@inproceedings{0be78c8be0ff48bb868196a4f81499a0,
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}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 ; Conference date: 03-10-2019 Through 06-10-2019",
year = "2019",
month = oct,
doi = "10.1109/ECICE47484.2019.8942689",
language = "???core.languages.en_GB???",
series = "2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "478--481",
editor = "Teen-Hang Meen",
booktitle = "2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019",
}
