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題名:運用資料探勘技術與深度學習演算法改善產品生產流程
作者:彭啟峰
作者(外文):PENG, CHI-FENG
校院名稱:中華大學
系所名稱:科技管理博士學位學程
指導教授:賀力行
學位類別:博士
出版日期:2020
主題關鍵詞:馬氏田口邏輯斯迴歸類神經網路深度學習MTSLogistic RegressionNeural NetworkDeep learning
原始連結:連回原系統網址new window
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工業4.0與智慧工廠的提倡下,為提升產業競爭力,自動化與電子化已是必然的趨勢。近來因資訊發達,在資料取得要比過去來的容易許多,但在這些龐大的資料中要如何獲取有用的資訊,或是透過這些資料來建立預測模型,就必須應用資料探勘(data mining)與大數據(Big Data)分析的技術。
本研究的主旨是運用資料探勘技術與深度學習演算法來分析大數據以改善產品生產流程。以資料探勘技術相關演算法做深入的瞭解,以及對大數據的定義與過去之相關運用等研究做探討,並且收集相關研究與文獻總結出資料探勘技術與大數據分析的目標與重要意義,提煉出本研究的改善產品生產流程與提高生產效率的目標,設計與建立本研究方法模型。接續,透過企業實證研究,收集產品生產製程等相關資料。最後,根據個案狀況,建立高生產效率之製程管理系統透過大數據分析服務,可針對生產效率、生產週期與節能等需求,來找出有效的改善計畫,進而為工廠提升整體效率,降低成本,增加競爭力。
With the promotion of Industry 4.0 and smart factories, in order to enhance the competitiveness of the industry, automation and electronicization have become an inevitable trend. Recently, due to the development of information, it is much easier to obtain data than in the past, but how to obtain useful information from these huge data, or to build predictive models through these data, must apply data mining and analyze big data technology.
This study used data mining technology and deep learning algorithms to analyze big data to improve production processes. Learn more about algorithms related to data mining technology, and discuss the definition of big data and related applications in the past. Collect relevant research and literature to summarize the goals and significance of data mining technology and big data analysis. With the goal of improving production process and increasing production efficiency of this research, design and establish the research method model of this subject. Continuing, through the empirical research of the enterprise, collect relevant information such as product manufacturing process. Finally, according to the study case, a process management system with high production efficiency can be set up. Through big data analysis services, it can find effective improvement plans for the needs of production efficiency, production cycle and energy saving, thereby improving the overall efficiency of the factory, reducing costs and increase competitiveness.
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