Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant

Yi Chang Yang; Jehn Ruey Jiang

doi:10.1109/ECICE47484.2019.8942689

Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant

Yi Chang Yang
, Jehn Ruey Jiang

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The Cyber-Physical System (CPS) is critical for smart manufacturing of the Industry 4.0 vision. This study shows the design and implementation of machine learning modeling modules for a web-based CPS construction assistant, called PINE. The modules make easy the modeling of support vector classification (SVC), support vector regression (SVR), deep neural network (DNN), and convolutional neural network (CNN). They facilitate users to set modeling hyper-parameters and can generate source codes for the modeling. Examples are given to show how to use the modules to assist in training CNN models for an automated optical inspection (AOI) system.

Original language	English
Title of host publication	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
Editors	Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	478-481
Number of pages	4
ISBN (Electronic)	9781728125015
DOIs	https://doi.org/10.1109/ECICE47484.2019.8942689
State	Published - Oct 2019
Event	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 - Yunlin, Taiwan Duration: 3 Oct 2019 → 6 Oct 2019

Publication series

Name	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

Conference

Conference	2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
Country/Territory	Taiwan
City	Yunlin
Period	3/10/19 → 6/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

convolutional neural network
deep neural network
machine learning
smart manufacturing
support vector machine

Access to Document

10.1109/ECICE47484.2019.8942689

Cite this

Yang, Y. C., & Jiang, J. R. (2019). Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant. In T.-H. Meen (Ed.), 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 (pp. 478-481). Article 8942689 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECICE47484.2019.8942689

Yang, Yi Chang ; Jiang, Jehn Ruey. / Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant. 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. editor / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 478-481 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019).

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title = "Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant",

abstract = "The Cyber-Physical System (CPS) is critical for smart manufacturing of the Industry 4.0 vision. This study shows the design and implementation of machine learning modeling modules for a web-based CPS construction assistant, called PINE. The modules make easy the modeling of support vector classification (SVC), support vector regression (SVR), deep neural network (DNN), and convolutional neural network (CNN). They facilitate users to set modeling hyper-parameters and can generate source codes for the modeling. Examples are given to show how to use the modules to assist in training CNN models for an automated optical inspection (AOI) system.",

keywords = "convolutional neural network, deep neural network, machine learning, smart manufacturing, support vector machine",

author = "Yang, \{Yi Chang\} and Jiang, \{Jehn Ruey\}",

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series = "2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019",

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Yang, YC & Jiang, JR 2019, Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant. in T-H Meen (ed.), 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019., 8942689, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, Institute of Electrical and Electronics Engineers Inc., pp. 478-481, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019, Yunlin, Taiwan, 3/10/19. https://doi.org/10.1109/ECICE47484.2019.8942689

Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant. / Yang, Yi Chang; Jiang, Jehn Ruey.
2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. ed. / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2019. p. 478-481 8942689 (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The Cyber-Physical System (CPS) is critical for smart manufacturing of the Industry 4.0 vision. This study shows the design and implementation of machine learning modeling modules for a web-based CPS construction assistant, called PINE. The modules make easy the modeling of support vector classification (SVC), support vector regression (SVR), deep neural network (DNN), and convolutional neural network (CNN). They facilitate users to set modeling hyper-parameters and can generate source codes for the modeling. Examples are given to show how to use the modules to assist in training CNN models for an automated optical inspection (AOI) system.

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KW - deep neural network

KW - machine learning

KW - smart manufacturing

KW - support vector machine

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Yang YC, Jiang JR. Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant. In Meen TH, editor, 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 478-481. 8942689. (2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019). doi: 10.1109/ECICE47484.2019.8942689

Web-based Machine Learning Modeling in a Cyber-Physical System Construction Assistant

Abstract

Publication series

Conference

UN SDGs

Keywords

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