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研究生：	李奇 Li, Chi
論文名稱：	基於智慧型工廠安全之辨識模型推薦系統 The Recommendation System of Identification Models for the Safety of Smart Factories
指導教授：	鄭憲宗 Cheng, Sheng-Tzong
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	30
中文關鍵詞：	智慧型工廠安全、推薦系統、卷積神經網路、支持向量機、多項式回歸
外文關鍵詞：	Smart Factory Security, Recommendation System, Convolutional Neural Network, Support Vector Machine, polynomial Regression
相關次數：	點閱：94 下載：0
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近年來人工智慧的成功帶來了各式各樣的領域結合應用，其中在工業上更是提出了智慧工廠的概念。工廠中的製程智慧化了，安全系統也智慧化了，但面對眾多類型的工廠往往有不同的危害，每一個智慧安全系統都需要重新研究與設計，我們期望改善這項缺失，由系統幫我們推薦能使用的智慧安全模型。本篇論文提出了將模型對於問題的準確度視為回歸問題，並以其結果預測模型的準確度，以此作為過濾模型的標準，讓推薦系統在選擇推薦模型上有更好的效果。

In recent years, the success of artificial intelligence has brought together a variety of fields, and in the industry, the concept of a smart factory has been proposed. The process in the factory is intelligent, and the security system is also intelligent. However, in the face of many types of factories, there are often different hazards. Every smart security system needs to be re-researched and designed. We hope to improve this deficiency. We recommend a smart security model that can be used. This paper proposes to consider the accuracy of the model as a regression problem and use the results to predict the accuracy of the model as a criterion for filtering the model. Let the recommendation system have a better effect in selecting the recommended model.

摘要 I
Abstract II
ACKNOWLEDGMENT III
TABLE OF CONTENTS IV
LIST OF TABLES VI
LIST OF FIGURES VII
Chapter 1.  Introduction and Motivation 1
Chapter 2.  Background and Related work 4
2.1 Content-based Recommendation 4
2.2 Algorithm-related trends 6
2.3 Convolution Neural Network 8
2.3.1 Bone of CNNs 8
2.3.2 Convolution 9
2.3.3 Pooling 10
2.4 Support Vector Machine 11
2.4.1 Overview of SVM 11
2.4.2 Kernel Selection of SVM 14
2.4.3 Model Selection of SVM 15
Chapter 3. System Design and Approach 17
3.1 Problem Description 17
3.2 System Design 18
3.2.1 Multiple Model Training 20
3.2.2 Model Filter 21
3.2.3 Model Select 24
Chapter 4. Experiments 25
4.1 Environment 25
4.2 Experiments 25
4.2.1 Data classification experiment 25
Chapter 5. Conclusions and Future Work 29
Reference 30

                                    

[1] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in ICLR, 2015.
[2] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” IEEE Conference on Computer Vision and Pattern Recognition, 2014
[3] Joseph Redmon, Santosh Divvala, and Ross Girshick, Ali Farhadi, " You Only Look Once: Unified, Real-Time Object Detection," IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
[4] Pasquale Lops, Dietmar Jannach, Cataldo Musto , Toine Bogers , and Marijn Koolen, “Trends in content-based recommendation,” User Modeling and User-Adapted Interaction,29(2),2019,pp. 239-249
[5] María Hernández-Rubio,Iván Cantador, and Alejandro Bellogín, “A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews,” User Modeling and User-Adapted Interaction,29(2),2019,pp. 381-441
[6] Alexander Vergara, Shankar Vembu,Tuba Ayhan, Margaret A.Ryan, Margie L.Homer and Ramón Huerta, "Chemical gas sensor drift compensation using classifier ensembles," Sensors and Actuators B: Chemical Volumes 166–167, 2012, Pages 320-329

校內：2024-07-24公開
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