請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85090完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴怡吉 (Alexander I-Chi Lai) | |
| dc.contributor.author | Hung-Chih Yang | en |
| dc.contributor.author | 楊閎智 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:43:00Z | - |
| dc.date.copyright | 2022-08-18 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-11 | |
| dc.identifier.citation | 1. 魏文郡, “網購市場順勢躍升新高，成長率優於整體零售業” 產業經濟統計簡訊第398期，2022年2月15日，URL: https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=9&html=1&menu_id=18808&bull_id=9673 2. Internet Crime Complaint Center, “Internet Crimt Report 2021,” Federal Bureau of Investigation, 23 Mar. 2022. Available at: https://www.ic3.gov/Media/PDF/AnnualReport/2021_IC3Report.pdf. 3. Alexandra Dimitropoulou, “Economy Rankings: Largest countries by GDP, 2022,” CEOWORLD magazine, 31 Mar. 2022, URL: https://ceoworld.biz/2022/03/31/economy-rankings-largest-countries-by-gdp-2022/ 4. 警政署統計室, “警政統計通報.” 內政部警政署全球資訊網, 2021年第11週 & 2022年第18週。 5. Tayeb Basta and Rami N.K. Salahat, “A Smart Security System for Accessing Web Services.” 2019 International Conference on Digitization (ICD), Sharjah, United Arab EmiRatios, pp. 183-186, Nov. 2019. doi: 10.1109/ICD47981.2019.9105752. 6. K. Priya, T. Venkaiah Naidu and R. Vamsi Krishna, “An Advanced InFormation Security System Using Image Based Graphical Password Scheme.” 2018 2nd International Conference on Trends in Electronics and InFormatics (ICOEI), Tirunelveli, pp. 304-307, May 2018. doi: 10.1109/ICOEI.2018.8553816. 7. 顧宜錚, 李家瑩, 黃相翎, “有服務品質就足夠嗎？顧客體驗對網站滿意度之影響.” 中山管理評論 2013年九月號, 第21卷第3期, p.479-509, doi: 10.6160/2013.09.01. 8. 林信成, “日誌分析(Log analysis).” 2012 圖書館學與資訊科學大辭典. 國家教育研究院 雙語詞彙、學術名詞暨辭書資訊網, URL: https://terms.naer.edu.tw/detail/1679029/ 9. 昭君, “特稿/一念之隔 「卡神」面前 銀行變「卡奴」.” 星島環球網. 2006-04-13網際網路檔案館WebArchive的存檔，存檔日期2007-12-15, URL: https://Web.archive.org/Web/20071215004504/http://www.singtaonet.com:82/global/chinafin/t20060413_189804.html . 10. Samuelson William and Richard ZeckhaUser, “Status quo bias in decision making.” Journal of risk and uncertainty 1.1, pp. 7-59, Mar. 1988. doi: 10.1007/BF00055564. 11. Amos Tversky and Daniel Kahneman, “Loss Aversion in Riskless Choice: A Reference- Dependent Model', The Quarterly Journal of Economics, Volume 106, Issue 4, pp. 1039-1061, Nov. 1991. doi: 10.2307/2937956. 12. Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk.” Econometrica, vol. 47, no. 2, [Wiley, Econometric Society], pp. 263–91, Mar. 1979. doi: 10.2307/1914185. 13. Dennis W. Rook, “The buying impulse.” Journal of Consumer Research, Volume 14, Issue 2, pp. 189-199, Sep. 1987. doi: 10.1086/209105. 14. Seounmi Youn and Ronald J. Faber, “Impulse buying: its relation to personality traits and cues.”, Advances in Consumer Research in North American Advances, Volume 27, pp. 179-185, Jan.2000. 15. Verplanken, Bas and Astrid Herabadi, “Individual Differences in Impulse Buying Tendency: Feeling and No Thinking.” European Journal of Personality, vol. 15, no. 1_suppl, pp. S71-S83, Nov. 2001. doi:10.1002/per.423. 16. Anna S. Mattila and Jochen Wirtz, “The role of store environmental stimulation and social factors on impulse purchasing.” Journal of Services Marketing, Volume 22, No. 7, pp. 562-567, Oct. 2008. doi:10.1108/08876040810909686. 17. ROvert Donovan, John R. Rossiter, Gilian Marcoolyn and Andrew Nesdale, 'Store atmosphere and purchasing Behavior.” Journal of retailing, Volume 70, Issue 3, pp. 283-294, Autumn 1994. doi:10.1016/0022-4359(94)90037-X. 18. Roy F. Baumeister, “Yielding to temptation: Self-control failure, impulsive purchasing, and consumer Behavior.” Journal of consumer research, Volume 28, Issue 4, pp. 670–676, Mar. 2002. doi:10.1086/338209. 19. Robert A. Mittelstaedt and Daniel J. Boorstin, “ The Americans: The Democratic Experience.” Journal of Marketing, volume 39, no. 1, pp. 117–118, Jan. 1975. doi: 10.2307/1250816. 20. Barbara E. Kahn and David C. Schmittlein, “The relationship between purchases made on promotion and shopping trip Behavior.” Journal of Retailing, Volume 68, Issue 3, pp. 294-315, Fall 1992. 21. Robert K. Merton, “Science and the social order.” Philosophy of science, Volume 5, No. 3, pp. 321-337, Jul. 1938. JSTOR, http://www.jstor.org/stable/184838 . 22. 林山田, 林東茂, 林燦璋. 犯罪學, 增訂4版, 三民書局, 民國 96 年 10 月, ISBN: 978-9-571-44891-6. 23. Ulrich SIEBER, Computerkriminalität Und Strafrecht. Heymanns, Carl; 2., Aufl. edition, Jan. 1980, ISBN: 3452188442. 24. Donn B. Parker, Crime by computer. New York: Scribner, 1976. pp. 44. 25. Donn B. Parker, Susan Nycum and S. Stephen Oüra. Computer abuse. Menlo Park, California: Stanford Research Institute, 1973. 26. ChiChao Lu, WenYuan Jen, Weiping Chang and Shihchieh Chou. “Cybercrime & Cybercriminals: An Overview of the Taiwan Experience,” JOURNAL OF COMPUTERS, 1(6), pp.11-18, Sep. 2006. 27. Sasanka Potluri and Christian Diedrich, “Accelerated deep neural Networks for enhanced Intrusion Detection System.” 2016 IEEE 21st International Conference on Emerging TechnoLogies and Factory Automation (ETFA), pp. 1-8, Sep. 2016. doi: 10.1109/ETFA.2016.7733515. 28. M. Sadiq Ali Khan, “Rule based Network intrusion detection using genetic algorithm,” International Journal of Computer Applications, Volume 18, No. 8, pp. 26-29, Mar. 2011. doi: 10.5120/2303-2914. 29. William S. Noble, “What is a support vector machine?.” Nature biotechnoLogy, Volume 24, Issue 12, pp. 1565-1567, Dec. 2006. doi: 10.1038/nbt1206-1565. 30. Shan Suthaharan, “Support vector machine.” Machine Learning Models and Algorithms for Big Data Classification (Integrated Series in InFormation Systems), ISIS, Volume 36, Springer US, Boston MA, pp. 207-235, 2016. doi: 10.1007/978-1-4899-7641-3_9. 31. Yang Yang, Juan Hao, Jianguang Zhao, Cihang Chen and Haoyue Sun, “Computer User Behavior Anomaly Detection Based on K-Means Algorithm” Security and Communication Networks, Volume 2022, Article ID 3084420, 8 pages, May. 2022. doi: 10.1155/2022/3084420. 32. T. Warren Liao, “Clustering of time series Data—a survey.” Pattern recognition, Volume 38, Issue 11, pp. 1857-1874, Jan. 2005. doi: 10.1016/j.patcog.2005.01.025. 33. George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel and Greta M. Ljung. Time series analysis: forecasting and control, 5th Edition, Jun.2015, John Wiley & Sons, Hoboken, New Jersey, ISBN: 978-1-118-67502-1. 34. Peter G. Zhang. “Time series forecasting using a hybrid ARIMA and neural Network Model.” Neurocomputing, volume 50, pp. 159-175, Jan. 2003. doi: 10.1016/S0925-2312(01)00702-0. 35. Kun Zhou, Wen Yong Wang, Teng Hu and Chen Huang Wu, “Comparison of Time Series Forecasting Based on Statistical ARIMA Model and LSTM with Attention Mechanism, “ Journal of Physics: Conference Series, Hangzhou, Zhejiang, China, Volume 1631, No. 1, IOP Publishing, Sep.2020. doi: 10.1088/1742-6596/1631/1/012141. 36. Rishabh Madan and Partha Sarathi Mangipudi, “Predicting Computer Network Traffic: A Time Series Forecasting Approach Using DWT, ARIMA and RNN.” 2018 Eleventh International Conference on Contemporary Computing (IC3), Noida, India, pp. 1-5, Nov. 2018. doi: 10.1109/IC3.2018.8530608. 37. Sima Siami-Namini, Neba Tavakoli and Akbar Siami Namin, 'A Comparison of ARIMA and LSTM in Forecasting Time Series.” 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, pp. 1394-1401, Dec. 2018. doi: 10.1109/ICMLA.2018.00227 38. Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas and Domonkos Tikk, “Session-based recommendations with recurrent neural Networks.” International Conference on Learning Representations, Nov. 2015, doi: 10.48550/arXiv.1511.06939. 39. Guy Shani, Ronen I. Brafman and David Heckerman, “An MDP-Based Recommender System.' Journal of Machine Learning Research 6, pp.1265-1295, Sep. 2005. 40. Shengdong Du, Tianrui Li and Shi-Jinn Horng, “Time Series Forecasting Using Sequence-to- Sequence Deep Learning Framework.” 2018 9th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), pp. 171-176, Dec. 2018. doi: 10.1109/PAAP.2018.00037. 41. Martin Längkvist, Lars Karlsson and Amy Loutfi, “ A review of unsupervised feature Learning and deep Learning for time-series Modeling.” Pattern Recognition Letters, volume 42, pp. 11-24, Jun. 2014. doi: 10.1016/j.patrec.2014.01.008. 42. Jürgen Schmidhuber, “ Deep Learning in neural Networks: An Overview.” Neural Networks, volume 61, pp. 85-117, Jan. 2015. doi: 10.1016/j.neunet.2014.09.003. 43. Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton, “ Imagenet Classification with deep convolutional neural Networks.” Communications of the ACM, Volume 60, Issue 6, pp. 84-90, Jun. 2017. doi: 10.1145/3065386. 44. Andrej Karpathy and Li Fei-Fei, “Deep visual-semantic alignments for generating image descriptions.” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3128-3137, Jun. 2015. doi: 10.1109/CVPR.2015.7298932. 45. Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk and Yoshua Bengio.' Learning phrase representations using rnn enCoder-deCoder for statistical machine translation', Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724-1734, Oct. 2014. doi: 10.3115/v1/D14-1179. 46. John Cristian Borges Gamboa, “Deep Learning for time-series analysis.' arXiv preprint arXiv: 1701.01887, Jan. 2017. doi: 10.48550/arXiv.1701.01887. 47. Warren S. McCulloch and Walter Pitts, “ A Logical calculus of the ideas immanent in nervous Activity.' The Bulletin of Mathematical Biophysics 5.4, pp. 115-133, Dec. 1943. doi: 10.1007/BF02478259. 48. Samaneh Mahdavifar and Ali A. Ghorbani, 'Application of deep Learning to cybersecurity: A survey.' Neurocomputing, Elsevier, Volume 347, pp. 149-176, Jun. 2019. doi: 10.1016/j.neucom.2019.02.056. 49. Eric Martin and Chris Cundy, 'Parallelizing linear recurrent neural nets Over Sequence length.' International Conference on Learning Representations. 2018, Feb. 2018. https://openreview.net/forum?id=HyUNwulC- . 50. Lang, Tobias and Matthias Rettenmeier, 'Understanding consumer Behavior with recurrent neural Networks.” Workshop on Machine Learning Methods for Recommender Systems. Apr. 2017. 51. Ulises M. Ramirez-Alcocer, Edgar Tello-Leal and Jonathan A. Mata-Torres, 'Predicting incidents of crime through LSTM neural Networks in smart city domain.' Proc. SMART 8th Int. Conf. Smart Cities Syst. Devices Technol., pp. 32-37, Jul. 2019. 52. Sepp Hochreiter and Jürgen Schmidhuber, 'Long short-term memory.” Neural computation 9.8, pp. 1735-1780, Nov. 1997. doi: 10.1162/neco.1997.9.8.1735 . 53. Jihyun Kim, Jaehyun Kim, Huong Le Thi Thu and Howon Kim, “Long short term memory recurrent neural Network Classifier for intrusion detection.” 2016 International Conference on Platform TechnoLogy and Service (PlatCon), Jeju, Korea(South), pp. 1-5, Feb. 2016. doi: 10.1109/PlatCon.2016.7456805. 54. David So, Quoc Le and Chen Liang, “The evolved transformer.” Proceedings of the 36th International Conference on Machine Learning, PMLR 97, pp. 5877-5886, Jun. 2019. https://proceedings.mlr.press/v97/so19a.html . 55. Phi Michael, “IllustRatiod Guide to Transformers-Step by Step Explanation.” Towards Data Science (online), 1 May 2020, URL: https://towardsDatascience.com/illustrated-guide-to-transformers-step-by-step-explanation-f74876522bc0 . 56. Myeongsu Kang and Noel Jordan Jameson. 'Machine Learning: Fundamentals.' Prognostics and Health Management of Electronics, John Wiley & Sons, Hoboken, New Jersey, pp. 85-109, Aug. 2018. doi: 10.1002/9781119515326.ch4 57. Julianna Delua. 'Supervised vs. unsupervised learning: What’s the difference.' IBM Blog (online), 12 Mar. 2021, URL: https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning. 58. Leijnen, Stefan and Fjodor van Veen, “The neural Network zoo.” Multidisciplinary Digital Publishing Institute Proceedings, Volume 47, No. 1, May 2020. doi: 10.3390/proceedings2020047009 . 59. Jan‐Erik Holmberg, “Defense‐in‐Depth.” Handbook of Safety Principles, John Wiley & Sons, Hoboken, New Jersey, pp. 42-62, Dec. 2017. doi: 10.1002/9781119443070.ch4. 60. Mike Chapple, Michael Stewart James and Darril Gibson. (ISC) 2 CISSP Certified Information Systems Security Professional Official Study Guide, 9th Edition. John Wiley & Sons, 2018, ISBN: 9781119786238. 61. Bo Sun, Yunzong Zhu, Yongkang Xiao, Rong Xiao and Yungang Wei, 'Automatic Question Tagging with Deep Neural Networks.' in IEEE TransActions on Learning Technologies, volume 12, no. 1, pp. 29-43, 1 Jan.-March 2019, doi: 10.1109/TLT.2018.2808187. 62. Lu Fang, Lijie Wang, Meng Li, Junfeng Zhao, Yanzhen Zou and Lingshuang Shao, 'Towards Automatic Tagging for Web Services.” 2012 IEEE 19th International Conference on Web Services, pp. 528-535, Jun. 2012. doi: 10.1109/ICWS.2012.99. 63. Aitor Almeida and Gorka Azkune, “Predicting Human Behaviour with Recurrent Neural Networks' Applied Sciences 8, no. 2, pp. 305, Feb. 2018. doi: 10.3390/app8020305. 64. Ling Li, Sida Dai and Zhiwei Cao, 'Deep Long Short-term Memory (LSTM) Network with Sliding-Window Approach in Urban Thermal Analysis.” 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops), pp. 222-227, Aug. 2019. doi: 10.1109/ICCChinaW.2019.8849965. 65. H.S. Hota , Richa Handa and A.K. Shrivas, “Time Series Data Prediction Using Sliding Window Based RBF Neural Network.” International Journal of Computational Intelligence Research, Volume 13, No. 5 (2017), pp. 1145-1156. ISSN: 0973-1873 66. Tuba Ceren Deveci, Image Classification with Energy Efficient Hadamard Neural Networks. Diss. Bilkent Universitesi (Turkey), ProQuest Dissertations Publishing, Jan. 2018. ISBN: 979-8-209-97138-2. 67. Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak and Ilya Sutskever, “Deep double descent: Where bigger Models and more Data hurt.” Journal of Statistical Mechanics: Theory and Experiment, Dec. 2021, 124003. doi: 10.1088/1742-5468/ac3a74. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85090 | - |
| dc.description.abstract | 當代電子商務蓬勃發展，尤其後疫情時代人們對於線上購物的需求有增無減；而在網購資訊流量大增的同時，包含了惡意攻擊、爬蟲比價和詐騙之資安威脅也日趨嚴重。特別是那些利用購物網內部的新型態商業邏輯與行銷活動的機制漏洞所造成的損害，其威脅正與日俱增，究其所以乃因為傳統的網路安全機制多是針對來自外部的攻擊而設計的防禦邊界，而較少涵蓋到已取得登入認證後的消費者由內部發起的不當行為所致。有鑒於此，本研究提出一種基於多重深度學習與可重構的時間序列模型框架，稱為EBBA（Event-Based Behavior Analysis），用於偵知網站內已經取得登入認證後的消費者的不當行為所引起的異常狀況。與傳統由人工設計的固有異常偵測模型相比，經由真實使用者行為日誌所訓練出的深度學習模型，能更好地適應現代電商日新月異的消費行為情境，並可作為整體安全架構的一個組成部分。本研究以Python構建一在Linux上的EBBA原型系統，具備可切換RNN（循環神經網路）和LSTM（長短期記憶）等各種深度學習模型，並透過2021年於臺灣名列前茅之電子商務服務站真實的使用者存取日誌進行半監管式訓練。初步實驗結果顯示，初步實驗結果表明，RNN和LSTM模型的預測正確率分別可達86.4%和92.2%，而錯放率則不高於0.0385%。 | zh_TW |
| dc.description.abstract | Attribute to the rapid development in electronic commerce (e-commerce) during the post-pandemic era, the threat of online misbehaviors including frauds, hoaxes, and scams by authenticated members of the e-commerce sites becomes much more serious, especially because conventional network security mechanisms are typically designed against attacks from outside the defense perimeters instead of the internal exploitations of system and regulation loopholes. To address such an increasing threat, this research proposed a reconfigurable framework with multiple deep-learning (DL) based time-series recognition models called EBBA (standing for Event-Based Behavior Analysis) to detect and examine the anomaly caused by the misbehaviors from authorized users who already obtained access rights of the e-commerce system. Compared with the traditional human-devised models, deep-learning models trained by real-world site access logs are better adaptive to the evolving contexts of modern e-commerce. As an integral part of the total security architecture, a prototype EBBA system on Linux with two switchable DL models, RNN (Recurrent Neural Networks) and LSTM (Long/Short Term Memory), was constructed in Python and trained in a semi-supervised manner, by real-world access logs from a tier-one Taiwan-based e-commerce site in 2021. Preliminary experimental results show that RNN and LSTM models achieved as high as 86.4% and 92.3% correctness ratios, and as low as zero and 0.0385% mis-granting ratios in prediction of online misbehavior occurrences, respectively. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:43:00Z (GMT). No. of bitstreams: 1 U0001-1008202214593700.pdf: 6336552 bytes, checksum: ed0f5402600989d4278c6ccccb6fca61 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 致謝 ii 摘要 iii Abstract iv 目錄 v 圖目錄 vii 表目錄 ix 第一章 緒論 1 1.1 背景 1 1.2 研究動機 6 1.3 問題表述與主要貢獻 8 1.4 論文架構 9 第二章 文獻探討 10 2.1 消費心理側寫 10 2.1.1 心理層面 14 2.1.2 社會層面 17 2.1.3 犯罪層面 18 2.2 異常偵測方法 20 2.2.1 Rule-Based 21 2.2.2 經典之機器學習 (ML) 相關技術 21 2.2.3 深度學習 (Deep Learning, DL) 的時間序列預測 25 2.2.4 機器學習之學習方法 29 2.3 文獻探討小結 30 第三章 研究方法 (Methodology) 33 3.1 Defense-in-Depth Assumption 33 3.2 行為分析方法 34 3.2.1 使用者行為紀錄 (Access Activity Log) 34 3.2.2 使用者行為段落化 35 3.2.3 流程分析定義與人工標記 38 3.3 預測方法 (Prediction Method) 39 3.3.1 Data Gathering 40 3.3.2 Data Labeling 42 3.3.3 Behavior Modularization 45 3.3.4 模型之訓練 (Training Models) 48 3.3.5 Testing Model 53 3.3.6 Tuning & Prediction 54 第四章 實驗建置與結果 56 4.1 系統架構設計 56 4.1.1 Pandas 60 4.1.2 PyTorch 60 4.1.3 Matplotlib 2D繪圖庫 60 4.1.4 AMD ROCm 60 4.2 實驗結果、分析、與比較 61 4.2.1 實驗開始之前 61 4.2.2 實驗參數與原始數據 63 4.2.3 不同深度學習模型效果之比較 65 4.2.4 優化器 (Optimizer) 比較 68 4.2.5 校正分析 70 4.2.6 二階段實戰驗證 73 4.3 實驗結果小結 75 第五章 結論與未來展望 77 參考文獻 79 | |
| dc.language.iso | zh-TW | |
| dc.subject | 基於事件的行為分析 | zh_TW |
| dc.subject | 時間序列 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 線上購物詐欺 | zh_TW |
| dc.subject | 異常檢測 | zh_TW |
| dc.subject | Time Series | en |
| dc.subject | Anomaly Detection | en |
| dc.subject | Deep Learning | en |
| dc.subject | Online Shopping Fraud | en |
| dc.subject | Event-Based Behavior Analysis (EBBA) | en |
| dc.title | 利用深度學習建構使用者行為異常偵測模型--以電子商務為例 | zh_TW |
| dc.title | Deep Learning Based Behavior Anomaly Detection Models within the Context of Electronic Commerce | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.author-orcid | 1104-0124-0401-1203 | |
| dc.contributor.advisor-orcid | 賴怡吉 (1104-0124-0401-1203) | |
| dc.contributor.oralexamcommittee | 范俊逸 (Chun-I FAN),張維平 (Robert Chang),吳沛遠(Pei-Yuan Wu) | |
| dc.contributor.oralexamcommittee-orcid | 范俊逸 (1104-0124-0401-1203),張維平 (1104-0124-0401-1203),吳沛遠(1104-0124-0401-1203) | |
| dc.subject.keyword | 線上購物詐欺,深度學習,時間序列,基於事件的行為分析,異常檢測, | zh_TW |
| dc.subject.keyword | Online Shopping Fraud,Deep Learning,Time Series,Event-Based Behavior Analysis (EBBA),Anomaly Detection, | en |
| dc.relation.page | 92 | |
| dc.identifier.doi | 10.6342/NTU202202260 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-08-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-08-18 | - |
| 顯示於系所單位： | 電機工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1008202214593700.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 6.19 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。