Browsing by Author "Hsu, Chun-Chin"

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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.
Adaptive Kernel Principal Component Analysis (KPCA) for Monitoring Small Disturbances of Nonlinear Processes
(2010-03-03)
Cheng, Chun-Yuan
;
Hsu, Chun-Chin
;
Chen, Mu-Chen
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
The Tennessee Eastman (TE) process, created by Eastman Chemical Company, is a complex nonlinear process. Many, previous studies focus on the detectability of monitoring a multivariate process by using TE process as an example. Principal component analysis (PCA) is a widely used dimension-reduction tool for monitoring multivariate linear process. Recently, the kernel principal component analysis (KPCA) has emerged as all effective method to tackling the problem of nonlinear data. Nevertheless, the conventional KPCA used the sum of squares of latest observations is the monitoring statistics and hence failed to detect small disturbance of the process. To enhance the delectability of the KPCA-based monitoring method, an adaptive KPCA-based monitoring statistic is proposed in this paper. The basic idea of the proposed method is first adopting the multivariate exponentially moving average to predict the process mean shifts and then combining the estimated mean shifts with the extracted components by KPCA to construct the adaptive monitoring statistic. The efficiency of the proposed monitoring scheme is implemented in a simulated nonlinear system and in the TE process. The experimental results indicate that the proposed method outperforms the traditional PCA and KPCA monitoring schemes.
An efficient ICA-DW-SVDD fault detection and diagnosis method for non-Gaussian processes
(2016)
Chen, Mu-Chen
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Hsu, Chun-Chin
;
Malhotra, Bharat
;
Tiwari, Manoj Kumar
;
運輸與物流管理系註：原交通所+運管所
Independent Component Analysis (ICA) has been extensively used for detecting faults in industrial processes. While applying ICA to process monitoring, the inability of identifying the important components affect the fault diagnosis ability. For further improving the competence of ICA, this paper proposes an approach integrating ICA, Durbin Watson (DW) criterion and Support Vector Data Description (SVDD) to monitor non-Gaussian process for detecting faults. In the proposed approach, namely ICA-DW-SVDD, ICA is a non-Gaussian information extractor from original variables, DW identifies dominating ICs, and SVDD plays the role of fault detector. This paper also discusses the retracing method to detect original variables causing disturbance in the process. One simulation case and the Tennessee Eastman Process are used to demonstrate the effectiveness of our proposed approach.
CUSTOMER SEGMENTATION AND CLASSIFICATION FROM BLOGS BY USING DATA MINING: AN EXAMPLE OF VOIP PHONE
(2009)
Chen, Long-Sheng
;
Hsu, Chun-Chin
;
Chen, Mu-Chen
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Blogs have been considered the 4th Internet application that can cause radical changes in the world, after e-mail, instant messaging, and Bulletin Board System (BBS). Many Internet users rely heavily on them to express their emotions and personal comments on whatever topics interest them. Nowadays, blogs have become the popular media and could be viewed as new marketing channels. Depending on the blog search engine, Technorati, we tracked about 94 million blogs in August 2007. It also reported that a whole new blog is created every 7.4 seconds and 275,000 blogs are updated daily. These figures can be used to illustrate the reason why more and more companies attempt to discover useful knowledge from this vast number of blogs for business purposes. Therefore, blog mining could be a new trend of web mining. The major objective of this study is to present a structure that includes unsupervised (self-organizing map) and supervised learning methods (back-propagation neural networks, decision tree, and support vector machines) for extracting knowledge from blogs, namely, a blog mining (BM) model. Moreover, a real case regarding VoIP (Voice over Internet Protocol) phone products is provided to demonstrate the effectiveness of the proposed method.
Information granulation based data mining approach for classifying imbalanced data
(2008-08-15)
Chen, Mu-Chen
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Chen, Long-Sheng
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Hsu, Chun-Chin
;
Zeng, Wei-Rong
;
運輸與物流管理系註：原交通所+運管所
Recently, the class imbalance problem has attracted much attention from researchers in the field of data mining. When learning from imbalanced data in which most examples are labeled as one class and only few belong to another class, traditional data mining approaches do not have a good ability to predict the crucial minority instances. Unfortunately, many real world data sets like health examination, inspection, credit fraud detection, spam identification and text mining all are faced with this situation. In this study, we present a novel model called the "Information Granulation Based Data Mining Approach" to tackle this problem. The proposed methodology, which imitates the human ability to process information, acquires knowledge from Information Granules rather then from numerical data. This method also introduces a Latent Semantic Indexing based feature extraction tool by using Singular Value Decomposition, to dramatically reduce the data dimensions. In addition, several data sets from the UCI Machine Learning Repository are employed to demonstrate the effectiveness of our method. Experimental results show that our method can significantly increase the ability of classifying imbalanced data. (c) 2008 Elsevier Inc. All rights reserved.
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.