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題名 印刷業數位轉型：以燙金錯誤偵測為例
The Digital Transformation In The Printing Industry: Take Hot Foil Stamping Defects Recognition As An Example作者 邱千泰
CHIU, CHIEN-TAI貢獻者 謝明華
邱千泰
CHIU, CHIEN-TAI關鍵詞 數位轉型
卷積神經網路模型
印刷業日期 2022 上傳時間 1-Mar-2022 16:43:31 (UTC+8) 摘要 在本文章中，探討了現今印刷業所面臨的挑戰，包含了消費習慣改變導致印刷需求下降等等。這些挑戰迫使著印刷業進行數位轉型。其中藉由光學儀器與機器學習辨識印刷及燙金錯誤成為了一個可能的轉型方向，可以降低人力成本的花費，同時也能幫助減少管理問題。在數位轉型的方法中，以機器學習作為數位轉型的工具成為了常見的數位轉型方法。在這些機器學習方法中，卷積神經網路是較為適合用於圖像辨識的模型，也能夠用於各種分類問題上。本文以燙金業為例，探討卷積神經網路應用於燙金錯誤辨識所面臨的議題。 參考文獻 Ali, O., Ally, M., & Dwivedi, Y. (2020). The state of play of blockchain technology in the financial services sector: A systematic literature review. International Journal of Information Management, 54, 102–199. Bellman, R., & Lee, E. S. (1978). Functional equations in dynamic programming. Aequationes Mathematicae, 17(1), 1–18. Berman, S. J. (2012). Digital transformation: opportunities to create new business models. Strategy & Leadership. Bharadwaj, A., Sawy, O. A. El, Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights. MIS Quarterly, 471–482. Bredt, S. (2019). Artificial Intelligence (AI) in the financial sector—Potential and public strategies. Frontiers in Artificial Intelligence, 2, 16. Chan, H. K., Griffin, J., Lim, J. J., Zeng, F., & Chiu, A. S. F. (2018). The impact of 3D Printing Technology on the supply chain: Manufacturing and legal perspectives. International Journal of Production Economics, 205, 156–162. Clarke, D., Puthiyamadam, T., Gaynor, P., & Likens, S. (2020). Payback ahead. Take charge of your future. PwC. Downes, L., & Nunes, P. (2013). Big-bang disruption. Harvard Business Review, 91(3), 44–56. Fukushima., K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 36, 193–202. Haugeland, J. (1985). Artificial intelligence: the very idea. In: Cambridge, MA: MIT Press. Heavin, C., & Power, D. J. (2018). Challenges for digital transformation–towards a conceptual decision support guide for managers. Journal of Decision Systems, 27(sup1), 38–45. Machinery, C. (1950). Computing machinery and intelligence-AM Turing. Mind, 59(236), 433. Matt, C., Hess, T., & Benlian, A. (2015). Digital Transformation Strategies. Business & Information Systems Engineering, 57(5), 339–343. Relewicz, J. Q. (2017). Big data and big money: The role of data in the financial sector. IT Professional, 19(3), 8–10. Rojers, J. P. (2018). Digital Transformation, Business Model Innovation and Efficiency in Content Industries: A Review. The International Technology Management Review, 7(1), 59–70. Schwertner, K. (2017). Digital transformation of business. Trakia Journal of Sciences, 15(1), 388–393. Shaughnessy, H. (2018). Creating digital transformation: strategies and steps. Strategy & Leadership. Shaw, C., & Hamilton, R. (2016). The intuitive customer: 7 imperatives for moving your customer experience to the next level. Villalba-Diez, J., Schmidt, D., Gevers, R., Ordieres-Meré, J., Buchwitz, M., & Wellbrock, W. (2019). Deep learning for industrial computer vision quality control in the printing industry 4.0. Sensors, 19(18), 3987. Werbos., P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD Thesis, Harvard University. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Press. 描述 碩士
國立政治大學
經營管理碩士學程(EMBA)
105932007資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105932007 資料類型 thesis dc.contributor.advisor 謝明華 zh_TW dc.contributor.author (Authors) 邱千泰 zh_TW dc.contributor.author (Authors) CHIU, CHIEN-TAI en_US dc.creator (作者) 邱千泰 zh_TW dc.creator (作者) CHIU, CHIEN-TAI en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Mar-2022 16:43:31 (UTC+8) - dc.date.available 1-Mar-2022 16:43:31 (UTC+8) - dc.date.issued (上傳時間) 1-Mar-2022 16:43:31 (UTC+8) - dc.identifier (Other Identifiers) G0105932007 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139146 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經營管理碩士學程(EMBA) zh_TW dc.description (描述) 105932007 zh_TW dc.description.abstract (摘要) 在本文章中，探討了現今印刷業所面臨的挑戰，包含了消費習慣改變導致印刷需求下降等等。這些挑戰迫使著印刷業進行數位轉型。其中藉由光學儀器與機器學習辨識印刷及燙金錯誤成為了一個可能的轉型方向，可以降低人力成本的花費，同時也能幫助減少管理問題。在數位轉型的方法中，以機器學習作為數位轉型的工具成為了常見的數位轉型方法。在這些機器學習方法中，卷積神經網路是較為適合用於圖像辨識的模型，也能夠用於各種分類問題上。本文以燙金業為例，探討卷積神經網路應用於燙金錯誤辨識所面臨的議題。 zh_TW dc.description.tableofcontents 摘要 1 目錄 1 第一章 國際印刷業數位轉型發展態勢 2 第二章 數位轉型文獻探討 8 第三章 機器學習介紹 15 第一節 人工智慧與機器學習 15 第二節 機器學習的類型 16 第四章 以燙金錯誤偵測探討機器學習的應用 21 第一節 卷積神經網路模型 21 第二節 將卷積神經網路模型應用於燙金錯誤辨識 34 第五章 結論及展望 48 參考文獻 50 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105932007 en_US dc.subject (關鍵詞) 數位轉型 zh_TW dc.subject (關鍵詞) 卷積神經網路模型 zh_TW dc.subject (關鍵詞) 印刷業 zh_TW dc.title (題名) 印刷業數位轉型：以燙金錯誤偵測為例 zh_TW dc.title (題名) The Digital Transformation In The Printing Industry: Take Hot Foil Stamping Defects Recognition As An Example en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Ali, O., Ally, M., & Dwivedi, Y. (2020). The state of play of blockchain technology in the financial services sector: A systematic literature review. International Journal of Information Management, 54, 102–199. Bellman, R., & Lee, E. S. (1978). Functional equations in dynamic programming. Aequationes Mathematicae, 17(1), 1–18. Berman, S. J. (2012). Digital transformation: opportunities to create new business models. Strategy & Leadership. Bharadwaj, A., Sawy, O. A. El, Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights. MIS Quarterly, 471–482. Bredt, S. (2019). Artificial Intelligence (AI) in the financial sector—Potential and public strategies. Frontiers in Artificial Intelligence, 2, 16. Chan, H. K., Griffin, J., Lim, J. J., Zeng, F., & Chiu, A. S. F. (2018). The impact of 3D Printing Technology on the supply chain: Manufacturing and legal perspectives. International Journal of Production Economics, 205, 156–162. Clarke, D., Puthiyamadam, T., Gaynor, P., & Likens, S. (2020). Payback ahead. Take charge of your future. PwC. Downes, L., & Nunes, P. (2013). Big-bang disruption. Harvard Business Review, 91(3), 44–56. Fukushima., K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern., 36, 193–202. Haugeland, J. (1985). Artificial intelligence: the very idea. In: Cambridge, MA: MIT Press. Heavin, C., & Power, D. J. (2018). Challenges for digital transformation–towards a conceptual decision support guide for managers. Journal of Decision Systems, 27(sup1), 38–45. Machinery, C. (1950). Computing machinery and intelligence-AM Turing. Mind, 59(236), 433. Matt, C., Hess, T., & Benlian, A. (2015). Digital Transformation Strategies. Business & Information Systems Engineering, 57(5), 339–343. Relewicz, J. Q. (2017). Big data and big money: The role of data in the financial sector. IT Professional, 19(3), 8–10. Rojers, J. P. (2018). Digital Transformation, Business Model Innovation and Efficiency in Content Industries: A Review. The International Technology Management Review, 7(1), 59–70. Schwertner, K. (2017). Digital transformation of business. Trakia Journal of Sciences, 15(1), 388–393. Shaughnessy, H. (2018). Creating digital transformation: strategies and steps. Strategy & Leadership. Shaw, C., & Hamilton, R. (2016). The intuitive customer: 7 imperatives for moving your customer experience to the next level. Villalba-Diez, J., Schmidt, D., Gevers, R., Ordieres-Meré, J., Buchwitz, M., & Wellbrock, W. (2019). Deep learning for industrial computer vision quality control in the printing industry 4.0. Sensors, 19(18), 3987. Werbos., P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD Thesis, Harvard University. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Press. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200322 en_US
