Browsing by Author "Liu, TC"

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A new ART-counterpropagation neural network for solving a forecasting problem
(2005-01-01)
Liu, TC
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Li, RK
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工業工程與管理學系
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Department of Industrial Engineering and Management
This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance. (C) 2004 Elsevier Ltd. All rights reserved.
Development and application of an airport terminal simulation model - A case study of CKS Airport
(1998)
Wong, JT
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Liu, TC
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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
Technology advancement, terminal user behavior and changes of service characteristics of terminal facilities all have great impact on airport terminal operations. Consequently, the adequacy of traditional airport terminal planning concepts and standards have recently been challenged and are worth being explored. To investigate the associated impact of environmental changes on terminal operations, a simulation model which takes into consideration air travel patterns, facility operational characteristics, flight delays, passenger behavior and needs, etc, is developed and verified. The simulation logic and results show that the model works well for the case of CKS (Chiang Kai-Shek International Airport, Taipei) operations. Also, the exploration demonstrates that passenger arrival patterns, numbers of group passengers, flight delays and load factors all have very significant influences on terminal space requirements. This result strongly suggests that local characteristics should not be neglected in planning terminal operations.
Development of an artificial neural network to predict lead frame dimensions in an etching process
(2006-02-01)
Liu, TC
;
Li, RK
;
Chen, MC
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工業工程與管理學系
;
Department of Industrial Engineering and Management
The electronic industry is rapidly developing, creating high demand for IC production. Etched semiconductor lead frames are the basic material used in IC packaging. IC packaging requires high-precision lead frames. The dimensions of the pilot hole are generally required to be highly precise in lead frame manufacturing. The photo-etching process must control the dimension of the pilot hole and record the manufacturing data of the etching machine and inspection data. This study presents the development of an artificial neural network (ANN) model that can be applied to construct the predicting model. The predictive model can estimate the dimensions of the pilot hole and thus determine the process parameters needed to improve lead frame quality in the etching process.