Browsing by Author "Yeh, Tzu-Lei"

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A MapReduce-Based Ensemble Learning Method with Multiple Classifier Types and Diversity for Condition-Based Maintenance with Concept Drifts
(2017-11-01)
Lin, Chun-Cheng
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Shu, Lei
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Deng, Der-Jiunn
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Yeh, Tzu-Lei
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Chen, Yu-Hsiang
Condition-based maintenance in Industry 4.0 collects a huge amount of production datastreams continuously from the Internet of Things attached to machines to forecast the time when to maintain machines or replace components. However, as conditions of machines change dynamically with time owing to machine aging, malfunction, or replacement, the concept of capturing the forecasting pattern from the datastream could drift unpredictably, so it is hard to find a robust forecasting method with high precision. Therefore, this work proposes an ensemble learning method with multiple classifier types and diversity for condition-based maintenance in manufacturing industries, to address the bias problem when using only one base classifier type. Aside from manipulating data diversity, this method includes multiple classifier types, dynamic weight adjusting, and data-based adaption to concept drifts for offline learning models, to promote precision of the forecasting model and precisely detect and adapt to concept drifts. With these features, the proposed method requires powerful computing resources to efficiently respond to practical condition-based maintenance applications. Therefore, the implementation of this method based on the MapReduce framework is proposed to increase computational efficiency. Simulation results show that this method can detect and adapt to all concept drifts with a high precision rate.
Supply chain optimization of forest biomass electricity and bioethanol coproduction
(2017-11-15)
Liu, Wan-Yu
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Lin, Chun-Cheng
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Yeh, Tzu-Lei
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工業工程與管理學系
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Department of Industrial Engineering and Management
To establish a systematic and efficient forest bioenergy supply chain management system, most previous related studies did not investigate a system that involves generating two or more types of energy simultaneously, and did not consider the carbon emissions caused by electricity power generation of forest biomass combustion. Consequently, this study addressed the problems regarding the tactical supply chain optimization of a forest biomass electricity and second-generation bioethanol coproduction plant. A mixed-integer linear programming model was employed to determine the optimal production strategies to maximize the profit and to minimize the carbon emissions cost. The advantages of coproduction include enabling equipment and facility sharing, reducing investment costs, and dispersing the risks that involve raw materials, leading to synergy. Empirical simulation results indicated that the coproduction model yielded more profit than the independently operated model. When electricity demand is reduced, the coproduction model mitigates its profit losses through an increase in its bioethanol production. However, when faced with an increase in carbon emission costs, the coproduction model loses its advantages. According to the sensitivity analysis, electricity prices are the most vital parameter to the profit of the model, followed sequentially by the higher heating value of raw materials and carbon emission costs. (C) 2017 Elsevier Ltd. All rights reserved.