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研究生:邱政賢
研究生(外文):Cheng-HsienChiu
論文名稱:使用盒鬚圖建構初期高維度的製造模式-以8.5代廠之液晶製造廠為例
論文名稱(外文):Employing Box-and-Whisker Plots to Build Early High-Dimensional Manufacturing Models in 8.5 G TFT-LCD Plants
指導教授:利德江利德江引用關係
指導教授(外文):Der-Chiang Li
學位類別:碩士
校院名稱:國立成功大學
系所名稱:高階管理碩士在職專班(EMBA)
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:60
中文關鍵詞:小樣本資料虛擬樣本盒鬚圖資訊擴散
外文關鍵詞:small datasetvirtual samplebox-and-whisker plotsinformation diffusion
相關次數:
  • 被引用被引用:0
  • 點閱點閱:271
  • 評分評分:
  • 下載下載:26
  • 收藏至我的研究室書目清單書目收藏:0
由於全球化的激烈競爭,為滿足各種客戶不同的需求,產品生命週期已變得越來越短,此種現象於近年來在電子產業尤甚。在如此高度競爭的產業中,如何藉由減少試產次數以縮減新產品開發的時程進而加速產品上市,已成為一個很重要的策略,但亦因此而衍伸出小樣本的學習問題。本論文針對一個存在於液晶面板製造廠的陣列(Array)製程之實務問題,在其曝光子製程中包含九個輸入屬性、七十二個輸出屬性,但資料僅有二十筆的個案進行研究。由於此種高輸入維度、多輸出、且少樣本的學習問題對於現有的機械學習演算法並不易處理,因此本研究應用盒鬚圖先行估算各屬性的合理值域後,再使用可能性評估機制來擴展樣本數量以建構穩健的管理模式。實驗結果顯示,藉由衍生出之虛擬樣本確可建構更穩健、精準的倒傳遞類神經網路,並祈能協助個案公司之工程師應用以預先推論相關的製程知識。
Owing to the global competition, product life cycles are becoming shorter and shorter to meet the various demands of customers, and such situation is usually seen in the electronic industry in recent years. In such highly competitive industry, it has become an important strategy to accelerate new products coming to the market by reducing the pilot runs. Therefore, it leads a small dataset learning problem. In this paper, a real learning task in the Array production process for a TFT-LCD (thin-film transistor liquid-crystal display) that has seventy-two output attributes is proposed. It is quite difficult for most existing modeling algorithms to deal with such a high dimensional situation when the sample size is small in the early stage of a manufacturing system. In order to archive it, this paper employs the box-and-whisker plots to estimate the possible distributions of attribute values, and uses a plausibility assessment mechanism to extend sample sizes for building robust management models. By deriving more samples, the results of the experiment show that the approach presented in this research is effective in building a more robust and precise back-propagation neural network. Engineers thus can infer more process knowledge in advance by applying the model.
摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1 研究背景 1
1.1.1 個案描述 1
1.1.2 小樣本學習問題 4
1.2 研究動機 8
1.3 研究目的 9
1.4 研究範圍與限制 10
1.5 研究流程 10
第二章 文獻探討 12
2.1 資訊擴散技術為基礎的虛擬樣本產生法 12
2. 2 其他虛擬樣本產生法 19
第三章 研究方法 24
3.1 符號定義 24
3.2 虛擬樣本產生流程 25
3.2.1 值域推估 26
3.2.2 母體分配推論 28
3.2.3 虛擬值生成 29
3.2.4 樣本形成 30
3.3 倒傳遞類神經網路 30
第四章 實例驗證 37
4.1 實驗流程 37
4.1.1 實驗方式 38
4.1.2 預測誤差評估指標 38
4.1.3 假設檢定 39
4.2 個案實驗 40
4.3 實驗步驟詳述 44
4.4 實驗結果 50
第五章 結論與建議 54
5.1 結論 54
5.2 建議 54
參考文獻 56

Anthony, M., & Biggs, N. (1997). Computational Learning Theory: Cambridge University Press.
Chan, K. Y., Kwong, C. K., & Tsim, Y. C. (2010). A genetic programming based fuzzy regression approach to modelling manufacturing processes. International Journal of Production Research, 48(7), 1967-1982.
Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap: New York: Chapmen & Hall.
Guo, G. D., & Dyer, C. R. (2005). Learning from examples in the small sample case: Face expression recognition. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 35(3), 477-488.
Hong, T. P., Tseng, L. H., & Chien, B. C. (2010). Mining from incomplete quantitative data by fuzzy rough sets. Expert Systems with Applications, 37(3), 2644-2653.
Huang, C. F. (1997). Principle of information. Fuzzy Sets and Systems, 91(1), 69-90.
Huang, C. F., & Moraga, C. (2004). A diffusion-neural-network for learning from small samples. International Journal of Approximate Reasoning, 35(2), 137-161.
Huang, C. J., Wang, H. F., Chiu, H. J., Lan, T. H., Hu, T. M., & Loh, E. W. (2010). Prediction of the Period of Psychotic Episode in Individual Schizophrenics by Simulation-Data Construction Approach. Journal of Medical Systems, 34(5), 799-808.
Ivănescu, V. C., Bertrand, J. W. M., Fransoo, J. C., & Kleijnen, J. P. C. (2006). Bootstrapping to solve the limited data problem in production control: an application in batch process industries. Journal of the Operational Research Society, 57(1), 2-9.
Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685.
Jennrich, R. I., & Schluchter, M. D. (1986). Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 42(4), 805-820.
Kuo, Y., Yang, T., Peters, B. A., & Chang, I. (2007). Simulation metamodel development using uniform design and neural networks for automated material handling systems in semiconductor wafer fabrication. Simulation Modelling Practice and Theory, 15(8), 1002-1015.
Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963-974.
Lanouette, R., Thibault, J., & Valade, J. L. (1999). Process modeling with neural networks using small experimental datasets. Computers & Chemical Engineering, 23(9), 1167-1176.
Li, D., Gu, H., & Zhang, L. Y. (2010). A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data. Expert Systems with Applications, 37(10), 6942-6947.
Li, D. C., Chang, C. C., & Liu, C. W. (2012a). Using structure-based data transformation method to improve prediction accuracies for small data sets. Decision Support Systems, 52(3), 748-756.
Li, D. C., Chang, F. M. M., & Chen, K. C. (2010a). Building reliability growth model using sequential experiments and the Bayesian theorem for small datasets. Expert Systems with Applications, 37(4), 3434-3443.
Li, D. C., Chen, C. C., Chang, C. J., & Chen, W. C. (2012b). Employing Box-and-Whisker plots for learning more knowledge in TFT-LCD pilot runs. International Journal of Production Research, 50(6), 1539-1553.
Li, D. C., Chen, C. C., Chang, C. J., & Lin, W. K. (2012c). A Tree-based-Trend-Diffusion prediction procedure for small sample sets in the early stages of manufacturing systems. Expert Systems with Applications, 39(1), 1575-1581.
Li, D. C., Chen, C. C., Chen, W. C., & Chang, C. J. (2012d). Employing dependent virtual samples to obtain more manufacturing information in pilot runs. International Journal of Production Research, 50(23), 6886-6903.
Li, D. C., Chen, L. S., & Lin, Y. S. (2003). Using Functional Virtual Population as assistance to learn scheduling knowledge in dynamic manufacturing environments. International Journal of Production Research, 41(17), 4011-4024.
Li, D. C., Fang, Y. H., Lai, Y. Y., & Hu, S. C. (2009a). Utilization of virtual samples to facilitate cancer identification for DNA microarray data in the early stages of an investigation. Information Sciences, 179(16), 2740-2753.
Li, D. C., Hsu, H. C., Tsai, T. I., Lu, T. J., & Hu, S. C. (2007a). A new method to help diagnose cancers for small sample size. Expert Systems with Applications, 33(2), 420-424.
Li, D. C., Huang, W. T., Chen, C. C., & Chang, C. J. (2013). Employing virtual samples to build early high-dimensional manufacturing models. International Journal of Production Research. doi: 10.1080/00207543.2012.746795
Li, D. C., & Lin, Y. S. (2006). Using virtual sample generation to build up management knowledge in the early manufacturing stages. European Journal of Operational Research, 175(1), 413-434.
Li, D. C., & Liu, C. W. (2010). Extending Attribute Information for Small Data Set Classification. IEEE Transactions on Knowledge and Data Engineering, 24(3), 452-464.
Li, D. C., Liu, C. W., Fang, Y. H., & Chen, C. C. (2010b). A yield forecast model for pilot products using support vector regression and manufacturing experience-the case of large-size polariser. International Journal of Production Research, 48(18), 5481-5496.
Li, D. C., Liu, C. W., & Hu, S. C. (2010c). A learning method for the class imbalance problem with medical data sets. Computers in Biology and Medicine, 40(5), 509-518.
Li, D. C., Tsai, T. I., & Shi, S. (2009b). A prediction of the dielectric constant of multi-layer ceramic capacitors using the mega-trend-diffusion technique in powder pilot runs: case study. International Journal of Production Research, 47(1), 51-69.
Li, D. C., Wu, C. S., & Chang, F. M. M. (2005). Using data-fuzzification technology in small data set learning to improve FMS scheduling accuracy. International Journal of Advanced Manufacturing Technology, 27(3-4), 321-328.
Li, D. C., Wu, C. S., Tsai, T. I., & Chang, F. M. M. (2006). Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge. Computers & Operations Research, 33(6), 1857-1869.
Li, D. C., Wu, C. S., Tsai, T. I., & Lina, Y. S. (2007). Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Computers & Operations Research, 34(4), 966-982.
Niyogi, P., Girosi, F., & Poggio, T. (1998). Incorporating prior information in machine learning by creating virtual examples. Proceedings of the IEEE, 86(11), 2196-2209.
Oniśko, A., Druzdzel, M. J., & Wasyluk, H. (2001). Learning Bayesian network parameters from small data sets: application of Noisy-OR gates. International Journal of Approximate Reasoning, 27(2), 165-182.
Papari, M. M., Yousefi, F., Moghadasi, J., Karimi, H., & Campo, A. (2011). Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks. International Journal of Thermal Sciences, 50(1), 44-52.
Thomas, M., Kanstein, A., & Goser, K. (1997). Rare fault detection by possibilistic reasoning. Paper presented at the In Proceedings of Fuzzy Days, Reusch, Bernd, Berlin.
Tsai, T. I., & Li, D. C. (2008a). Approximate modeling for high order non.-linear functions using small sample sets. Expert Systems with Applications, 34(1), 564-569.
Tsai, T. I., & Li, D. C. (2008b). Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems. Expert Systems with Applications, 35(3), 1293-1300.
Tukey, J. W. (1977). Exploratory data analysis: Reading (MA): Addison-Wesley.
Vapnik, V. N. (2000). The Nature of Statistical Learning Theory: Springer, New York.
Wang, F. K., Du, T., & Wen, F. C. (2007). Product mix in the TFT-LCD industry. Production Planning & Control, 18(7), 584-591.
Wang, H. F., & Huang, C. J. (2009). Data construction method for the analysis of the spatial distribution of disastrous earthquakes in Taiwan. International Transactions in Operational Research, 16(2), 189-212.
Wang, Y., Song, Q. B., MacDonell, S., Shepperd, M., & Shen, J. Y. (2009). Integrate the GM(1,1) and Verhulst Models to Predict Software Stage Effort. Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 39(6), 647-658.
Wang, Y., & Witten, I. (1997). Inducing Model Trees for Continuous Classes. Paper presented at the Proceedings of the Poster Papers of the European Conference on Machine Learning, Prague, Czech Republic.
Wang, Y. F. (2003). On-demand forecasting of stock prices using a real-time predictor. IEEE Transactions on Knowledge and Data Engineering, 15(4), 1033-1037.
Willemain, T. R., Bress, R. A., & Halleck, L. S. (2003). Enhanced simulation inference using bootstraps of historical inputs. IIE Transactions, 35(9), 851-862.
Wolpert, D. H. (1992). Stacked Generalization. Neural Networks, 5(2), 241-259.
Wu, C. W., Shu, M. H., Pearn, W. L., & Liu, K. H. (2008). Bootstrap approach for supplier selection based on production yield. International Journal of Production Research, 46(18), 5211-5230.
Zhang, J. (1999). Inferential estimation of polymer quality using bootstrap aggregated neural networks. Neural Networks, 12(6), 927-938.

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