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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

Access type: Metadata onlyAuthor: search.filters.author.Chen, Long-Sheng

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Now showing 1 - 3 of 3
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    Apply Ensemble Empirical Mode Decomposition to Discover Time Variants of Metro Station Passenger Flow
    (2017-01-01)
    Chen, Mu-Chen
    ;
    Chen, Long-Sheng
    ;
    Wei, Yu
    ;
    運輸與物流管理系 註：原交通所+運管所
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    Department of Transportation and Logistics Management
    This paper applies both Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) to extract the EMD and EEMD components from a data set of passenger flows of a station in the metro system, and illustrates the time variants of short-term passenger flow for this data sets. The results indicate that the extracted meaningful EEMD components reveal a more unique pattern than the extracted meaningful EMD components. The patterns of these EEMD components of passenger flow in the metro system are more specific and can be explained more easily for management purposes.
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    Applying Neural Networks to Detect the Failures of Turbines in Thermal Power Facilities
    (2009)
    Chen, Kai-Ying
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    Chen, Long-Sheng
    ;
    Chen, Mu-Chen
    ;
    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 has become one of the critical issues in a thermal power plant. Related works reported that efficiency and availability depend heavily on 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. Machine learning 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 applies Back-propagation Neural Networks (BPN) 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.
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    HPRS: A profitability based recommender system
    (2007)
    Chen, Mu-Chen
    ;
    Chen, Long-Sheng
    ;
    Hsu, Fei-Hao
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    Hsu, Yuanjia
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    Chou, Hsiao-Ying
    In electronic commerce, recommender systems are popularly being used to help enterprises for satisfying customers' individually diverse preferences. These systems learn about user preferences over time and automatically suggest products that fit the learned model of user preferences. In tradition, recommendations are provided to customers based on purchase probability and customers' preferences, without considering the profitability factor for sellers. This work presents a new profitability-based recommender system, HPRS (Hybrid Perspective Recommender System), which attempts to integrate the profitability factor into the traditional recommender systems. Comparisons between our proposed system and traditional system which only considers the purchase probability clarify the advantages of our system. The experimental results show that the proposed HPRS can increase profit from cross-selling without compromising recommendation accuracy.