臺灣博碩士論文加值系統

在人工智慧技術迅速發展的時代，其應用範疇日益擴大，然而將人工智慧系統性整合至產品設計與開發流程中的研究仍顯不足，本研究旨在探索如何有效利用人工智慧技術融合於產品開發的各個階段，並構建一套人工智慧驅動的產品開發流程系統；研究首先審視並解構現行產品開發流程，包括市場需求分析、設計風格、設計方案生成等關鍵環節，進行全面性評估以找出開發流程的共通性。
在研究方法方面，本研究選擇了多種人工智慧技術，包括自然語言處理（NLP）、圖像生成和資料探勘等，評估其在產品開發不同階段的應用潛力，基於這些技術，本研究開發了一個名為AIVY的系統，該系統整合了多種人工智慧技術，旨在提升產品開發效率及降低組織間的溝通成本；為驗證AIVY系統的實用性，本研究選定Optoma奧圖碼的PK301投影機作為示範案例，通過模擬產品開發流程，展示人工智慧技術在實際設計工作中的應用效果；最後通過問卷調查對系統的易用性、設計效率和創新性進行了全面評估，結果顯示，AIVY系統在提升設計效率、降低溝通成本和促進跨領域協作方面具有顯著優勢。
本研究展示了人工智慧技術在產品開發中的巨大潛力，並提供了詳細的技術實作細節和創新理論，對於當今的研究者、企業和設計人員具有重要的參考價值，此外，本研究在跨領域合作中展現了人工智慧技術創新應用的潛力，實現了設計與技術的深度融合，這些成果為未來的研究和應用奠定了堅實的理論和實踐基礎，期望本研究能夠推動產品設計與人工智慧的進一步融合，為相關領域的智慧化和高效化發展提供新的動力。 

In an era of rapid advancements in artificial intelligence (AI) technology, its scope of application continues to expand. However, research on systematically integrating AI into product design and development processes remains insufficient. This study aims to explore how to effectively utilize AI technology at various stages of product development and to construct an AI-driven product development process. Initially, the study examines and deconstructs existing product development processes, including key aspects such as market demand analysis, design style, and design scheme generation, to conduct a comprehensive assessment and identify commonalities in the development process.
In terms of research methodology, this study employs various AI technologies, including natural language processing (NLP), image generation, and data mining, to evaluate their potential applications at different stages of product development. Based on these technologies, we developed a system named AIVY, which integrates multiple AI technologies to enhance product development efficiency and reduce communication costs within organizations. To validate the practicality of the AIVY system, we selected the Optoma PK301 projector as a demonstration case, simulating the product development process to showcase the application of AI technology in actual design work. Finally, the study conducted a comprehensive evaluation of the system's usability, design efficiency, and innovation through questionnaires. The results indicate that the AIVY system significantly improves design efficiency, reduces communication costs, and promotes cross-disciplinary collaboration.
This study demonstrates the immense potential of AI technology in product development, providing detailed technical implementation and innovative theories that hold significant reference value for contemporary researchers, enterprises, and designers. Additionally, the study showcases the potential for innovative applications of AI technology in cross-disciplinary collaboration, achieving a deep integration of design and technology. These findings lay a solid theoretical and practical foundation for future research and applications. It is hoped that this study will promote further integration of product design and AI, providing new momentum for the intelligent and efficient development of related fields.

目錄

摘要．．．．．．．．．．i
ABSTRACT．．．．．．．．．．iii
致謝．．．．．．．．．．v
目錄．．．．．．．．．．vii
圖目錄．．．．．．．．．．xii
表目錄．．．．．．．．．．xvi
第一章 緒論．．．．．．．．．．1
1.1 研究動機與背景．．．．．．．．．．1
1.2 研究目的．．．．．．．．．．3
1.3 研究範圍及限制．．．．．．．．．．4
1.4 研究架構．．．．．．．．．．5
第二章 文獻探討．．．．．．．．．．6
2.1 產品開發流程理論探討．．．．．．．．．．6
2.1.1 工業設計與產品開發．．．．．．．．．．6
2.1.2 雙鑽石模型（Double Diamond）．．．．．．．．．．8
2.1.3 D.school / IDEO設計思考五步驟．．．．．．．．．．9
2.1.4 國際設計管理協會產品開發流程．．．．．．．．．．10
2.1.5 小結．．．．．．．．．．11
2.2 人工智慧與技術．．．．．．．．．．12
2.2.1 人工智慧的古與今．．．．．．．．．．12
2.2.2 NLP與ChatGPT．．．．．．．．．．12
2.2.3 小結．．．．．．．．．．15
2.3 AIGC與Stable Diffusion．．．．．．．．．．17
2.3.1 LoRA．．．．．．．．．．19
2.3.2 ControlNet．．．．．．．．．．20
2.4 小結．．．．．．．．．．22
第三章 研究方法．．．．．．．．．．25
3.1 實驗架構．．．．．．．．．．26
3.2 實驗說明．．．．．．．．．．28
3.2.1 實驗樣本與品牌設定．．．．．．．．．．28
3.2.2 實驗環境與設備．．．．．．．．．．30
3.3 實驗研究參與者規劃．．．．．．．．．．31
3.4 問卷設計．．．．．．．．．．31
3.4.1 問卷設計－研究說明．．．．．．．．．．32
3.4.2 問卷設計－基本資料．．．．．．．．．．32
3.4.3 問卷設計－SUS系統易用性問卷．．．．．．．．．．32
3.4.4 問卷設計－李克特量表問卷．．．．．．．．．．33
3.4.5 問卷設計－綜合評估問卷．．．．．．．．．．33
3.4.6 問卷設計－研究結語．．．．．．．．．．34
3.5 SUS系統易用性量表（System Usability Scale）．．．．．．．．．．35
3.5.1 SUS系統易用性－信度分析（Reliability Analysis）．．．．．．．．．．37
3.6 李克特量表（Likert Scale）．．．．．．．．．．39
3.6.1 李克特量表－信度分析（Reliability Analysis）．．．．．．．．．．40
3.6.2 李克特量表－平均分數和標準差σ．．．．．．．．．．40
3.6.3 李克特量表－獨立樣本t檢定．．．．．．．．．．41
第四章 AI驅動產品開發流程開發階段性驗證．．．．．．．．．．43
4.1 開發流程之驗證任務．．．．．．．．．．43
4.2 消費者評價資料探勘．．．．．．．．．．43
4.2.1 示範產品與消費者評價．．．．．．．．．．44
4.2.2 評價蒐集方法．．．．．．．．．．44
4.3 消費者評價與自然語言處理．．．．．．．．．．48
4.3.1 消費者評價處理結果與分析．．．．．．．．．．51
4.4 品牌產品風格模型訓練．．．．．．．．．．55
4.4.1 品牌產品圖像資料．．．．．．．．．．56
4.4.2 訓練圖像預處理．．．．．．．．．．56
4.4.3 LoRA模型．．．．．．．．．．64
4.4.3.1 LoRA模型訓練．．．．．．．．．．64
4.4.3.2 LoRA模型篩檢．．．．．．．．．．68
4.4.3.3 LoRA模型挑選．．．．．．．．．．73
4.5 生成目標語意控制與圖像控制．．．．．．．．．．75
4.5.1 語意與圖像控制－權重．．．．．．．．．．76
4.5.2 語意控制－提詞（Prompt）．．．．．．．．．．78
4.5.3 圖像控制－採樣器（Sampler）．．．．．．．．．．83
4.5.4 採樣器（Sampler）．．．．．．．．．．83
4.5.4.1 老派採樣器（Old-school solvers）．．．．．．．．．．84
4.5.4.2 新世代採樣器（New-generation sampler）．．．．．．．．．．86
4.5.4.3 祖先採樣器（Ancestor sampler）．．．．．．．．．．88
4.5.4.4 DPM採樣器 （Diffusion Probabilistic Model, DPM）．．．．．．．．．．91
4.5.4.5 Karras採樣器（Karras sampler）．．．．．．．．．．94
4.5.4.6 小結．．．．．．．．．．96
4.5.5 圖像控制－ControlNet．．．．．．．．．．97
4.6 圖像生成．．．．．．．．．．100
4.6.1 Optoma PK301投影機的繼任機種－保守方案．．．．．．．．．．102
4.6.2 Optoma PK301投影機的繼任機種－平衡方案．．．．．．．．．．103
4.6.3 Optoma奧圖碼PK301投影機的繼任機種－前衛方案．．．．．．．．．．104
4.6.4 小結．．．．．．．．．．105
第五章 以AI驅動產品開發系統之建立與展示．．．．．．．．．．106
5.1 示範任務．．．．．．．．．．106
5.2 模擬示範流程展示架構．．．．．．．．．．107
5.3 人工智慧驅動產品開發系統－AIVY．．．．．．．．．．108
5.3.1 AIVY－視覺設定．．．．．．．．．．108
5.3.2 AIVY－系統展示流程．．．．．．．．．．110
5.3.2.1 系統登入與使用者分組．．．．．．．．．．110
5.3.2.2 市場端工作流程．．．．．．．．．．112
5.3.2.3 設計端工作流程．．．．．．．．．．121
第六章 研究結果與分析．．．．．．．．．．126
6.1 研究參與者結構分析．．．．．．．．．．126
6.2 SUS系統易用性調查與使用者反饋．．．．．．．．．．128
6.2.1 SUS系統易用性量表－信度分析（Reliability Analysis）．．．．．．．．．．28
6.2.2 SUS系統易用性量表分析．．．．．．．．．．129
6.2.2.1 SUS系統易用性量表－整體分析．．．．．．．．．．130
6.2.2.2 SUS系統易用性量表－市場端分析．．．．．．．．．．132
6.2.2.3 SUS系統易用性量表－設計端分析．．．．．．．．．．134
6.2.2.4 小結．．．．．．．．．．135
6.3 李克特量表－AIVY系統使用後調查．．．．．．．．．．136
6.3.1 李克特量表－信度分析（Reliability Analysis）．．．．．．．．．．136
6.3.2 李克特量表－平均分數和標準差σ．．．．．．．．．．137
6.3.3 李克特量表－市場端平均分數和標準差σ．．．．．．．．．．137
6.3.4 李克特量表－設計端平均分數和標準差σ．．．．．．．．．．138
6.3.5 李克特量表－整體平均分數和標準差σ．．．．．．．．．．140
6.4 小結．．．．．．．．．．141
6.4.1 李克特量表－獨立樣本t檢定．．．．．．．．．．143
6.5 綜合評估問卷結果解析．．．．．．．．．．144
6.5.1 綜合評估問卷－多重選擇題．．．．．．．．．．144
6.5.2 綜合評估問卷－評分問題．．．．．．．．．．147
第七章 結論．．．．．．．．．．148
7.1 研究目的回顧．．．．．．．．．．148
7.2 主要發現．．．．．．．．．．148
7.3 創新成就．．．．．．．．．．149
7.3.1 瞭解市場－市場調查與分析．．．．．．．．．．150
7.3.2 瞭解設計－學習設計風格．．．．．．．．．．151
7.3.3 創造設計－產品設計．．．．．．．．．．152
7.3.4 小結．．．．．．．．．．153
7.4 研究限制．．．．．．．．．．154
7.5 總結．．．．．．．．．．154
第八章 討論與展望．．．．．．．．．．156
參考文獻 ．．．．．．．．．．159
附錄 ．．．．．．．．．．168
附錄A　 採樣器對比．．．．．．．．．．168
附錄B　 SUS易用性量表-問卷設計．．．．．．．．．．171
附錄C　SUS易用性量表-原始分數彙整．．．．．．．．．．176
附錄D　SUS易用性量表-逆處理分數彙整．．．．．．．．．．178

1.Bae, J. K., & Kim, J. (2011). Product development with data mining techniques: A case on design of digital camera. Expert Systems with Applications, 38(8), 9274-9280.
2.Bangor, A., Kortum, P., & Miller, J. (2009). Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of usability studies, 4(3), 114-123.
3.Berlyne, D. E. (1970). Novelty, complexity, and hedonic value. Perception & psychophysics, 8(5), 279-286.
4.Biswas, S. (2023). Role of Chat GPT in Education. Available at SSRN 4369981.
5.Boone Jr, H. N., & Boone, D. A. (2012). Analyzing likert data. The Journal of extension, 50(2), 48.
6.Brooke, J. (1995). SUS: A quick and dirty usability scale. Usability Eval. Ind., 189.
7.Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
8.Chen, J.-S., Wang, K.-C., & Liang, J.-C. (2008). A hybrid kansei design expert system using artificial intelligence. PRICAI 2008: Trends in Artificial Intelligence: 10th Pacific Rim International Conference on Artificial Intelligence, Hanoi, Vietnam, December 15-19, 2008. Proceedings 10,
9.Chomsky, N. (2002). Syntactic Structures (Second Edition ed.). De Gruyter Mouton. https://doi.org/doi:10.1515/9783110218329
10.Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.
11.Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.
12.Crowson, K. (2022). k-diffusion. https://github.com/crowsonkb/k-diffusion
13.Dahlquist, G. (1958). Stability and error bounds in the numerical integration of ordinary differential equations Almqvist & Wiksell].
14.Design Council. (2005). Eleven lessons: managing design in eleven global brands
15.Design Council. (2007). Eleven lessons: managing design in eleven global companies
16.Dinar, M., Shah, J. J., Cagan, J., Leifer, L., Linsey, J., Smith, S. M., & Hernandez, N. V. (2015). Empirical Studies of Designer Thinking: Past, Present, and Future. Journal of Mechanical Design, 137(2). https://doi.org/10.1115/1.4029025
17.Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., & Boesel, F. (2024). Scaling rectified flow transformers for high-resolution image synthesis. arXiv preprint arXiv:2403.03206.
18.Euler, L. (1761). Principia motus fluidorum. Novi commentarii academiae scientiarum Petropolitanae, 271-311.
19.Füller, J., & Matzler, K. (2007). Virtual product experience and customer participation—A chance for customer-centred, really new products. Technovation, 27(6), 378-387.
20.Field, A. (2013). Discovering statistics using IBM SPSS statistics. sage.
21.Fortune Media. (2023). Fortune Global 500. https://fortune.com/ranking/global500/2022/
22.Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
23.Gropius, W. (1975). The Theory and Organisation of the Bauhaus, 1923.
24.Heaton, J. (2018). Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618. Genetic programming and evolvable machines, 19(1-2), 305-307.
25.Henrici, P. (1962). Discrete variable methods in ordinary differential equations. New York: Wiley.
26.Hermann, M., Pentek, T., & Otto, B. (2016, 5-8 Jan. 2016). Design Principles for Industrie 4.0 Scenarios. 2016 49th Hawaii International Conference on System Sciences (HICSS),
27.Heun, K. (1900). Neue Methoden zur approximativen Integration der Differentialgleichungen einer unabhängigen Veränderlichen. Z. Math. Phys, 45, 23-38.
28.Ho, J., Chen, X., Srinivas, A., Duan, Y., & Abbeel, P. (2019). Flow++: Improving flow-based generative models with variational dequantization and architecture design. International Conference on Machine Learning,
29.Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851.
30.Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685. Retrieved June 01, 2021, from https://ui.adsabs.harvard.edu/abs/2021arXiv210609685H
31.Hung, W.-K., & Chen, L.-L. (2012). Effects of novelty and its dimensions on aesthetic preference in product design. International Journal of Design, 6(2), 81-90.
32.ICSID. (1959). Icsid Constitution. Retrieved September 4 from https://wdo.org/about/definition/industrial-design-definition-history/
33.ICSID. (2006). Industrial Design. Retrieved September 4 from https://wdo.org/glossary/industrial-design/
34.Jasperneite, J. (2012). Was hinter Begriffen wie Industrie 4.0 steckt.
35.Jianjun, H., Yao, Y., Hameed, J., Kamran, H. W., Nawaz, M. A., Aqdas, R., & Patwary, A. K. (2021). The role of artificial and nonartificial intelligence in the new product success with moderating role of new product innovation: a case of manufacturing companies in China. Complexity, 2021, 1-14.
36.Jin, J., Liu, Y., Ji, P., & Kwong, C. K. (2019). Review on recent advances in information mining from big consumer opinion data for product design. Journal of Computing and Information Science in Engineering, 19(1), 010801.
37.Jurafsky, D., & Martin, J. H. (2009). Speech and Language Processing (2nd Edition). Prentice-Hall, Inc.
38.Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90.
39.Karras, T., Aittala, M., Aila, T., & Laine, S. (2022). Elucidating the design space of diffusion-based generative models. Advances in neural information processing systems, 35, 26565-26577.
40.Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
41.Klintong, N., Vadhanasindhu, P., & Thawesaengskulthai, N. (2012). Artificial intelligence and successful factors for selecting product innovation development. 2012 Third International Conference on Intelligent Systems Modelling and Simulation,
42.Kumar, V. (2012). 101 design methods : a structured approach for driving innovation in your organization. John Wiley & Sons Inc. Hoboken, New Jersey.
43.Li, J., Li, D., Xiong, C., & Hoi, S. (2022). Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. International Conference on Machine Learning,
44.Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
45.Loshchilov, I., & Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983.
46.Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., & Zhu, J. (2022a). Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in neural information processing systems, 35, 5775-5787.
47.Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., & Zhu, J. (2022b). Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095.
48.Lun, Z., Kalogerakis, E., & Sheffer, A. (2015). Elements of style: learning perceptual shape style similarity. ACM Transactions on graphics (TOG), 34(4), 1-14.
49.McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
50.Mishchenko, K., & Defazio, A. (2023). Prodigy: An expeditiously adaptive parameter-free learner. arXiv preprint arXiv:2306.06101.
51.Mordor Intelligence Research & Advisory. (2023). Digital Light Processing (DLP) Projector Market Size & Share Analysis - Growth Trends & Forecasts (2023 - 2028). Mordor Intelligence. https://www.mordorintelligence.com/industry-reports/dlp-projector-market
52.Nüßgen, A., Degen, R., Irmer, M., Richter, F., Boström, C., & Ruschitzka, M. (2024). Leveraging Robust Artificial Intelligence for Mechatronic Product Development: A Literature Review. International Journal of Intelligence Science, 14(01), 1-21.
53.Norman, D. A. (2002). The Design of Everyday Things. Basic Books, Inc.
54.Nunnally, J., & Bernstein, I. (1994). Psychometric Theory 3rd edition (MacGraw-Hill, New York). In.
55.Nunnally, J. D. (1978). Psychometric Theory (2nd ed), New York: McGraw-Hill.
56.Open AI. (2024). Hello GPT-4o. https://openai.com/index/hello-gpt-4o/
57.Quan, H., Li, S., & Hu, J. (2018). Product innovation design based on deep learning and Kansei engineering. Applied Sciences, 8(12), 2397.
58.Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., & Clark, J. (2021). Learning transferable visual models from natural language supervision. International conference on machine learning,
59.Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
60.Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.
61.Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
62.Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,
63.Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. Advances in neural information processing systems, 29.
64.Sauro, J. (2011). A Practical Guide to the System Usability Scale: Background, Benchmarks & Best Practices. Measuring Usability LLC. https://books.google.com.tw/books?id=BL0kKQEACAAJ
65.Shen, D., Wu, G., & Suk, H. I. (2017). Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng, 19, 221-248.
66.Sihombing, H., Yuhazri, M., Yahaya, S., & Syaifoelida, F. (2013). The kansei design characteristics towards learning style. Journal of Engineering, 2013(1), 584656.
67.Simon, H. A. (1996). The sciences of the artificial. MIT press.
68.Sissie Hsiao. (2024). Get more done with Gemini: Try 1.5 Pro and more intelligent features. https://blog.google/products/gemini/google-gemini-update-may-2024/#context-window
69.Smith, P. G., & Reinertsen, D. G. (1998). Developing products in half the time: new rules, new tools. Van Nostrand Reinhold New York.
70.Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. International conference on machine learning,
71.Song, Y., & Ermon, S. (2019). Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32.
72.Stanford d.school. (2015). An Introduction to Design Thinking - PROCESS GUIDE.
73.Tullis, T., & Stetson, J. (2006). A Comparison of Questionnaires for Assessing Website Usability.
74.Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433-460.
75.Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
76.Villaumbrosia, C. G. D. (2023). Top 18 AI Tools for Product Managers and Product Teams. https://productschool.com/blog/artificial-intelligence/ai-tools-for-product-managers
77.Vlahogianni, E., Karlaftis, M., & Golias, J. (2014). Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technologies, 43.
78.Wang, H.-H., Lin, Y.-Y., & Huang, H.-T. (2023). Application of Typicality in Predicting Product Appearance. Engineering Proceedings, 55(1), 66.
79.Wang, H.-H., Liu, C.-K., & Yu, S.-B. (2023). Predicting Consumer Preferences by the Deformation Threshold of Product Appearance. Engineering Proceedings, 55(1), 67.
80.Weng, L. (2021). What are Diffusion Models? https://lilianweng.github.io/posts/2021-07-11-diffusion-models/
81.White, T. (2016). Sampling generative networks. arXiv preprint arXiv:1609.04468.
82.Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International journal of production research, 56(8), 2941-2962.
83.Xu, Y., Deng, M., Cheng, X., Tian, Y., Liu, Z., & Jaakkola, T. (2023). Restart sampling for improving generative processes. Advances in neural information processing systems, 36, 76806-76838.
84.Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003, 22-22 Nov. 2003). Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. Third IEEE International Conference on Data Mining,
85.Zhang, L., & Agrawala, M. (2023). Adding Conditional Control to Text-to-Image Diffusion Models. arXiv:2302.05543. Retrieved February 01, 2023, from https://ui.adsabs.harvard.edu/abs/2023arXiv230205543Z
86.Zhao, W., Bai, L., Rao, Y., Zhou, J., & Lu, J. (2024). Unipc: A unified predictor-corrector framework for fast sampling of diffusion models. Advances in neural information processing systems, 36.
87.周文賢. (2002). 多變量統計分析: SAS/STAT使用方法. 智勝文化事業有限公司.
88.陳文印. (1997). 設計解讀 : 工業設計專業知能之探索 (初版. ed.). 亞太圖書出版社.