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Paper Abstract and Keywords
Presentation 2022-01-20 17:45
Anomaly detection based on the unity, duality and dependence of entropy -- Usefulness of Entropy, Kullback-Leibler divergence and Mutual Information --
Akira Imori (HIT), Shunji Maeda (HIT/*), Takashi Komatsu, Tetsuji Taniguchi (HIT/*), Osamu Toda IT2021-53 SIP2021-61 RCS2021-221
Abstract (in Japanese) (See Japanese page) 
(in English) Monitoring with multiple sensors is important for system diagnosis, and active researches are being conducted using machine learning. However, treating multiple sensors having different units may result in the loss of information necessary for anomaly detection, and the handling and interpretation of sensor data, which do not lose information are desired in the industry. In this paper, we analyze equipment deterioration and anomalies based on the changes of information entropy. That is to say, we show that entropy, Kullback-Leibler divergence and mutual information, which are the basic concepts of information theory, are of great value for anomaly detection and diagnosis. The keywords for each role are unification, duality and dependency.
Keyword (in Japanese) (See Japanese page) 
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Reference Info. IEICE Tech. Rep., vol. 121, no. 327, IT2021-53, pp. 137-142, Jan. 2022.
Paper # IT2021-53 
Date of Issue 2022-01-13 (IT, SIP, RCS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IT2021-53 SIP2021-61 RCS2021-221

Conference Information
Committee RCS SIP IT  
Conference Date 2022-01-20 - 2022-01-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IT 
Conference Code 2022-01-RCS-SIP-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Anomaly detection based on the unity, duality and dependence of entropy 
Sub Title (in English) Usefulness of Entropy, Kullback-Leibler divergence and Mutual Information 
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1st Author's Name Akira Imori  
1st Author's Affiliation Hiroshima Institute of Technology (HIT)
2nd Author's Name Shunji Maeda  
2nd Author's Affiliation Hiroshima Institute of Technology/Math. Research Institute Calc for Industry (HIT/*)
3rd Author's Name Takashi Komatsu  
3rd Author's Affiliation Math. Research Institute Calc for Industry (*)
4th Author's Name Tetsuji Taniguchi  
4th Author's Affiliation Hiroshima Institute of Technology/Math. Research Institute Calc for Industry (HIT/*)
5th Author's Name Osamu Toda  
5th Author's Affiliation MEIDENSHA CORPORATION (*)
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Speaker Author-1 
Date Time 2022-01-20 17:45:00 
Presentation Time 25 minutes 
Registration for IT 
Paper # IT2021-53, SIP2021-61, RCS2021-221 
Volume (vol) vol.121 
Number (no) no.327(IT), no.328(SIP), no.329(RCS) 
Page pp.137-142 
#Pages 6 
Date of Issue 2022-01-13 (IT, SIP, RCS) 

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