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系統識別號 U0026-1908202015294800
論文名稱(中文) 基於機器學習之食品供應鏈異常偵測方法與技術研發
論文名稱(英文) Development of Machine Learning based Method and Technology for Food Supply Chain Anomaly Detection
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所
系所名稱(英) Institute of Manufacturing Information and Systems
學年度 108
學期 2
出版年 109
研究生(中文) 李俊賢
研究生(英文) Jyun-Sian Li
學號 P96074121
學位類別 碩士
語文別 中文
論文頁數 82頁
口試委員 指導教授-陳裕民
共同指導教授-陳宗義
口試委員-陳育仁
中文關鍵字 食品供應鏈  食品安全  食安監控與管理  安全食品防護  資料科學  機器學習  區塊鏈 
英文關鍵字 Food supply chain  Food safety  Food safety monitoring and management  Safe food protection  Data Science  Machine Learning  Blockchain 
學科別分類
中文摘要 食品供應鏈的複雜化導致食安事件頻繁發生,不僅造成社會不安,也直接或間接危害人們健康與生活,故食品安全的維護已成為世界各國重視之議題。隨著資料科學(Data Science)、機器學習(Machine Learning)與區塊鏈(Blockchain)的興起,系統智能化的理想已逐漸能夠實現,本研究參考資料科學之觀念與方法,以及機器學習與區塊鏈之技術,期為食品供應鏈之食品安全管理提供解方案,為人類帶來福祉。
本研究以資料科學之概念,設計一個「食安監控與管理模式」,針對「食安監控與管理模式」分析系統之需求,並參考區塊鏈之概念以及機器學習之原理,規劃「安全食品防護系統」之架構。依據該架構界定資料分析之需求、設計「食安稽查資料模型」,運用資料探勘(Data Mining)與機器學習技術,分析食品異常之影響因子與異常之模式,以開發與建置「異常偵測技術與機制」。
本研究以公開之供銷資料進行測試,驗證所提之技術的正確性與有效性。由於異常偵測之重點在將異常挑出越多越好,因此以召回率作為評估指標,最終供銷異常偵測模型之召回率由0.75027上升至0.86638。針對設備異常偵測,本研究同樣以公開之設備資料進行測試,模型之誤差率由0.006077下降至0.004112。上述之模型皆以評估指標進行評估,而評估指標能夠反映模型準確度,因此能夠驗證本研究之異常偵測方法與技術的有效性。
英文摘要 The complexity of the food supply chain leads to frequent food security incidents, which not only causes social unrest, but also directly or indirectly endangers people's health and life. Therefore, the maintenance of food safety has become an important factor for countries all over the world. With the rise of Data Science, Machine Learning and Blockchain, the ideal of system intelligence has gradually been realized. This study looks forward to solving food safety problems through the support of intelligent systems to thereby promote human welfare.

This study designed a "food safety monitoring and management model" based on the concept of data science. For this "food safety monitoring and management mode", the functional requirements of its system are analyzed, and the functional architecture of the "safe food protection system" is planned and designed with reference to the concept of blockchain and the principles of machine learning. According to the functional framework, define the needs of data analysis, design the "food safety inspection data model", use machine learning technology to analyze the impact factors and abnormal patterns of food anomalies to build anomaly detection mechanism.

In order to verify the validity and correctness, this study uses public data to detect. First, detect supply and sales anomalies. Since anomaly detection focuses on picking out as many anomalies as possible, the recall rate is used as an validation index. The final model's recall rate increases from 0.75027 to 0.86638. Then it detects the abnormality of the supplier's equipment, and also uses public data to detect the error rate of the model from 0.006077 to 0.004112. The above models are all evaluated with validation index, and the validation index can reflect the accuracy of the model, so it can verify the effectiveness of the anomaly detection methods and techniques in this study.
論文目次 摘要 I
誌謝 VI
目錄 VII
表目錄 X
圖目錄 XI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
1.4 研究問題分析 3
1.5 研究項目與方法 3
1.6 研究流程 5
第二章 文獻探討 6
2.1 研究領域探討 6
2.1.1 供應鏈 6
2.2.2 食品安全 7
2.2 相關技術探討 8
2.2.1 區塊鏈 8
2.2.2 機器學習 11
2.2.3 深度學習 13
2.2.4 採樣優化 14
2.2.5 超參數優化 15
2.3 類似研究探討 16
第三章 基於區塊鏈之安全食品供應鏈模型與防護系統設計 18
3.1 安全食品供應鏈模型 18
3.2 食品安全防護系統架構 19
3.2.1 系統架構 19
3.2.2 食品安全監控與管理模式 21
3.3 食品安全防護技術架構 22
第四章 安全食品資料分析與資料模型設計 25
4.1 供應網路模型 25
4.2 供應鏈工廠模型 26
4.3 食安稽查資料模型 30
第五章 食品供應鏈異常偵測方法設計與實現技術開發 32
5.1 異常偵測方法 32
5.2 供銷異常偵測方法 33
5.2.1 資料前處理 33
5.2.2 模型訓練 34
5.2.3 模型評估與優化 36
5.3 設備異常偵測技術 40
5.3.1 資料前處理 41
5.3.2 模型訓練 44
5.3.3 模型評估與優化 46
5.4 異常程度估計算法 47
第六章 實作與驗證 49
6.1 實作環境介紹 49
6.2 實驗過程與結果 49
6.2.1 供銷異常偵測 49
6.2.2 設備異常偵測 60
6.2.3 異常程度估計算法 67
第七章 結論與未來展望 72
7.1 結論 72
7.2 未來展望 72
參考文獻 74
參考文獻 Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European journal of Operational research, 196(1), 1-20.
Alfian, G., Rhee, J., Ahn, H., Lee, J., Farooq, U., Ijaz, M. F., &Syaekhoni, M. A. (2017). Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering, 212(Supplement C), 65–75.
Anirudh, M., Thileeban, S. A., & Nallathambi, D. J. (2017, January). Use of honeypots for mitigating DoS attacks targeted on IoT networks. In 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), (pp. 1-4).
Assefa, T. T., Meuwissen, M. P., & Lansink, A. G. O. (2017). Price risk perceptions and management strategies in selected European food supply chains: An exploratory approach. NJAS-Wageningen Journal of Life Sciences, 80, 15-26.
Ayoub, M. (2020). A review on machine learning algorithms to predict daylighting inside buildings. Solar Energy, 202, 249-275.
Azzi, R., Chamoun, R. K., & Sokhn, M. (2019). The power of a blockchain-based supply chain. Computers & industrial engineering, 135, 582-592.
Basnayake, B. M. A. L., & Rajapakse, C. (2019, March). A Blockchain-based decentralized system to ensure the transparency of organic food supply chain. In 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE) (pp. 103-107).
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(Feb), 281-305.
Betrò, B. (1991). Bayesian methods in global optimization. Journal of Global Optimization, 1(1), 1-14.
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602-613.
Bishop, C. M. (2016). Pattern Recognition and Machine Learning. New York, United States: Springer Publishing.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
Chen, Z., Yan, Q., Han, H., Wang, S., Peng, L., Wang, L., & Yang, B. (2018). Machine learning based mobile malware detection using highly imbalanced network traffic. Information Sciences, 433, 346-364.
Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and electronics in agriculture, 151, 61-69.
Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the internet of things. Ieee Access, 4, 2292-2303.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE transactions on neural networks and learning systems, 29(8), 3784-3797.
Di Pierro, M. (2017). What is the blockchain?. Computing in Science &Engineering, 19(5), 92-95.
Diabat, A., Govindan, K., & Panicker, V. V. (2012). Supply chain risk management and its mitigation in a food industry. International Journal of Production Research, 50(11), 3039-3050.
Dinh, T. T. A., Liu, R., Zhang, M., Chen, G., Ooi, B. C., & Wang, J. (2018). Untangling blockchain: A data processing view of blockchain systems. IEEE Transactions on Knowledge and Data Engineering, 30(7), 1366-1385.
Dinh, T. N., & Thai, M. T. (2018). Ai and blockchain: A disruptive integration. Computer, 51(9), 48-53.
Dorri, A., Steger, M., Kanhere, S. S., & Jurdak, R. (2017). Blockchain: A distributed solution to automotive security and privacy. IEEE Communications Magazine, 55(12), 119-125.
Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. John Wiley & Sons.
Fraga-Lamas, P., & Fernández-Caramés, T. M. (2019). A review on blockchain technologies for an advanced and cyber-resilient automotive industry. IEEE Access, 7, 17578-17598.
Freise, M., & Seuring, S. (2015). Social and environmental risk management in supply chains: a survey in the clothing industry. Logistics Research, 8(1), 2.
Fernández-Caramés, T. M., & Fraga-Lamas, P. (2018). A Review on the Use of Blockchain for the Internet of Things. IEEE Access, 6, 32979-33001.
Gaukler, G. M., Özer, Ö., & Hausman, W. H. (2008). Order progress information: Improved dynamic emergency ordering policies. Production and Operations Management, 17(6), 599-613.
George, R. V., Harsh, H. O., Ray, P., & Babu, A. K. (2019). Food quality traceability prototype for restaurants using blockchain and food quality data index. Journal of Cleaner Production, 240, 118021.
Gori, M. (2017). Machine Learning: A constraint-based approach. Morgan Kaufmann.
Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649).
Guan, G. F., Dong, Q. L., & Li, C. H. (2011, September). Risk identification and evaluation research on F-AHP evaluation based supply chain. In 2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management (pp. 1513-1517).
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80.
Hobbs, J. E. (2004). Information asymmetry and the role of traceability systems. Agribusiness: An International Journal, 20(4), 397-415.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Hochreiter, S. (1998). The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(2), 107-116.
ISO, I. (2018). 22000: Food safety management systems—Requirements for any organization in the food chain. International Standard, 1-48.
Jackson, J. E. (2005). A user's guide to principal components (Vol. 587). John Wiley & Sons.
Jiang, G., & Wang, W. (2017). Markov cross-validation for time series model evaluations. Information Sciences, 375, 219-233.
Jing, X., Dongjie, Z., & Zhongsu, M. (2009, December). The research on the BP neural network application in food supply chain risk management. In 2009 International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 1, pp. 545-548).
Jones, D. R. (2001). A taxonomy of global optimization methods based on response surfaces. Journal of global optimization, 21(4), 345-383.
Kabir, E., Guikema, S., & Kane, B. (2018). Statistical modeling of tree failures during storms. Reliability Engineering & System Safety, 177, 68-79.
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194.
Kiayias, A., Russell, A., David, B., & Oliynykov, R. (2017, August). Ouroboros: A provably secure proof-of-stake blockchain protocol. In Annual International Cryptology Conference (pp. 357-388). Springer, Cham.
Kirchgässner, W., Wallscheid, O., & Böcker, J. (2019, June). Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines. In 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE) (pp. 318-323).
Kirchgässner, W., Wallscheid, O., & Böcker, J. (2019, May). Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors. In 2019 IEEE International Electric Machines & Drives Conference (IEMDC) (pp. 1439-1446).
Kleinbaum, D. G., Dietz, K., Gail, M., Klein, M., & Klein, M. (2002). Logistic regression. New York: Springer-Verlag.
Krisztin, T. (2018). Semi-parametric spatial autoregressive models in freight generation modeling. Transportation Research Part E: Logistics and Transportation Review, 114, 121-143.
Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89.
Lee, H., & Özer, Ö. (2007). Unlocking the value of RFID. Production and operations management, 16(1), 40-64.
Lopez-Rojas, E. A., & Axelsson, S. (2014, September). BANKSIM: A bank payments simulator for fraud detection research. In 26th European Modeling and Simulation Symposium, EMSS (pp. 144-152).
Luo, H., Huang, M., & Zhou, Z. (2018). Integration of Multi-Gaussian fitting and LSTM neural networks for health monitoring of an automotive suspension component. Journal of Sound and Vibration, 428, 87-103.
Makhdoom, I., Abolhasan, M., Abbas, H., & Ni, W. (2019). Blockchain's adoption in IoT: The challenges, and a way forward. Journal of Network and Computer Applications, 125, 251-279.
Mehra, M., Saxena, S., Sankaranarayanan, S., Tom, R. J., & Veeramanikandan, M. (2018). IoT based hydroponics system using Deep Neural Networks. Computers and electronics in agriculture, 155, 473-486.
Mettler, M. (2016, September). Blockchain technology in healthcare: The revolution starts here. In 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 1-3). IEEE.
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010). Recurrent neural network based language model. In Eleventh annual conference of the international speech communication association, 1045-1048.
Mockus, J., Tiesis, V., & Zilinskas, A. (1978). The application of Bayesian methods for seeking the extremum. Towards global optimization, 2(2), 117-129.
Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT Security: Challenges and Solution using Machine Learning, Artificial Intelligence and Blockchain Technology. Internet of Things, 100227.
Morgan, T. R., Richey Jr, R. G., & Ellinger, A. E. (2018). Supplier transparency: Scale development and validation. The International Journal of Logistics Management, 29(3), 959-984.
Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S. S., Hosseinzadeh-Bandbafha, H., & Chau, K. W. (2017). Energy consumption enhancement and environmental life cycle assessment in paddy production using optimization techniques. Journal of cleaner production, 162, 571-586.
Nakamoto, S., & Bitcoin, A. (2008). A peer-to-peer electronic cash system. Bitcoin.–URL: https://bitcoin.org/bitcoin.pdf.
Ndraha, N., Hsiao, H. I., Vlajic, J., Yang, M. F., & Lin, H. T. V. (2018). Time-temperature abuse in the food cold chain: Review of issues, challenges, and recommendations. Food Control, 89, 12-21.
Nielsen, T. D., & Jensen, F. V. (2009). Bayesian networks and decision graphs. Springer Science & Business Media.
Óskarsdóttir, K., & Oddsson, G. V. (2019). Towards a decision support framework for technologies used in cold supply chain traceability. Journal of food engineering, 240, 153-159.
Pärssinen, M., Kotila, M., Rumin, R. C., Phansalkar, A., & Manner, J. (2018). Is Blockchain Ready to Revolutionize Online Advertising?. IEEE Access, 6, 54884-54899.
Pearson, K. (1895). VII. Note on regression and inheritance in the case of two parents. proceedings of the royal society of London, 58(347-352), 240-242.
Powers, D. M. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.
Rasmussen, C. E. (1997). Evaluation of Gaussian processes and other methods for non-linear regression (Doctoral dissertation, University of Toronto Toronto, Canada).
Ray, S. (2019, February). A Quick Review of Machine Learning Algorithms. In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 35-39).
Resende-Filho, M. A., & Hurley, T. M. (2012). Information asymmetry and traceability incentives for food safety. International Journal of Production Economics, 139(2), 596-603.
Santana, A., Fukuyama, Y., Murakami, K., & Matsui, T. (2016, November). Machine learning application for refrigeration showcase fault discrimination. In 2016 IEEE Region 10 Conference (TENCON) (pp. 10-13).
Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926.
Shirani, M., & Demichela, M. (2015). Integration of FMEA and human factor in the food chain risk assessment. International Journal of Social, Behavioural, Educational, Economic, Business and Industrial Engineering, 12, 4103-4106.
Smith, T. G., Chouinard, H. H., & Wandschneider, P. R. (2011). Waiting for the invisible hand: Novel products and the role of information in the modern market for food. Food Policy, 36(2), 239-249.
Sun, Q., & Tang, Y. (2014). The Literature Review of Food Supply Chain Risk Assessment. International Journal of Business and Social Science, 5(5), 198-202.
Sun, S., & Wang, X. (2019). Promoting traceability for food supply chain with certification. Journal of cleaner production, 217, 658-665.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information processing & management, 45(4), 427-437.
Szabo, N. (1994). Smart contracts. Unpublished manuscript.
Vaughan, G. (2018). Efficient big data model selection with applications to fraud detection. International Journal of Forecasting.
Vigneau, E., Courcoux, P., Symoneaux, R., Guérin, L., & Villière, A. (2018). Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception. Food Quality and Preference, 68, 135-145.
Wang, J., Li, M., He, Y., Li, H., Xiao, K., & Wang, C. (2018). A blockchain based privacy-preserving incentive mechanism in crowdsensing applications. IEEE Access, 6, 17545-17556.
Wang, J., & Yue, H. (2017). Food safety pre-warning system based on data mining for a sustainable food supply chain. Food Control, 73, 223-229.
Wang, J., Yue, H., &Zhou, Z. (2017). An improved traceability system for food quality assurance and evaluation based on fuzzy classification and neural network. Food Control, 79, 363–370.
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data mining and knowledge discovery, 18(1), 30-55.
Wilson, D. L. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics, (3), 408-421.
World Health Organization, R. O. for S.-E. A. (2015). Food safety: What you should know. Retrieved from http://www.searo.who.int/entity/world_health_day/2015/whd-what-you-should-know/en/#quality
Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26-40.
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., ... & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6, 35365-35381.
Xu, X., Cao, D., Zhou, Y., & Gao, J. (2020). Application of neural network algorithm in fault diagnosis of mechanical intelligence. Mechanical Systems and Signal Processing, 141, 106625.
Yao, C., Cai, D., Bu, J., & Chen, G. (2017). Pre-training the deep generative models with adaptive hyperparameter optimization. Neurocomputing, 247, 144-155.
Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology?—a systematic review. PloS one, 11(10), e0163477.
Zhao, F., Chen, L., Xia, T., Ye, Z., & Zheng, Y. (2019). Gas Turbine Exhaust System Health Management Based on Recurrent Neural Networks. Procedia CIRP, 83, 630-635.
Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352-375.
Zhou, Y., Han, M., Liu, L., He, J. S., & Wang, Y. (2018, April). Deep learning approach for cyberattack detection. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 262-267).
陳姿蓉. (2018). 智能化食品安全監控與管理系統設計. 成功大學製造資訊與系統研究所學位論文, 1-91.
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