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Author: search.filters.author.Chen, Long-ShengSubject: search.filters.subject.Data mining

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    Using SVM based method for equipment fault detection in a thermal power plant
    (2011-01-01)
    Chen, Kai-Ying
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    Chen, Long-Sheng
    ;
    Chen, Mu-Chen
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    Lee, Chia-Lung
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    運輸與物流管理系 註：原交通所+運管所
    Due to the growing demand on electricity, how to improve the efficiency of equipment in a thermal power plant has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependant upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Data mining techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work proposes a support vector machines (SVM) based model which integrates a dimension reduction scheme to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness of the proposed SVM based model. Experimental results show that SVM outperforms linear discriminant analysis (LDA) and back-propagation neural networks (BPN) in classification performance. (C) 2010 Elsevier B.V. All rights reserved.
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    CUSTOMER SEGMENTATION AND CLASSIFICATION FROM BLOGS BY USING DATA MINING: AN EXAMPLE OF VOIP PHONE
    (2009)
    Chen, Long-Sheng
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    Hsu, Chun-Chin
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    Chen, Mu-Chen
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    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.