請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70889
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張瑞益(Ray-I Chang) | |
dc.contributor.author | Jia-Ying Lin | en |
dc.contributor.author | 林佳穎 | zh_TW |
dc.date.accessioned | 2021-06-17T04:42:33Z | - |
dc.date.available | 2018-08-15 | |
dc.date.copyright | 2018-08-15 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-03 | |
dc.identifier.citation | REFERENCES
[1] Wang, S., Wan, J., Li, D., & Zhang, C. (2016). Implementing Smart Factory of Industrie 4.0: An Outlook. International Journal of Distributed Sensor Networks, 12(1), 3159805. doi:10.1155/2016/3159805 [2] Xu, L. D., He, W., & Li, S. (2014). Internet of Things in Industries: A Survey. IEEE Transactions on Industrial Informatics, 10(4), 2233-2243. [3] Aydin, G., Hallac, I. R., & Karakus, B. (2015). Architecture and Implementation of a Scalable Sensor Data Storage and Analysis System Using Cloud Computing and Big Data Technologies. Journal of Sensors, 1-11. doi:10.1155/2015/834217 [4] Agarwal, R., & Dhar, V. (2014). Editorial—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research. Information Systems Research, 25(3), 443-448. doi:10.1287/isre.2014.0546 [5] Davidson, S. (2017). Cyber-Physical System Design With Sensor Networking Technologies. IEEE Design & Test, 34(3), pp.105-107. [6] Me, D. (2013). Warping Parameters Influence on Warp Yarns Properties: Part 1: Warping Speed and Warp Yarn Tension. Journal of Textile Science & Engineering, 03(02). doi:10.4172/2165-8064.1000132 [7] Kovačević, S., & Schwarz, I. (2015). Weaving Complex Patterns — From Weaving Looms to Weaving Machines. Cutting Edge Research in Technologies. doi:10.5772/61091 [8] Pathak, H., & A, P. (2014). Applications and Prospects of Microbial Polymers in Textile Industries. Journal of Textile Science & Engineering, 04(06). doi:10.4172/2165-8064.1000172 [9] Gloy Y. S., Renkens W., Herty M., Gries T. (2015). Simulation and Optimisation of Warp Tension in the Weaving Process. Journal of Textile Science & Engineering, 05(01). doi:10.4172/2165-8064.1000179 [10] Karnoub A., Kadi N., Azari Z., Bakeer E. S. (2015). Find the Suitable Warp Tension to get the Best Resistance for Jacquard Fabric. Journal of Textile Science & Engineering, 05(06). doi:10.4172/2165-8064.1000222 [11] Junejo, K. N., & Goh, J. (2016). Behaviour-Based Attack Detection and Classification in Cyber Physical Systems Using Machine Learning. Proceedings of the 2nd ACM International Workshop on Cyber-Physical System Security - CPSS 16. doi:10.1145/2899015.2899016 [12] Bullón Pérez J., González Arrieta A., HernándezEncinas A., Queiruga-Dios A. (2017) Industrial Cyber-Physical Systems in Textile Engineering. In: Graña M., López-Guede J., Etxaniz O., Herrero Á., Quintián H., Corchado E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO 2016, ICEUTE 2016, CISIS 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham [13] Saggiomo, M., Kemper, M., Gloy, Y., & Gries, T. (2016). Weaving machine as cyber-physical production system: Multi-objective self-optimization of the weaving process. 2016 IEEE International Conference on Industrial Technology (ICIT). doi:10.1109/icit.2016.7475090 [14] Heshmaty, B., & Kandel, A. (1985). Fuzzy linear regression and its applications to forecasting in uncertain environment. Fuzzy Sets and Systems, 15(2), 159-191. doi:10.1016/0165-0114(85)90044-2 [15] Sellam, V., & Poovammal, E. (2016). Prediction of Crop Yield using Regression Analysis. Indian Journal of Science and Technology, 9(38). [16] Wang, S., Ji, B., Zhao, J., Liu, W., & Xu, T. (2017). Predicting ship fuel consumption based on LASSO regression. Transportation Research Part D: Transport and Environment. doi:10.1016/j.trd.2017.09.014 [17] Balamurugan, P. (2013). Large-Scale Elastic Net Regularized Linear Classification SVMs and Logistic Regression. 2013 IEEE 13th International Conference on Data Mining. doi:10.1109/icdm.2013.126 [18] Vigneau, E., Devaux, M. F., Qannari, E. M., & Robert, P. (1997). Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration. Journal of Chemometrics, 11(3), 239-249. [19] Chang, R., Hsu, H., Lin, S., Chang, C., & Ho, J. (2017). Query-Based Learning for Dynamic Particle Swarm Optimization. IEEE Access, 5, 7648-7658. [20] Chang, R., Lin, S. and Hung, Y. (2012). Particle swarm optimization with query-based learning for multi-objective power contract problem. Expert Systems with Applications, 39(3), pp.3116-3126. [21] Lai, L., Chang, R., & Kouh, J. (2005). Mining Data by Query-Based Error-Propagation. Lecture Notes in Computer Science Advances in Natural Computation, 1224-1233. doi:10.1007/11539087_162 [22] Chang, R. (2005). Disease Diagnosis Using Query-Based Neural Networks. Advances in Neural Networks – ISNN 2005 Lecture Notes in Computer Science, 767-773. doi:10.1007/11427469_122 [23] Chang, R., Lo, C., Su, W., & Wang, J. (2006). Query-Based Learning Decision Tree and its Applications in Data Mining. Proceedings of the 9th Joint Conference on Information Sciences (JCIS). doi:10.2991/jcis.2006.49 [24] Chang, R., Huang, C., Lai, L., & Lee, C. (2018). Query-Based Machine Learning Model for Data Analysis of Infrasonic Signals in Wireless Sensor Networks. Proceedings of the 2nd International Conference on Digital Signal Processing - ICDSP 2018. doi:10.1145/3193025.319303 [25] Pudi, V., & Krishna, R. P. (2009). Data mining concepts and techniques. Oxford: Oxford University Press. [26] Chen, Y., Ip, H. H., Li, S., & Wang, G. (2009). Discovering hidden knowledge in data classification via multivariate analysis. Expert Systems, 27(2), 90-100. [27] https://inanalysis.github.io/ [28] Li Peng Enterprise Co., Ltd. http://www.lealeagroup.com.tw/?crc=0_2516544_2516543 [29] R, B. S., & Sheshadri, H. (2014). An approach to preprocess data in the diagnosis of Alzheimers disease. Proceedings of 2014 International Conference on Cloud Computing and Internet of Things. doi:10.1109/cciot.2014.7062522 [30] Zhang, Z., He, H., & Zhou, N. (2017). A neural network-based method with data preprocess for fault diagnosis of drive system in battery electric vehicles. 2017 Chinese Automation Congress (CAC). doi:10.1109/cac.2017.8243504 [31] Deignan, P., Meckl, P., Franchek, M., Abraham, J., & Jaliwala, S. (2000). Using mutual information to pre-process input data for a virtual sensor. Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334). [32] Greenwood-Nimmo, M., & Shields, K. (2017). An Introduction to Data Cleaning Using Internet Search Data. Australian Economic Review, 50(3), 363-372. doi:10.1111/1467-8462.12235 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70889 | - |
dc.description.abstract | 在工業4.0的浪潮之下,傳統紡織工廠進行智慧化轉型是維持競爭力的重要關鍵。智慧化轉型其中一項關鍵技術便是虛實整合系統。虛實整合系統將產業鏈中的每一個實體元素在虛擬端產生一個鏡像模型 (Cyber Twin),達到對實體元素行為進行預測與管理的目的。虛實整合系統需具備自省性、自比較性,意即對自身狀態變化有所意識並且比較類似實體元素進行預測,更重要的是「自重構性」,根據系統目標進行自我強化,建構出能反映實體狀態的鏡像虛擬模型。
為實現織布機台參數設定智慧化這個目的,本研究目標有兩項,第一項為開發一套雲端資料分析系統InAnalysis。第二項將針對織布段機台參數設定以資料分析模型為基礎進行參數預測智慧化。從資料科學的角度,整合織造過程中準備段與織布段的歷史數據,經由機器學習回歸演算法,建構出能反映實體狀態的織布機台參數預測模型,並且透過十摺交叉驗證法(10-fold Cross Validation)了解模型表現,達到虛實整合系統所需的「自省性」、「自比較性」。進一步導入詢問式學習的概念,透過添加學習獲得的新案例資料,進而有效地強化整體虛擬預測模型的表現,得到更好的現實策略,這即為虛實整合系統最重要的特質「自重構性」。實驗結果顯示,基本織布機台操作參數回歸預測模型的MSE(Mean Square Error)僅0.000165。並且能透過詢問式學習強化織布參數預測模型以及驗布品質預測模型。 未來將用InAnalysis的API模組設計一套紡織廠的機台操作參數推薦系統(Operation Parameter Recommendation System, OPRS)。透過資料生成的機器學習預測模型,協助技術人員設定操作參數,將整個製程的決策過程智慧化。並解決紡織技術人才不斷老化,經驗難以傳承等問題。更進一步,將該系統串聯至整個紡織產業上、中、下游的決策系統,將「接單」、「研發設計」、「供應」、「生產」、「檢驗」、「出貨」、「銷售」等服務全部由虛實整合系統進行高效率的智慧化管理與決策。 | zh_TW |
dc.description.abstract | In Industry 4.0, the intelligent transformation of traditional textile factories is important to maintain competitiveness. One of the key technologies for smart transformation is the cyber-physical system. The cyber-physical system will generate the cyber twin on the virtual end for each entity element in the industrial chain to achieve the purpose of predicting and managing the behavior of the entity element. The cyber-physical system needs to be self-examination and self-comparative, meaning that it is aware of changes in its own state and more similar entity elements to make predictions. More importantly, it is 'self-reconfigurable' and self-enhanced according to system goals.
In order to achieve the goal of setting the loom parameters intelligently, this study has two objectives. First one is to development a cloud data analysis system, InAnalysis. The second one will be build a parameters prediction model for the weaving process. From the perspective of data science, the machine learning regression algorithm is used to build a loom parameter prediction model, and 10-fold cross validation is used to understand the performance of the model. This achieved the 'self-introspection' and 'self-comparison' required by cyber-physical system. Also, the concept of query-based learning is used. And the performance of the prediction model can be effectively enhanced and a better realistic strategy can be obtained. This is the most important feature of the cyber-physical system, self-reconfigurable. The experimental results show that the MSE (Mean Square Error) of the prediction model is only 0. 000165. Moreover, the performance of the parameter prediction model and quality prediction model can be reinforced through query-based learning. In the future, InAnalysis's API will be used in an operation parameter recommendation system (OPRS) and weaving process of decision-making will become intelligent. Further, the system could implement into entire textile industry. Such as, 'orders,' 'R&D,' 'supplies,' 'production,' 'inspection,' 'shipments,' and 'sales.' All other services are managed efficiently and intelligently by the cyber-physical system. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T04:42:33Z (GMT). No. of bitstreams: 1 ntu-107-R05525122-1.pdf: 2352154 bytes, checksum: 6ea0fbda35724e7242735a54b20bd83c (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 viii Chapter 1 緒論 1 1.1 研究背景 1 1.1.1 臺灣紡織產業 1 1.1.2 工業4.0 2 1.2 研究動機 4 1.2.1 紡織廠現況與問題 4 1.2.2 智慧紡織工廠 6 1.3 問題定義 7 1.4 研究目標與貢獻 7 Chapter 2 文獻探討 9 2.1 紡織產業價值鏈 9 2.2 虛實整合系統 10 2.3 機器學習回歸演算法 11 2.4 詢問式學習 14 Chapter 3 研究方法 15 3.1 研究架構 15 3.1.1 InAnalysis資料分析系統開發流程 16 3.1.2 資料蒐集 17 3.1.3 資料前處理 18 3.1.4 特徵工程 19 3.1.5 模型訓練與製作API 20 Chapter 4 實驗方法與結果 22 4.1 模型評估方法 22 4.2 準備訓練資料 23 4.2.1 資料蒐集 23 4.2.2 資料前處理 23 4.2.3 特徵工程 25 4.3 模型訓練實驗結果 27 4.3.1 混紡特徵工程實驗 27 4.3.2 織布參數預測模型與驗布品質預測模型 28 4.3.3 利用對接模型進行相互驗證 30 4.3.4 導入詢問式學習強化預測模型 31 Chapter 5 結論 34 5.1 研究價值 34 5.2 未來發展 34 REFERENCES 36 | |
dc.language.iso | zh-TW | |
dc.title | 利用機器學習方法預測織布製程參數於虛實整合系統 | zh_TW |
dc.title | Parameter Prediction for Weaving Process in Cyber-Physical Systems Using Machine Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蔡進發(Jing-Fa Tsai),張信宏(Shin-Hung Chang),蕭宇程(Yu-Cheng Hsiao) | |
dc.subject.keyword | 機器學習,工業 4.0,虛實整合系統,詢問式學習,參數預測, | zh_TW |
dc.subject.keyword | Machine Learning,Industry 4.0,Cyber-Physical System,Query-based Learning,Parameter Prediction, | en |
dc.relation.page | 38 | |
dc.identifier.doi | 10.6342/NTU201802481 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-06 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-107-1.pdf 目前未授權公開取用 | 2.3 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。