AnoGAN-Based Anomaly Filtering for Intelligent Edge Device in Smart Factory
Maintenance of production equipment and controlling products quality through data analysis are the main issues of smart factory. During production, detected data for analysis is showing abnormal data more than normal data. Therefore, there is lots of energy consumption for analysis, cost, and saving...
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Published in | 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM) pp. 1 - 6 |
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Main Authors | , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
04.01.2021
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Subjects | |
Online Access | Get full text |
DOI | 10.1109/IMCOM51814.2021.9377409 |
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Abstract | Maintenance of production equipment and controlling products quality through data analysis are the main issues of smart factory. During production, detected data for analysis is showing abnormal data more than normal data. Therefore, there is lots of energy consumption for analysis, cost, and saving of data. Edge Device which applied deep learning algorithm is able to solve this problem. In this paper, a framework for data filtering method before data analysis is proposed through Anomaly detection using single board computer (SBC). Using Nvidia Jetson nano and desktop computer to compare and analyze the two virtual environments to determine the framework of optimum anomaly data filtering. AnoGAN is a deep learning model utilized for anomaly detection. |
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AbstractList | Maintenance of production equipment and controlling products quality through data analysis are the main issues of smart factory. During production, detected data for analysis is showing abnormal data more than normal data. Therefore, there is lots of energy consumption for analysis, cost, and saving of data. Edge Device which applied deep learning algorithm is able to solve this problem. In this paper, a framework for data filtering method before data analysis is proposed through Anomaly detection using single board computer (SBC). Using Nvidia Jetson nano and desktop computer to compare and analyze the two virtual environments to determine the framework of optimum anomaly data filtering. AnoGAN is a deep learning model utilized for anomaly detection. |
Author | Jeong, Jongpil Oh, Seokju Kim, Donghyun Cha, Jaegyeong |
Author_xml | – sequence: 1 givenname: Donghyun surname: Kim fullname: Kim, Donghyun email: donghyun.kim@g.skku.edu organization: Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Republic of Korea – sequence: 2 givenname: Jaegyeong surname: Cha fullname: Cha, Jaegyeong email: sean9887@naver.com organization: Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Republic of Korea – sequence: 3 givenname: Seokju surname: Oh fullname: Oh, Seokju email: kas7189@g.skku.edu organization: Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Republic of Korea – sequence: 4 givenname: Jongpil surname: Jeong fullname: Jeong, Jongpil email: jpjeong@skku.edu organization: Sungkyunkwan University,Department of Smart Factory Convergence,Suwon,Republic of Korea |
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Snippet | Maintenance of production equipment and controlling products quality through data analysis are the main issues of smart factory. During production, detected... |
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SubjectTerms | AnoGAN Anomaly detection Data analysis Deep learning Edge Intelligence Device Filtering IIoT Image edge detection Smart manufacturing Virtual environments |
Title | AnoGAN-Based Anomaly Filtering for Intelligent Edge Device in Smart Factory |
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