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Paper Abstract and Keywords
Presentation 2022-07-15 13:50
Prediction of E-field Distribution in Indoor Environments Using Deep Learning Technique
Liu Sen, Onishi Teruo, Taki Masao, Watanabe Soichi (NICT) EMCJ2022-34
Abstract (in Japanese) (See Japanese page) 
(in English) As one of the important aspects of monitoring electromagnetic field (EMF) exposure levels, comprehensively grasping the electric-field (E-field) distribution in an indoor environment is challenging. Essentially, it belongs to creating a surrogate model in a highly uncertain and variable condition. Benefit from the advancements in deep learning, in this paper, we present two prediction models with one based on fully-connected neural networks (FCNNs) and the other one based on graph neural networks (GNNs). The models are trained upon a same dataset, and are compared to each other. We demonstrate that both models can be used to predict the field distribution in a floorplan geometry that is beyond the training data. We emphasize that GNNs are more powerful than FCNNs due to their non-Euclidean data handling feature.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / EMF monitoring / Machine learning / Surrogate model / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 112, EMCJ2022-34, pp. 1-5, July 2022.
Paper # EMCJ2022-34 
Date of Issue 2022-07-08 (EMCJ) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee EMD WPT EMCJ PEM  
Conference Date 2022-07-15 - 2022-07-15 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To EMCJ 
Conference Code 2022-07-EMD-WPT-EMCJ-PEM 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Prediction of E-field Distribution in Indoor Environments Using Deep Learning Technique 
Sub Title (in English)  
Keyword(1) Deep learning  
Keyword(2) EMF monitoring  
Keyword(3) Machine learning  
Keyword(4) Surrogate model  
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1st Author's Name Liu Sen  
1st Author's Affiliation National Institute of Information and Communications Technology (NICT)
2nd Author's Name Onishi Teruo  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
3rd Author's Name Taki Masao  
3rd Author's Affiliation National Institute of Information and Communications Technology (NICT)
4th Author's Name Watanabe Soichi  
4th Author's Affiliation National Institute of Information and Communications Technology (NICT)
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Speaker Author-1 
Date Time 2022-07-15 13:50:00 
Presentation Time 25 minutes 
Registration for EMCJ 
Paper # EMCJ2022-34 
Volume (vol) vol.122 
Number (no) no.112 
Page pp.1-5 
#Pages
Date of Issue 2022-07-08 (EMCJ) 


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