|
大和有話說(2018年04月08日)。完整解析AI人工智慧:3大浪潮+3大技術+3大應用。大和有話說,取自https://dahetalk.com/
王慧君(2003)。馬氏-田口系統之特性探討及其於信用評分之應用。國立交通大學工業工程與管理系所,新竹市。
石渼華(2015年8月19日)。工業4.0互聯網加速產業變革邁向智能智造。企業通,取自http://mag.digiwin.biz/
尹相志(2006)。SQL Sever 2005資料採礦聖經。台北:學貫出版社。
吳泰宗(2007)。廠務作業自動化能力評估系統建構之研究。未出版之博士論文,輔仁大學國際創業與經營管理學系,新北市。
周歆凱、蘇喜、黃興進、蔡明足、翁林仲(2006)。運用決策樹技術探討及診病患醫療費用之消耗。台灣衛誌,25(5),430-439。
李逢嘉(2010)。特徵選取為基礎之複合分類預測模式-以信用資料為例。未出版之博士論文,國立清華大學工業工程與工程管理學系,新竹市。
袁國超(2015)。雲端運算與大數據對筆記型電腦的發展趨勢影響之研究。未出版之碩士論文,國立中興大學高階經理人碩士在職專班,台中市。
胡運祥(2015)。工業4.0 對筆記型電腦代工製造之影響分析。未出版之碩士論文,國立中興大學高階經理人碩士在職專班,台中市。
張仲銘(2008)。以馬氏田口系統設計服務品質量表之研究。未出版之碩士論文,私立中華大學科技管理研究所,新竹市。
黃國祥(2006)。自動化製造系統之設計方法論與物件導向塑模。未出版之碩士論文,私立明新科技大學工程管理研究所。新竹縣。
姜台林(2001)。整合式智慧型最佳化參數設計之研究。未出版之博士論文,國立交通大學工業工程與管理系,新竹市。
張斐章、張麗秋(2005)。類神經網路。台北:台灣東華書局股份有限公司。
張家瑋(2013)。雲端平台大數據資料庫研究-以報關訊息資料為例。未出版之碩士論文,龍華科技大學資訊管理系,桃園市。
薛友仁(2001)。整合機器學習方法於決策樹為基智慧型排程系統之研究。未出版之博士論文,國立交通大學工業工程與管理系,新竹市。
蘇朝墩(2002)。品質工程。台北市:中華民國品質學會出版。
Ang J., Goh C., Saldivar A., & Li Y. (2017). Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment. Energies, 10(5), 610.
Berry, M. & Linoff, G. (1997). Data Mining Techniques: For Marketing, Sales, and Customer Support. New York: John Wiley and Sons.
Bertot, J. C. & Choi, H. (2013). Big data and e-government: issues, policies, and recommendations. 14th Annual International Conference on Digital
Buenviaje, B., Bischoff, J.E., Roncace, R.A., Willy, C.J. (2016). Mahalanobis–Taguchi System to Identify Preindicators of Delirium in the ICU. IEEE Journal of Biomedical and Health Informatics, 20(4), 1205-1212.
Chen, L. F., Su, C. T., & Chen, M. H. (2009). A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process. IEEE Transactions on Electronics Packaging Manufacturing, 32(1), 1-8.
Choi, J. Y., Shin, J. W., & Lee, J. S. (2013). Strategic demand forecasts for the tablet PC market using the Bayesian mixed logit model and market share simulations. Behaviour & Information Technology, 32(11), 1177-1190.
Deng, W. J., Chen, W. C., & Pei, W. (2008). Back-propagation neural network based importance–performance analysis for determining critical service attributes. Expert Systems with Applications, 34(2), 1115-1125.
Ecer, F. (2013). Comparing the Bank Failure Prediction Performance of Neural Networks and Support Vector Machines: The Turkish Case. Ekonomska Istraživanja-Economic Research, 26(3), 81-98.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37.
Girtelschmid, S., Steinbauer, M., Kumar, V., Fensel, A., & Kotsis, G. (2013). Big Data in Large Scale Intelligent Smart City Installations. International Conference on Information Integration and Web-based Applications & Services, 428.
Gopalkrishnan, V., Steier, D., Lewis, H., & Guszcza, J. (2012). Big data, big business: bridging the gap. 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, 7-11.
Ha, S. H. & Park, S. C. (1998). Application of data mining tools to hotel data mart on the intranet for database marketing. Expert Systems With Applications, 15(1), 1-31.
Han, J., Fu, Y., & Tang, S. (1995). Advances of the DBlearn system for knowledge discovery in large databases. Proceedings of 1995 International Joint Conference on Artificial Intelligence (IJCAI’95), 2049-2050.
Hinton, G. E., Li, D., Dong, Y., Dahl G. E., Mohamed, A., Jaitly, N., et al., (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 82-97.
Hochreiter, S. & Schmidhuber, J. (1997). Long short-Term memory. Neural Comput, 9(8), 1735.
Hu, T., Li, P., Zhang, C., & Liu, R. (2013). Design and application of a real-time industrial Ethernet protocol under Linux using RTAI. Int J Comput Integr Manuf, 26(5), 429-39.
Huang, Z., Hu, T., Peng, C., Hou, M., & Zhang, C. (2016). Research and development of industrial real-time Ethernet performance testing system used for CNC system. Int J Adv Manuf Technol, 83(5–8), 1199-207.
Jobi-Taiwo, A. A. (2014). Data classification and forecasting using the Mahalanobis-Taguchi method. Master’s Thesis, 7248.
Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544(7648), 23-5.
Lalanda, P., Morand, D., & Chollet, S. (2017). Autonomic mediation middleware for smart manufacturing. IEEE Internet Comput, 21(1), 32-9.
Lee, Y. C., Hsiao, Y. C., Peng, C. F., Tsai, S. B., Wu, C. H., & Chen, Q. (2015). Using Mahalanobis–Taguchi system, logistic regression, and neural network method to evaluate purchasing audit quality. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(S1), 3-12.
Lee, Y. T., Kumaraguru, S., Jain, S., Hatim, Q., Robinson, S., Helu, M., et al. (2017). A classification scheme for smart manufacturing systems’ performance metrics. Smart Sustain Manuf Syst, 1(1) ,52-74.
Lécun, Y., Bottou, L., Bengio, Y., & Haffner, P.. (1998). Gradient-based learning applied to document recognition. Proc IEEE, 86(11), 2278-324.
Li, D. C., Chang, C. C., Liu, C. W., & Chen, W. C. (2013). A new approach for manufacturing forecast problems with insufficient data: the case of TFT–LCDs. Journal of Intelligent Manufacturing, 24(2), 225-233.
Chen, L. J., Tsai, S. B., & Wang, C. K. (2014). The Competitive Strategies of a Green Energy Company. The 4th International Conference Energy, Environment and Sustainable Development (EESD 2014). EI Index. Conference paper.
Lu, Y., Xu, X., & Xu, J. (2014). Development of a hybrid manufacturing cloud. J Manuf Syst, 33(4), 551-66.
McCulloch, W. S. & Pitts, W. (1943). A logical calculus of ideas immanent in nervous activity.Bull. Math.Biophys. 5, 115-133
Putnik, G., Sluga, A., ElMaraghy, H., Teti, R., Koren, Y., Tolio, T., et al. (2013). Scalability in manufacturing systems design and operation: state-of-the-art and future developments roadmap. CIRP Ann Manuf Technol, 62(2), 751-74.
Quinn, T. S., Flesher, T. K., Johnson, J. D., & Flesher, F. L. (2014). Neural Networks: An Interdisciplinary Tax Research Methodology. Journal of Accounting and Financel, 14(1), 51-74.
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386-408.
Smart Manufacturing Coalition. Manufacturing growth continues despite uncertain economy, according to ASQ outlook survey. (2013). https://smartmanufacturingcoalition.org/sites/default/files/12.16.13manufacturing outlook survey.pdf. [Accessed 10 Sepember 2017].
Su, C. T., Chou, C. J., Hung, S. H., & Wang, P. C. (2012). Adopting the Healthcare Failure Mode and Effect Analysis to Improve the Blood Transfusion Processes. International Journal of Industrial Engineering, 19(8), 320-329.
Su, C. T. & Hsiao, Y. H. (2007). An evaluation of the robustness of MTS for imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 19(10), 1321-1332.
Taguchi, G. & Jugulum, R.(2000). New trends in multivariate diagnosis. The IndianJournal of Statistics, 62, 233-248
Taguchi, G. & Jugulum, R. (2002). The Mahalanobis-Taguchi Strategy: A Pattern Technology System, John Wiley & Sons, Inc.
Taguchi. G., Chowdhury. S., & Wu. Y. (2001). The Mahalanobis-Taguchi System, New York, Mcgraw-Hill.
Taketoshi, R., Akihito, S. J., Kenji, O., & Hajime, T. (2005). The yield enhancement methodology for invisible defects using the MTS method. Transactions on Semiconductor Manufacturing, 18, 561-568.
Tao, F. & Qi, Q. (2017). New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Trans Syst Man Cybern Syst, 99, 1-11.
Tian, X. H. & Pu, Y. J. (2008). An artificial neural network approach to hotel employee satisfaction: The case of china. Social Behavior and Personality, 36(4), ProQuest Science Journals, 467.
Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. J Manuf Syst, 37, 517-27.
Wang, P., Gao, R. X., & Fan, Z. (2015). Cloud computing for cloud manufacturing: benefits and limitations. J Manuf Sci Eng, 137, 1-10.
Wei, C., Piramuthu, S., & Shaw, M. J. (2003). Knowledge Discovery and Data Mining. 2, 157-189, Berlin, Germany: Springer-Verlag.
White, T. (2012). Hadoop: The Definitive Guide, 3rd Edition, CA: O'Reilly Media Yahoo Press.
Wu, D., Rosen, D. W., & Schaefer, D. (2015). Cloud-based design and manufacturing: status and promise. Comput Aided Des, 59, 1-14.
Xue, Z., Shen, G., Li J., Xu Q., Zhang, Y., & Shao J. (2012). Compression aware I/O performance analysis for big data clustering. 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, 45-52.
Ye, Y., Hu, T., Zhang, C., & Luo, W. (2016). Design and development of a CNC machining process knowledge base using cloud technology. Int J Adv Manuf Technol, 1-13.
Yeh, C. P. & Chang, W. H. (2014). Neural Network Forecasts of Taiwan Bureau of National Health Insurance Expenditures. The International Journal of Business and Finance Research, 8(5), 95-114.
Zhang, G. P., Patuwo, E. P., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14, 35-62.
Zhang, J., Li, T., & Pan Y. (2012). Parallel rough set based knowledge acquisition using Map/Reduce from big data. 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, 20-27.
|