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 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) |
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EMCJ2022-34 |
Conference Information |
Committee |
EMD WPT EMCJ PEM |
Conference Date |
2022-07-15 - 2022-07-15 |
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(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 |
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Keyword(1) |
Deep learning |
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EMF monitoring |
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Machine learning |
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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 |
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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 |
5 |
Date of Issue |
2022-07-08 (EMCJ) |
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