基於Web之虛實融合系統機器學習建模;Web-based Machine Learning Modeling in a Cyber-Physical System

NCU Institutional Repository > 資訊電機學院 > 資訊工程研究所 > 博碩士論文 > Item 987654321/81175

請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/81175

題名:	基於Web之虛實融合系統機器學習建模;Web-based Machine Learning Modeling in a Cyber-Physical System
作者:	楊宜昌;Yang, Yi-Chang
貢獻者:	資訊工程學系
關鍵詞:	工業4.0;智慧製造;虛實融合系統;全球資訊網;機器學習;建模;測試案例;支援向量機;支援向量迴歸;深度神經網路;卷積神經網路;Industry 4.0;Smart Manufacturing;Cyber-Physical System;Web;Machine Learning;Modeling;Test Cases;Support Vector Machine;Support Vector Regression;Deep Neural Network;Convolutional Neural Network
日期:	2019-07-25
上傳時間:	2019-09-03 15:38:28 (UTC+8)
出版者:	國立中央大學
摘要:	虛實融合系統(Cyber-Physical System, CPS)為工業4.0(Industry 4.0)智慧製造核心系統，整合大數據(Big Data)、工業物聯網(Industrial Internet of Things, IIoT)、人工智慧(Artificial Intelligence, AI)和雲端運算(Cloud Computing)等技術使製造智慧化，透過生產參數及生產狀態資料的即時擷取、過濾、前處理、分析與分析結果的反饋，能夠提升生產效能，降低生產成本、增進生產品質及提高生產產量。機器學習(Machine Learning)為CPS中常用的資料分析技術，它是人工智慧的一個分支，旨在透過以往的大量資料找到隱藏在資料中的資料模式或知識。但一般而言，若不使用特殊工具，機器學習上必須具有些許程式設計背景才能實現。因此，本論文嘗試降低實現機器學習資料分析的門檻，以提升實作效率。本論文以基於全球資訊網(Web)的 CPS建構輔助系統“PINE”為基礎，聚焦於設計及實作該系統之資料分析機器學習建模功能，整合PINE的網站開發框架及機器學習套件，完成機器學習建模及模型共享架構，並製作測試案例以確保所實作的功能與設計規格相符。支援的機器學習模型包括支援向量機(Support Vector Machine, SVM)、支援向量迴歸(Support Vector Regression, SVR)、深度神經網路(Deep Neural Network, DNN)及卷積神經網路(Convolutional Neural Network, CNN)等。機器學習建模功能讓使用者透過簡單的網頁操作介面即可針對資料建立機器學習分析模型，並產生分析模型架構之程式原始碼。這可降低機器學習建模相關技術之學習成本，簡化建模過程並提升建模效率；另外，分析模型共享功能可讓使用者透過瀏覽器界面上傳及下載建模之超參數、建模結果及模型之使用情境，並可透過搜尋功能讓使用者找出最適當的共享資料分析模型，節省從頭開始建模的成本。 ;The Cyber-Physical System (CPS) is critical for smart manufacturing of the Industry 4.0 vision. It integrates advanced technologies, such as Industrial Internet of Things (IIoT), Big Data, Cloud Computing, and Artificial Intelligence (AI), to improve production efficiency, reduce production costs and increase production quality. The CPS collects, preprocesses, and analyzes data of manufacturing parameters and states, and feedbacks analyzed results, which contain deep-insight information of the data, to the manufacturing system for realizing online smart control of the whole system. Machine learning plays an important role in data analysis of the CPS. Its objective is to find specific patterns or hidden knowledge in the data through analyzing a large amount of historical data. However, it takes much effort to realize data analysis using machine leaning mechanisms. This motivates us to design and implement machine learning modeling modules for a web-based CPS construction assistant “PINE”. The modules integrate PINE’s web-based CPS development framework and common machine learning packages. The supported machine learning models include Support Vector Machine (SVM), Support Vector Regression (SVR), Deep Neural Network (DNN), and Convolutional Neural Network (CNN). The modules allow users to set and select hyper-parameters for machine learning modeling through simple web interfaces. The source code related to the modeling is generated, so that users can modify it at will. Moreover, the hyper-parameter tuning process for training the model, the best model ever trained, and the model usage information (i.e., the input, output and function of the model) are kept for model reusing and sharing. By establishing metadata for models, users can even search the most appropriate model with certain functions to be applied to specific applications. Many test cases are made and tested to make sure that the implementation meets the design specifications. We believe the design and implementation of machine learning modeling can facilitate users to construct a CPS.
顯示於類別:	[資訊工程研究所] 博碩士論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	114	檢視/開啟

在NCUIR中所有的資料項目都受到原著作權保護.

社群 sharing

資料載入中.....