臺灣博碩士論文加值系統

許多的企業現正面臨全球化的競爭與縮短的新產品生命週期。因此，假如他們無法掌握產品品質，他們將延遲產品開發與上市時間，而且甚至無法快速地提供多樣化的產品。資料探勘能從資料中發掘隱藏的知識型樣，而且能促使複雜的企業流程能被瞭解與再造。此外，無線射頻辨識能輕鬆使每一個物件變成行動網路的節點，此一節點能被追蹤、監控、觸發行動、或回應行動的要求。因此，本研究聚焦在資料探勘與無線射頻辨識在品質管理方面的整合與應用。
本研究的目的是要提出一個以無線射頻辨識為基礎的混合與啟發式資料探勘架構，以發掘隱藏且有意義的產品品質的知識規則，以及再造品質管理流程，以幫助企業提升產品品質。本架構主要是利用無線射頻辨識、製造執行系統、基因演算法、基於倒傳遞網路演算法的類神經網路、決策樹演算法、貝氏分類演算法。在此一架構中，儲存產品品質資料的製造執行系統是作為資料探勘的資料來源。本研究提出三種類型的演算法，來當做資料探勘引擎的核心，以進行資料分類與預測。在資料探勘系統中，這些演算法被區分成實驗組與對照組。在實驗組中，本研究利用人工智慧的方法學，提出整合基因演算法與倒傳遞網路演算法的混合與啟發式方法。在對照組中，本研究利用統計的方法學，提出決策樹演算法與貝氏分類演算法。在測試與驗證這些演算法之後，最佳的演算法則被選取與利用於探勘隱藏的產品品質知識。然後，本研究將探勘到的產品品質知識合併到無線射頻辨識系統中。此無線射頻辨識系統則能促使品質管理流程的再造。為了測試與驗證此一架構的可應用性與有效性，本研究實際應用此一架構於企業實務中。
本研究的結果顯示所提出的架構能緊密地整合資料探勘、無線射頻辨識系統、品質管理流程。此一架構被應用來改善產品品質的可追蹤度與可視度，以及讓企業做出較佳的決策。依據實驗結果與分析，此一架構能實際地幫助企業提升客戶滿意度、節省成本、提升內部流程效率、促進組織學習與成長。本研究所提出的架構亦能被應用於生產管理、倉儲管理、產品銷售推薦，以提升企業競爭力。

Many enterprises are confronting global competition and shortened life cycle of new products now. Therefore, if they can not master product quality, they will delay the product development as well as time to market and can not even provide product variety immediately. The data mining can find hidden knowledge patterns in data and enable complex business processes to be understood and reengineered. In addition, RFID can effortlessly turn every object into mobile network nodes which can be tracked, traced, monitored, trigger actions, or respond to action requests. Therefore, this study focused on the integration and application of data mining and RFID system for quality management.
The purpose of this study was to propose an RFID-based hybrid heuristic data mining (RHHDM) framework to discover hidden and meaningful knowledge rules for product quality and reengineer the quality management processes in order to help enterprises enhance product quality. The RHHDM framework primarily utilized RFID, manufacturing execution system (MES), genetic algorithm (GA), artificial neural network (ANN) based on back propagation network (BPN) algorithm, decision tree algorithm, and Bayesian classification algorithm. In the RHHDM framework, the MES storing product quality data was the data source of data mining system. This study proposed three types of algorithms to act as the nucleus of data mining engines for data classification and prediction. These algorithms were divided into experiment group and contrast group in the data mining system. In experiment group, this study utilized artificial intelligence approaches to propose hybrid heuristic methods integrating the GA and BPN (GABPN) algorithm. In contrast group, this study utilized statistical approaches to propose the decision tree algorithm and Bayesian classification algorithm. After testing and verifying these algorithms, the best algorithm was selected and utilized to mining hidden product quality knowledge. Then, this study incorporated the discovered product quality knowledge into RFID system in the RHHDM framework. The RFID system could enable the quality management processes to be reengineered. This study actually applied the proposed RHHDM framework to the enterprise in practice in order to test and verify the applicability and effectiveness of the framework.
The results of this study were that the proposed RHHDM framework could tightly integrate data mining, RFID system, and quality management processes. The RHHDM framework was applied to improve the traceability and visibility of product quality and make better decisions by the enterprise. According to the experimental results and analyses, the RHHDM framework could actually help the enterprise enhance customer satisfaction, save the cost, enhance the efficiency of the internal process, and enable the organization learning and growth. The proposed RHHDM framework could be also applied to production management, warehouse management, and product recommendation for sale, in order to help enterprises enhance competitiveness.

Chinese Abstract I
English Abstract III
Acknowledgements V
Contents VI
List of Figures VIII
List of Tables IX
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Motivation 2
1.3 Research Objectives 6
1.4 Research Architecture 8
Chapter 2 Literature Review 11
2.1 RFID Technology 11
2.2 Data Mining 15
2.3 Artificial Neural Network 22
2.4 Genetic Algorithm 23
Chapter 3 Methodology 25
3.1 Proposed RHHDM Framework 25
3.2 Back Propagation Network Algorithm 26
3.3 GABPN Algorithm 27
3.4 Decision Tree Algorithm 29
3.5 Bayesian Classification Algorithm 30
Chapter 4 Experimental Design 31
4.1 Case Study 31
4.2 Data Preprocessing 32
4.3 Data Mining Architecture 33
4.4 MMAE RFID Architecture 36
Chapter 5 Experimental Result and Evaluation 40
5.1 Information Infrastructure 40
5.2 Results of BPN Algorithm 43
5.3 Results of GABPN Algorithm 47
5.4 Results of Decision Tree Algorithm 50
5.5 Results of Bayesian Classification Algorithm 52
5.6 Comparison of Algorithms 53
5.7 Application and Results in Practice 54
Chapter 6 Conclusion and Future Work 62
6.1 Conclusion 62
6.2 Future Work 64
References 66

[1] S. Garfinkel and B. Rosenberg, RFID: Applications, security, and privacy, Upper Saddle River, NJ, Addison-Wesley, 2006.
[2] S. Shepard, RFID: Radio frequency identification, New York, NY, McGraw-Hill, 2005.
[3] C. Heinrich, RFID and beyond: Growing your business through real world awareness, Indianapolis, IN, Wiley, 2005.
[4] D. Paret, RFID and contactless smart card applications, England, Wiley, 2005.
[5] Y. Zhang, L.T. Yang, and J. Chen, RFID and sensor networks: Architectures, protocols, security, and integrations, Boca Raton, FL, CRC Press, 2010.
[6] A. Rida, L. Yang, and M. Tentzeris, RFID-enabled sensor design and applications, Norwood, MA, Artech House, 2010.
[7] B. Glover and H. Bhatt, RFID essentials, Sebastopol, CA, O'Reilly, 2006.
[8] P.J. Sweeney, RFID for dummies, Hoboken, NJ, Wiley, 2005.
[9] K. Finkenzeller, RFID handbook: Fundamentals and applications in contactless smart cards and identification, England, Wiley, 2003.
[10] J.M. Myerson, RFID in the supply chain: A guide to selection and implementation, Boca Raton, FL, Auerbach, 2007.
[11] R.A. Kleist, RFID labeling: Smart labeling concepts &; applications for the consumer packaged goods supply chain, Irvine, CA, Printronix Inc., 2004.
[12] W.O. Hedgepeth, RFID metrics: Decision making tools for today's supply chains, Boca Raton, FL, CRC Press, 2007.
[13] S. Lahiri, RFID sourcebook, Upper Saddle River, NJ, IBM Press, 2006.
[14] Y. Zhang and P. Kitsos, Security in RFID and sensor networks, Boca Raton, FL, CRC Press, 2009.
[15] P. Giudici and S. Figini, Applied data mining for business and industry, Chichester, U.K., Wiley, 2009.
[16] C. Vercellis, Business intelligence: Data mining and optimization for decision making, Chichester, U.K., Wiley, 2009.
[17] Galit Shmueli, Nitin R. Patel, and Peter C. Bruce, Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner, Hoboken, NJ, John Wiley &; Sons, 2007.
[18] R. Chattamvelli, Data mining methods, Oxford, U.K., Alpha Science International, 2009.
[19] D.T. Larose, Data mining methods and models, Hoboken, NJ, John Wiley &; Sons, 2006.
[20] L. Rokach and O. Maimon, Data mining with decision trees: Theroy and applications, Singapore, World Scientific, 2008.
[21] J.P. Bigus, Data mining with neural networks: Solving business problems--from application development to decision support, New York, NY, McGraw-Hill, 1996.
[22] L. Torgo, Data mining with R: Learning with case studies, Boca Raton, FL, Chapman &; Hall/CRC, 2011.
[23] G. Williams, Data mining with Rattle and R: The art of excavating data for knowledge discovery, New York, NY, Springer, 2011.
[24] M. Mohammadian, Intelligent agents for data mining and information retrieval, Hershey, PA, Idea Group Publishing, 2004.
[25] D. Olson and Y. Shi, Introduction to business data mining, New York, NY, McGraw-Hill/Irwin, 2007.
[26] S. Sumathi and S.N. Sivanandam, Introduction to data mining and its applications, Berlin, Springer, 2006.
[27] T.M. Huang, V. Kecman, and I. Kopriva, Kernel based algorithms for mining huge data sets: Supervised, semi-supervised, and unsupervised learning, Berlin, Springer, 2006.
[28] X. Zhu and I. Davidson, Knowledge discovery and data mining: Challenges and realities, Hershey, PA, Information Science Reference, 2007.
[29] T.W. Liao and E. Triantaphyllou, Recent advances in data mining of enterprise data: Algorithms and applications, Singapore, World Scientific, 2007.
[30] J. Han, M. Kamber, and J. Pei, Data mining: Concepts and techniques, Burlington, MA, Elsevier, 2011.
[31] A. Gunasekaran, E.W.T. Ngai, and R.E. McGaughey, Information technology and systems justification: A review for research and applications, European Journal of Operational Research, Vol.173, No.3, pp.957-983, 2006.
[32] R.G. Qiu, RFID-enabled automation in support of factory integration, Robotics and Computer-Integrated Manufacturing, Vol.23, No.6, pp.677-683, 2007.
[33] G.Q. Huang, Y.F. Zhang, and P.Y. Jiang, RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts, Robotics and Computer-Integrated Manufacturing, Vol.23, No.4, pp.469-477, 2007.
[34] H.K.H. Chow, K.L. Choy, W.B. Lee, and K.C. Lau, Design of a RFID case-based resource management system for warehouse operations, Expert Systems with Applications, Vol.30, No.4, pp.561-576, 2006.
[35] A.K. Parlikad and D. McFarlane, RFID-based product information in end-of-life decision making, Control Engineering Practice, Vol.15, No.11, pp.1348-1363, 2007.
[36] A. Lehto, J. Nummela, L. Ukkonen, L. Sydänheimo, and M. Kivikoski, Passive UHF RFID in paper industry: Challenges, benefits and the application environment, IEEE Transactions on Automation Science and Engineering, Vol.6, No.1, pp.66-79, 2009.
[37] Y. Sato, J. Mitsugi, O. Nakamura, and J. Murai, Theory and performance evaluation of group coding of RFID tags, IEEE Transactions on Automation Science and Engineering, Vol.9, No.3, pp.458-466, 2012.
[38] N. Ur-Rahman and J.A. Harding, Textual data mining for industrial knowledge management and text classification: A business oriented approach, Expert Systems with Applications, Vol.39, Iss.5, pp.4729-4739, 2012.
[39] N. Aggarwal, A. Kumar, H. Khatter, and V. Aggarwal, Analysis the effect of data mining techniques on database, Advances in Engineering Software, Vol.47, Iss.1, pp.164-169, 2012.
[40] C.K.H. Lee, K.L. Choy, G.T.S. Ho, K.S. Chin, K.M.Y. Law, and Y.K. Tse, A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry, Expert Systems with Applications, Vol.40, Iss.7, pp.2435-2446, 2013.
[41] B. Kamsu-Foguem, F. Rigal, and F. Mauget, Mining association rules for the quality improvement of the production process, Expert Systems with Applications, Vol.40, Iss.4, pp.1034-1045, 2013.
[42] L. Rokach and D. Hutter, Automatic discovery of the root causes for quality drift in high dimensionality manufacturing processes, Journal of Intelligent Manufacturing, Vol.23, Iss.5, pp.1915-1930, 2012.
[43] N. Ruschin-Rimini, O. Maimon, and R. Romano, Visual analysis of quality-related manufacturing data using fractal geometry, Journal of Intelligent Manufacturing, Vol.23, Iss.3, pp.481-495, 2012.
[44] C. Ciflikli and E. Kahya-Ozyirmidokuz, Enhancing product quality of a process, Industrial Management &; Data Systems, Vol.112, Iss.8-9, pp.1181-1200, 2012.
[45] Y. Lv, C.K.M. Lee, H.K. Chan, and W.H. Ip, RFID-based colored Petri net applied for quality monitoring in manufacturing system, International Journal of Advanced Manufacturing Technology, Vol.60, Iss.1-4, pp.225-236, 2012.
[46] W.N. Liu, L.J. Zheng, D.H. Sun, X.Y. Liao, M. Zhao, J.M. Su, and Y.X. Liu, RFID-enabled real-time production management system for Loncin motorcycle assembly line, International Journal of Computer Integrated Manufacturing, Vol.25, Iss.1, pp.86-99, 2012.
[47] S.H. Gwon, S.C. Oh, N. Huang, and S.K. Hong, Advanced RFID application for a mixed-product assembly line, International Journal of Advanced Manufacturing Technology, Vol.56, Iss.1-4, pp.377-386, 2011.
[48] J. Lyu, S.Y. Chang, and T.L. Chen, Integrating RFID with quality assurance system – Framework and applications, Expert Systems with Applications, Vol.36, Iss.8, pp.10877-10882, 2009.
[49] R. Narasimhan and T. Schoenherr, The effects of integrated supply management practices and environmental management practices on relative competitive quality advantage, International Journal of Production Research, Vol.50, Iss.4, pp.1185-1201, 2012.
[50] S. Gebus and K. Leiviska, Knowledge acquisition for decision support systems on an electronic assembly line, Expert Systems with Applications, Vol.36, Iss.1, pp.93-101, 2009.
[51] J. Jiang, J. Cheng, and D. Tao, Color biological features-based solder paste defects detection and classification on printed circuit boards, IEEE Transactions on Components Packaging and Manufacturing Technology, Vol.2, Iss.9, pp.1536-1544, 2012.
[52] T.N. Tsai, Development of a soldering quality classifier system using a hybrid data mining approach, Expert Systems with Applications, Vol.39, Iss.5, pp.5727-5738, 2012.
[53] T. Cang, E.S. Pan, and M.X. Zhang, Optimization study of reflow soldering profile for surface mount technology, Proceedings of the International Conference on Computer Science and Network Technology, Vol.1-4, pp.1772-1775, 2012.
[54] G. Acciani, G. Fornarelli, and A. Giaquinto, A fuzzy method for global quality index evaluation of solder joints in surface mount technology, IEEE Transactions on Industrial Informatics, Vol.7, Iss.1, pp.115-124, 2011.
[55] A. Giaquinto, G. Fornarelli, and G. Brunetti, A neurofuzzy method for the evaluation of soldering global quality index, IEEE Transactions on Industrial Informatics, Vol.5, Iss.1, pp.56-66, 2009.
[56] T.N. Tsai, Modeling and optimization of stencil printing operations: A comparison study, Computers &; Industrial Engineering, Vol.54, Iss.3, pp.374-389, 2008.
[57] S.C. Lo and B.M. Li, The design of a quality control system of PCB with SMT based on RFID and AOI, Proceedings of the 8th International Conference on Intelligent Systems Design and Applications, Vol.1, pp.649-653, 2008.
[58] Y.H. Hung, Optimal process parameters design for a wire bonding of ultra-thin CSP package based on hybrid methods of artificial intelligence, Microelectronics International, Vol.24, Iss.3, pp.3-10, 2007.
[59] G. Acciani, G. Brunetti, and G. Fornarelli, Application of neural networks in optical inspection and classification of solder joints in surface mount technology, IEEE Transactions on Industrial Informatics, Vol.2, Iss.3, pp.200-209, 2006.
[60] T. Yang, T.N. Tsai, and J.W. Yeh, A neural network-based prediction model for fine pitch stencil-printing quality in surface mount assembly, Engineering Applications of Artificial Intelligence, Vol.18, Iss.3, pp.335-341, 2005.
[61] M.E. Zervakis, S.K. Goumas, and G.A. Rovithakis, A Bayesian framework for multilead SMD post-placement quality inspection, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, Vol.34, Iss.1, pp.440-453, 2004.
[62] A. Schaller, T. Tirpak, and W.M. Xiao, Reference control model for predicting screen printer quality, Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-age, pp.1977-1981, 2002.