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...

Full description

Saved in:
Bibliographic Details
Published in2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM) pp. 1 - 6
Main Authors Kim, Donghyun, Cha, Jaegyeong, Oh, Seokju, Jeong, Jongpil
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.01.2021
Subjects
Online AccessGet full text
DOI10.1109/IMCOM51814.2021.9377409

Cover

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.
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
BookMark eNotj71OwzAYRY0EAy08AQN-gQQ7dvwzhtCUiJYOwFzZzufIUuqg1ELK2xOpna7OcnTuCt3GMQJCz5TklBL90u7rw76kivK8IAXNNZOSE32DVlSIkheMKnWPPqo4bqvP7NWcocMLnMww4yYMCaYQe-zHCbcxwTCEHmLCm64H_AZ_wQEOEX-dzJRwY1wap_kB3XkznOHxumv002y-6_dsd9i2dbXLQiF0yjQDSzyx2mlXUCaVAsE6Bh3tLOWOGetlKZ0xShFBAZjQnltuuJXG-SV8jZ4u3gAAx98pLBHz8fqP_QOn-0rx
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/IMCOM51814.2021.9377409
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE/IET Electronic Library
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 1665423188
9781665423182
EndPage 6
ExternalDocumentID 9377409
Genre orig-research
GrantInformation_xml – fundername: IITP(Institute for Information & Communications Technology Planning & Evaluation)
  grantid: IITP-2020-2018-0-01417
  funderid: 10.13039/501100001809
– fundername: Ministry of Trade, Industry and Energy (MOTIE, Korea)
  grantid: SKN19ED
  funderid: 10.13039/501100003052
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i269t-93eb0f0b9c9c213788e63d3ed1db14c3abf757caa88061ee369f4b4a4b7acf423
IEDL.DBID RIE
IngestDate Thu Jun 29 18:38:24 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i269t-93eb0f0b9c9c213788e63d3ed1db14c3abf757caa88061ee369f4b4a4b7acf423
PageCount 6
ParticipantIDs ieee_primary_9377409
PublicationCentury 2000
PublicationDate 2021-01-04
PublicationDateYYYYMMDD 2021-01-04
PublicationDate_xml – month: 01
  year: 2021
  text: 2021-01-04
  day: 04
PublicationDecade 2020
PublicationTitle 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)
PublicationTitleAbbrev IMCOM
PublicationYear 2021
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.8572729
Snippet Maintenance of production equipment and controlling products quality through data analysis are the main issues of smart factory. During production, detected...
SourceID ieee
SourceType Publisher
StartPage 1
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
URI https://ieeexplore.ieee.org/document/9377409
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3NT8IwFG-Qkyc1YPxODx7dWNdupUdFJmiGJkrCjfTj1RBlGDMO-NfbjonRePDWNi9p09f29_o-ETpPLFMy8n5hxNiAScmCrlJxYBmNhIYUYu0_ivkoHYzZ7SSZNNDFJhYGACrnMwh9s7Llm4VeelVZx0EpZz5ab4tzsY7Vql22SCQ6w7x3nycOsbyqJCZhTf2jbEqFGtkOyr_mWzuLvITLUoX641cqxv8uaBe1v-Pz8MMGefZQA4oWunM_-ZvLUXDlcMlg15nL1xXOZt4c7qiwk07xcJOAs8R98wz4GvxLgWcFfpy7Q4SzqvzOqo3GWf-pNwjqUgnBLE5FGQgKKrKRElromPgc8ZBSQ8EQowjTVCrLE66ldNc1JQA0FY5JTDLFpbZOpNpHzWJRwAHCNmFOCFKGJ8CYAtq1pMut1KmgxtsYD1HLb8T0bZ0NY1rvwdHfw8do2zOjUlqwE9Qs35dw6mC8VGcV_z4BDbCezw
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3NT8IwFG8IHvSkBozf9uDRjXXtNnpUBEEYmggJN9KPV0OUYcw44F9vOyZG48Fb2zRp09f2974fQpeRYVIEzi-MaOMxIZjXlDL0DKMBVxBDqJygmA7j7pjdT6JJBV1tYmEAoHA-A981C1u-XqilU5U1LJQmzEXrbUVWqkjW0Vql0xYJeKOXth7SyGKWU5aExC_n_yicUuBGZxelXyuu3UVe_GUuffXxKxnjf7e0h-rfEXr4cYM9-6gCWQ31rSx_dz30biwyaWw7c_G6wp2ZM4jbWdjyp7i3ScGZ47Z-BnwL7q_Asww_ze01wp2iAM-qjsad9qjV9cpiCd4sjHnucQoyMIHkiquQuCzxEFNNQRMtCVNUSJNEiRLCPtiYANCYWzIxwWQilLFM1QGqZosMDhE2EbNskNRJBIxJoE1DmokRKuZUOyvjEaq5g5i-rfNhTMszOP57-AJtd0fpYDroDfsnaMcRplBhsFNUzd-XcGZBPZfnBS0_AZbsoiA
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2021+15th+International+Conference+on+Ubiquitous+Information+Management+and+Communication+%28IMCOM%29&rft.atitle=AnoGAN-Based+Anomaly+Filtering+for+Intelligent+Edge+Device+in+Smart+Factory&rft.au=Kim%2C+Donghyun&rft.au=Cha%2C+Jaegyeong&rft.au=Oh%2C+Seokju&rft.au=Jeong%2C+Jongpil&rft.date=2021-01-04&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FIMCOM51814.2021.9377409&rft.externalDocID=9377409