Browsing by Author "Wang, Yi-Chi"

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A Direct-solution Fuzzy Collaborative Intelligence Approach for Yield Forecasting in Semiconductor Manufacturing
(2018-01-01)
Wang, Yi-Chi
;
Chen, Tin-Chih Toly
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Yield forecasting is a critical task to every semiconductor manufacturer. However, the existing methods for yield forecasting often deal with the logarithmic or log-sigmoid value, rather than the original value, of yield. To resolve this problem, in this study, the fuzzy collaborative intelligence (FCT) method proposed by Chen and Tin (2008) is modified, so that it can consider the original value of yield directly. The modified FCI method is called the direct-solution (DS)-FCI approach. The effectiveness oldie DS-FCI approach was validated with a real case. The experimental results showed that the DS-FCT approach outperformed Chen and Tin's FCI method in improving the forecasting accuracy and precision. (C) 2018 The Authors. Published by Elsevier B.V.
An incremental learning and integer-nonlinear programming approach to mining users' unknown preferences for ubiquitous hotel recommendation
(2019-07-01)
Chen, Tin-Chih Toly
;
Wang, Yi-Chi
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
To further increase the successful recommendation rate of a ubiquitous hotel recommendation system, an incremental learning and integer-nonlinear programming approach (INLP) is proposed in this study to mine users' unknown preferences. In the proposed methodology, an INLP problem is solved to adjust the values of weights in the recommendation mechanism to be closer to those in the decision-making mechanism so as to maximize the successful recommendation rate. In addition, the weights are adjusted on a rolling basis so that more historical data can be considered without inflating the INLP model. The experimental results showed that the proposed methodology outperformed several existing methods in increasing the successful recommendation rate, even with a cold start.