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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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Access type: Open accessSubject: search.filters.subject.ICA

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Now showing 1 - 4 of 4
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    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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    Alpha modulation in parietal and retrosplenial cortex correlates with navigation performance
    (2012-01-01)
    Chiu, Te-Cheng
    ;
    Gramann, Klaus
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    Ko, Li-Wei
    ;
    Duann, Jeng-Ren
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    Jung, Tzyy-Ping
    The present study investigated the brain dynamics accompanying spatial navigation based on distinct reference frames. Participants preferentially using an allocentric or an egocentric reference frame navigated through virtual tunnels and reported their homing direction at the end of each trial based on their spatial representation of the passage. Task-related electroencephalographic (EEG) dynamics were analyzed based on independent component analysis (ICA) and subsequent clustering of independent components. Parietal alpha desynchronization during encoding of spatial information predicted homing performance for participants using an egocentric reference frame. In contrast, retrosplenial and occipital alpha desynchronization during retrieval covaried with homing performance of participants using an allocentric reference frame. These results support the assumption of distinct neural networks underlying the computation of distinct reference frames and reveal a direct relationship of alpha modulation in parietal and retrosplenial areas with encoding and retrieval of spatial information for homing behavior.
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    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.
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    Item type:Item,
    Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring
    (2010-04-01)
    Chun-Chin, Hsu
    ;
    Mu-Chen, Chen
    ;
    Long-Sheng, Chen
    ;
    運輸與物流管理系 註：原交通所+運管所
    ;
    Department of Transportation and Logistics Management
    Recently, the independent component analysis (ICA) has been widely used for multivariate non-Gaussian process monitoring. For principal component analysis (PCA) based monitoring method, the control limit can be determined by a specific distribution (F distribution) due to the PCA extracted components are assumed to follow multivariate Gaussian distribution. However, the control limit for ICA based monitoring statistic is determined by using kernel density estimation (KDE). It is well known that the KDE is sensitive to the smoothing parameter, and it does not perform well with autocorrelated data. In most cases, the calculated ICA based monitoring statistic is usually autocorrelated. Thus, this study aims to integrate ICA and support vector machine (SVM) in order to develop an intelligent fault detector for non-Gaussian multivariate process monitoring. Simulation study indicates that the proposed method can effectively detect faults when compare to methods of original SVM and PCA based SVM in terms of detection rate. (C) 2009 Elsevier Ltd. All rights reserved.