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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chen, Mu-ChenSubject: search.filters.subject.ICA

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Now showing 1 - 2 of 2
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    Item type:Item,
    Integrating independent component analysis and support vector machine for multivariate process monitoring
    (2010-08-01)
    Hsu, Chun-Chin
    ;
    Chen, Mu-Chen
    ;
    Chen, Long-Sheng
    ;
    運輸與物流管理系 註：原交通所+運管所
    ;
    Department of Transportation and Logistics Management
    This study aims to develop an intelligent algorithm by integrating the independent component analysis (ICA) and support vector machine (SVM) for monitoring multivariate processes. For developing a successful SVM-based fault detector, the first step is feature extraction. In real industrial processes, process variables are rarely Gaussian distributed. Thus, this study proposes the application of ICA to extract the hidden information of a non-Gaussian process before conducting SVM. The proposed fault detector will be implemented via two simulated processes and a case study of the Tennessee Eastman process. Results demonstrate that the proposed method possesses superior fault detection when compared to conventional monitoring methods, including PCA, ICA, modified ICA, ICA-PCA and PCA-SVM. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
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    Item type:Item,
    A novel process monitoring approach with dynamic independent component analysis
    (2010-03-01)
    Hsu, Chun-Chin
    ;
    Chen, Mu-Chen
    ;
    Chen, Long-Sheng
    ;
    運輸與物流管理系 註：原交通所+運管所
    ;
    Department of Transportation and Logistics Management
    A novel process monitoring scheme is proposed to compensate for shortcomings in the conventional independent component analysis (ICA) based monitoring method. The primary idea is first to augment the observed data matrix in order to take the process dynamic into consideration. An outlier rejection rule is then proposed to screen out outliers, in order to better describe the majority of the data. Finally, a rectangular measure is used as a monitoring statistic. The proposed approach is investigated via three cases: a simulation example, the Tennessee Eastman process and a real industrial case. Results indicate that the proposed method is more efficient as compared to alternate methods. Crown Copyright (C) 2009 Published by Elsevier Ltd. All rights reserved.