008)  2008 

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  • Multi-objective Model Predictive Optimization using Computational Intelligence

    Second IFIP International Conference on Artificial Intelligence in Theory and Practice (IFIP AI 2008)  2008 

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  • Sequential Approximate Multi-Objective Optimization using Aspiration Level Approach and Expected Improvement

    Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis  2008 

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  • Practical Approach to Outlier Detection Using Support Vector Regression

    15th International Conference on Neural Information Processing  2008 

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  • Sequential Approximate Multi-Objective Optimization using Aspiration Level Approach and Expected Improvement

    Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis  2008 

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  • 客観的斜面崩壊危険度評価を用いた事業優先順位設定手法の応用事例

    佐藤丈晴,尹禮分,古川浩平

    第46回日本地すべり学会研究発表会  2007.8 

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  • On sequential approximate multi-objective optimization

    H.Nakayama,Y.B.Yun

    The 21th Workshop on Complex Systems Modeling  2007.8 

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  • サポートベクターマシンによる対策工効果を考慮した斜面災害危険度の設定

    大石博之,小林央宜,尹禮分,田中浩一,中山弘隆,古川浩平

    平成19年度砂防学会研究発表会  2007.5 

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  • 種々のデータによる改定IP-OLDF,SVM,判別手法の比較研究

    新村秀一, 尹禮分

    日本オペレーションズリサーチ学会2007年春季OR研究発表会  2007.3 

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  • Combining aspiration level methods in multi-objective programming and sequential approximate optimization using computational Intelligence

    H.Nakayama,Y.B.Yun,M.Yoon

    IEEE First Symposium on Computational Intelligence in Multi-Criteria Decision Making  2007.3 

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  • 計算知能を用いたハイブリッド型多目的最適化法

    尹禮分,中山弘隆,尹敏

    第7回最適化シンポジウム講演会  2006.12 

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  • Regression by support vector machines and its applications to engineering design

    H.Nakayama,Y.B.Yun

    The Fourth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical System  2006.11 

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  • Multi-objective optimization based on aspiration levels and approximation of Pareto frontier

    尹 禮分

    The Fourth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical System  2006.11 

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  • Computational intelligence method in multi-objective optimization

    尹 禮分

    International Joint Conference: Society of Instrument and Control Engineers - Institute of Control,Automation and Systems Engineers  2006.10 

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  • 土砂災害防止施設の施工に関する客観的な優先順位設定手法の開発

    尹 禮分

    第45回日本地すべり学会研究発表会  2006.8 

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  • μ-ν-SVR and its applications to engineering problems

    尹 禮分

    The 20th Workshop on Complex Systems Modeling  2006.8 

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  • Multi-objective optimization with prediction and approximation

    尹 禮分

    The 20th Workshop on Complex Systems Modeling  2006.8 

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  • 包絡分析を応用した事業優先度設定事例

    尹 禮分

    第45回日本地すべり学会研究発表会  2006.8 

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  • A hybrid method for approximating Pareto frontier

    尹 禮分

    21st European Conference on Operational Research  2006.7 

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  • Support vector regression based on goal programming and multi-objective programming

    尹 禮分

    World Congress on Computational Intelligence-IJCNN  2006.7 

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  • MOP/GP approaches to support vector regression and their applications to engineering design

    尹 禮分

    The 7th International Conference on Multi-Objective Programming and Goal Programming  2006.6 

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  • Fast approximation of Pareto frontier using computational intelligence

    尹 禮分

    The 18th International Conference on Multiple Criteria Decision Making  2006.6 

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  • SVMを用いた土砂災害発生限界基準線の設定に関する研究

    尹 禮分

    土木学会2005年中国支部研究発表会  2005.11 

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  • Genetic algorithm using GDEA in multi-objective optimization problems

    尹 禮分

    The Sixth Metaheuristics International Conference  2005.8 

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  • Generation of Pareto frontier using computational intelligence

    尹 禮分

    22nd IFIP TC 7 Conference on System Modeling and Optimization  2005.7 

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  • 計算知能を用いる多目的近似最適化手法

    尹 禮分

    第6回最適化シンポジウム  2004.11 

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  • Multi-objective optimization by using machine learning algorithm and evolutionary algorithm

    尹 禮分

    The Third China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical System  2004.11 

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  • Multi-objective optimization technique using computational intelligence

    尹 禮分

    International Conference on Intelligent Mechatronics and Automation  2004.8 

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  • A Family of support vector machines using MOP/GP

    尹 禮分

    The 17th International Conference on Multiple Criteria Decision Making  2004.8 

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  • Generation of Pareto frontiers using support vector machine

    尹 禮分

    The 17th International Conference on Multiple Criteria Decision Making  2004.8 

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  • Using support vector machines in multi-objective optimization

    尹 禮分

    International Joint Conference on Neural Networks  2004.7 

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  • Support vector machines using MOP/GP techniques

    尹 禮分

    European Congress on Computational Methods in Applied Sciences and Engineering  2004.7 

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  • Fitness evaluation using generalized data envelopment analysis in MOGA

    尹 禮分

    IEEE Congress on Evolutionary Computation  2004.6 

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  • 一般化包絡分析法と希求水準法を融合した多目的最適化手法

    尹 禮分

    第48回システム制御情報学会研究発表講演会(SCI'04)  2004.5 

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  • SVMによる企業の倒産予測

    尹 禮分

    日本オペレーションズリサーチ学会2004年春季研究発表会  2004.3 

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  • Metaheuristics, generalized DEA and aspiration-based method for multi-objective optimization

    尹 禮分

    The Fifth Metaheuristics International Conference  2003.8 

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  • A soft margin algorithm controlling tolerance directly

    尹 禮分

    Multi-Objective Programming and Goal Programming: Theory and Applications  2003.7 

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  • A role of total margin in support vector machines

    尹 禮分

    International Joint Conference on Neural Networks  2003.7 

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  • Support vector classification considering total margin

    尹 禮分

    IASTED International Conference on Artificial Intelligence and Soft Computing  2003.7 

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  • Multiple criteria decision making by generalized DEA introducing aspiration level method

    尹 禮分

    The Second China-Japan-Korea Joint Symposium on Optimization on Structural and Mechanical System  2002.11 

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  • Multi-objective optimization by using generalized DEA

    尹 禮分

    IFORS 16th Triennial Conference  2002.7 

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  • A generalized model for data envelopment analysis

    尹 禮分

    IFORS 16th Triennial Conference  2002.7 

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  • On evaluation of efficiency of local government finance by using DEA

    尹 禮分

    The 16th Workshop on Complex Systems Modeling  2002.7 

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  • Generalized data envelopment analysis in engineering design problem

    尹 禮分

    The Fourth International Conference on Multi-Objective Programming and Goal Programming: Theory and Applications  2002.6 

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  • Adaption of searching range of genetic algorithms for multi-objective optimization with data envelopment analysis

    尹 禮分

    The Fourth International Conference on Multi-Objective Programming and Goal Programming: Theory and Applications  2002.6 

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  • 一般包絡分析法による工学設計

    尹 禮分

    第11回設計工学システム部門講演会  2001.11 

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  • Decision making by using generalized data envelopment analysis and aspiration level method

    尹 禮分

    Korea-Vietnam Joint Seminar on Mathematical Optimization Theory and Applications  2001.11 

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  • 実数値GAによる多目的最適化

    尹 禮分

    第14回計算力学講演会  2001.11 

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  • データ包絡分析法を用いたトレンド分析法の開発

    尹 禮分

    第11回設計工学システム部門講演会  2001.11 

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  • Reading required characters in market of products by using data envelopment analysis

    尹 禮分

    ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference  2001.9 

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  • 多基準意思決定において一般化包絡分析法

    尹 禮分

    第6回日本計算工学会計算工学会講演会  2001.5 

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  • データ包絡分析法を用いた技術指標のトレンド分析

    尹 禮分

    第6回日本計算工学会計算工学会講演会  2001.5 

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  • 多目的最適設計のための領域適応型遺伝的アルゴリズムの開発

    尹 禮分

    日本機械学会機構論  2000.11 

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  • 多目的最適化におけるGDEAへの希求水準アプローチ

    尹 禮分

    日本応用数理学会2000年度年会  2000.10 

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  • Optimum design using radial basis function networks by adaptive range genetic algorithms (determination of radius in radial basis function networks)

    尹 禮分

    26th IEEE International Conference on Industrial Electronics,Control and Instrumentation  2000.10 

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  • Generalized DEA for multiple criteria decision making

    尹 禮分

    Konan-IIASA Joint Workshop on Natural Environment Management and Applied Systems Analysis  2000.9 

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  • Aspiration level approach to multiobjective optimization method using generalized data envelopment analysis

    尹 禮分

    INFORMS Fall 2000 Meetings: Integrating Theory and Applications  2000.6 

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  • A dual approach to generalized data envelopment analysis

    尹 禮分

    International Conference on 2000 INFORMS/KORMS  2000.6 

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  • Generalized data envelopment analysis and it's application to multi-objective optimization

    尹 禮分

    The First China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical System  1999.11 

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  • A multi-objective optimization method combining generalized data envelopment analysis and genetic algorithms

    尹 禮分

    IEEE Systems,Man,Cybernetics Conference  1999.10 

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  • 一般化包絡分析法と遺伝的アルゴリズムによる多目的最適化問題の一手法

    尹禮分 中山弘隆 谷野哲三 荒川雅生

    日本オペレーションズリサーチ学会1999年秋季研究発表会  1999.9 

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  • 包絡分析法(DEA)モデルの一般化

    尹禮分 中山 弘隆 谷野 哲三

    日本オペレーションズリサーチ学会1999年春季研究発表会  1999.3 

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Industrial property rights

  • 点検業務に利用可能な評点式データシートに基づく健全性評価システム

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    Date applied:2007.2

    Patent/Registration no:4152423 

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  • 危険度評価システム

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    Date applied:2006.2

    Patent/Registration no:4817363 

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  • 防災事業計画支援システム

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    Date applied:2006.2

    Patent/Registration no:3975407 

    登録番号:特許第3975407

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  • 構造物補修施工計画支援システム

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    Date applied:2005.11

    Patent/Registration no:3975406 

    公開番号:特開2007-140608 / 登録番号:特許第3975406

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  • 防災事業計画支援システムとその方法

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    Date applied:2004.6

    Patent/Registration no:3674707 

    登録番号:特許第3674707

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Works

  • 事業優先度設定に関する研究

    2007

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  • 事業優先度設定に関する研究

    2007

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  • 最適制御に関する研究

    2006

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  • 最適制御に関する研究

    2006

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  • 多目的最適化のアルゴリズムの実務的な応用に関する研究

    2005

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  • 多目的最適化のアルゴリズムの実務的な応用に関する研究

    2005

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Research Projects

  • Proposal of a combination of sustainable urban transport policies using a multi-agent model

    Grant number:21K04307  2021.4 - 2024.3

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

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    Grant amount:\3900000 ( Direct Cost: \3000000 、 Indirect Cost:\900000 )

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  • Study on Effective Learning on Multi-objective Sequential Optimization and its Applications

    Grant number:16K01269  2016.4 - 2020.3

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

    YUN YEBOON

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    Grant amount:\4680000 ( Direct Cost: \3600000 、 Indirect Cost:\1080000 )

    In optimization with high cost objective function, an approximate function is used as a surrogate. For generating an approximate function based on some sample points, machine learning such as Radial basis networks and Support vector machines is effective. The precision of approximate functions depends on hyper-parameters used in basis and kernel functions, which is deeply related to learning. The new methods of meta-learning in SVM and RBF networks were proposed with the aim of generating an approximate function of high accuracy with small number of function evaluations. Furthermore, for multi-objective optimal control problems based on meta-model under a dynamic environment, this study suggested the method of combining machine learning methods and predetermined linear model in order to construct more accurate and stable model prediction, Finally, the effectiveness of the proposed methods in this research was validated through some numerical examples and engineering design problems.

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  • Evaluation of Infrastructure Facilities Using Machine Learning

    Grant number:23710185  2011 - 2012

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

    YUN Yeboon

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    Grant amount:\4680000 ( Direct Cost: \3600000 、 Indirect Cost:\1080000 )

    In this research,we suggest a method using support vector machines (SVM) to evaluate objectively and effectively sewerage systems.Utilizing the characteristics of SVM,we elicit relevant attributes (factors) to collapse of roads from basic data and inspection for the sewerage systems in which road subsidence has occurred due to damaged sewer pipes.Calculating latent risk degree of each sewerage system,we decide a priority for maintaining preventively,properly and efficiently.Finally,the effectiveness of the proposed method will be investigated through some real inspection data.

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  • 計算知能による近似最適化法とその応用に関する研究

    Grant number:18710134  2006 - 2007

    日本学術振興会  科学研究費助成事業  若手研究(B)

    尹 禮分

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    Grant amount:\3500000 ( Direct Cost: \3500000 )

    工学設計などの実際問題では、目的関数が設計変数の陽な関数として与えられないことが多く、構造解析、流体解析、熱解析等の解析計算や実際の模型を作ってはじめて目的関数の値が与えられることが多い。このような問題においては必要な解析や模型試作の回数を出来る限り少なくして、かつ出来る限り最適に近い解を得ることが望まれている。そこで、応答曲面法をさらに発展させ、複雑な形状を持つ目的関数にも適用可能にするためにRBFネットワークやサポートベクターマシン等の計算知能の技法を用いて応答曲面を近似しながら、同時に進化型計算によって大局的な解を求めるという近似最適化の手法を開発した。さらに、高精度の近似曲面を得るために、どのような点を追加すればよいのかが非常に大事であるが、これまでの経験に基づく方法による試行錯誤的な選定法が主であった。本研究では、追加される点をどのように選定するかを、理論的な面からアプローチした上、シミュレーションなどによる分析を行い、さらに、多目的最適化問題へ拡張し、実用レベルでの活用ができるよう、これまで得られた研究成果のもとで総合的なハイブリッド型最適化システムを構築した。

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  • 社会基盤システムのLive Designのための避難シミュレーションシステムの開発に関する研究

    2005 - 2007

    International Joint Research Projects 

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    Grant type:Competitive

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  • 社会基盤システムのLive Designのための避難シミュレーションシステムの開発に関する研究

    2005 - 2007

    国際共同研究 

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    Grant type:Competitive

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  • 統計的機械学習理論を用いるシステムの信頼性評価法とその応用に関する研究

    Grant number:16710116  2004 - 2005

    日本学術振興会  科学研究費助成事業  若手研究(B)

    尹 禮分

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    Grant amount:\3800000 ( Direct Cost: \3800000 )

    多くの実際問題は相競合する目的関数をもつ多目的最適化問題として定式化される。多目的最適化問題ではすべての目的関数を同時に最適化する解は必ずしも存在せず、そのためパレート最適解が導入される。通常パレート最適解はたくさん存在し、その中から目的関数の間にバランスの取れた一つの最終解を選び出すことになる。しかし、工学設計などの実問題では種々の評価関数の形が陽にはわからず、構造解析、熱解析、流体解析等の数値解析によってはじめて設計変数の各値に対する評価関数値が定められることが多い。このような数値解析には多大の計算時間やコストがかかるため、満足のいく解を得るまでに必要とする解析回数は多くなり、従来からよく用いられた最適化手法の適用は現実的に困難である。したがって、最適設計などの問題においてはパレート最適解を求める際、要求される解析数を減らすことが非常に大事な課題である。一方、多点探索ができる進化型アルゴリズムを用いてパレート最適解の全体を生成する手法に関する研究が盛んになるが、得られた解の多様性の維持や最適性の評価法の解決すべき課題がある。
    そこで、本年度の研究では一般化包絡分析法(以下、GDEAという)による近似パレート曲面の生成法を提案した。意思決定体の効率性を分析するために提案された手法であるGDEAと遺伝的アルゴリズムを融合することにより、より少ない解析数で、より精度のいい近似パレート曲面を生成することができるようになった。さらに、GDEAから得られる解の情報を活用することで、多様性を維持することができるようになった。さらに、様々な数値例を通じて、本研究で提案した手法の有効性について検証した。

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Devising educational methods

  • ・教科内容をまとめた資料や演習問題の解説資料をCEASに掲載し、毎週予習・復習できるようにした。 ・授業中の座席は指定席にした。その結果、授業雰囲気の改善もみられ、大人数の授業の場合でも学生一人一人の達成度が容易に把握できるようになった。 ・演習や実習を伴わない理論系教科でも理解を深めるため、演習問題をとく時間をなるべく多く設けて、その日の授業内容はその時間内に理解するようにした。 ・先週授業内容に対する小テストを実施することで、ある意味では強制的ですが、毎週復習するようにし、授業出席率も高くなる効果があった。 ・演習問題を解くときには学生同士での議論を許可し、お互い助け合うようにした。 ・また演習内容をレポートして提出してもらい(強制性を持たすため)、それをチェックすることで、学生の進捗状況を把握し、さらにそれをfeed backさせることで、学生自身も各自で自分の理解度合いをチェックすることができた。

Teaching materials

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Teaching method presentations

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Special notes on other educational activities

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