Paper Abstract and Keywords |
Presentation |
2018-10-19 13:35
Underground Model Inversion from GPR Images by Deep Learning Using Generative Adversarial Networks Jun Sonoda (NIT, Sendai), Tomoyuki Kimoto (NIT, Oita) EMCJ2018-53 MW2018-89 EST2018-75 Link to ES Tech. Rep. Archives: MW2018-89 EST2018-75 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, deterioration of social infrastructures such as tunnels and bridges become a serious social problem. It is required to rapidly and accurately detect for abnormal parts of the social infrastructures. The ground penetrating radar (GPR) is efficient for the social infrastructure inspection. However, it is difficult to identify the material and size of the underground object from the radar image obtained the GPR. To objectively and quantitatively investigate from the GPR images by the deep learning, we have automatically and massively generated the GPR images by a fast finite-difference time-domain (FDTD) simulation with graphics processing units (GPUs), and it has been learned the underground object using a deep convolutional neural network (CNN), with the generated GPR images. As the results, we have obtained multilayer layers CNN can identify six materials and size with roughly more than 80% accuracy in some inhomogeneous underground. In this study, to estimate the underground objects from the GPR images, we have developed an underground model inversion from the GPR images by the deep learning using the generative adversarial networks (GAN). |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
generative adversarial networks / deep learning / ground penetrating radar / inversion / FDTD method / GPU / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 249, EST2018-75, pp. 115-119, Oct. 2018. |
Paper # |
EST2018-75 |
Date of Issue |
2018-10-11 (EMCJ, MW, EST) |
ISSN |
Print edition: ISSN 0913-5685 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 |
EMCJ2018-53 MW2018-89 EST2018-75 Link to ES Tech. Rep. Archives: MW2018-89 EST2018-75 |
Conference Information |
Committee |
EST MW EMCJ IEE-EMC |
Conference Date |
2018-10-18 - 2018-10-19 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Hachinohe Chamber of Commerce and Industry(Hachinohe city, Aomori) |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Simulation techniques, EMC, Microwave, Electromagnetic field simulation, etc. |
Paper Information |
Registration To |
EST |
Conference Code |
2018-10-EST-MW-EMCJ-EMC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Underground Model Inversion from GPR Images by Deep Learning Using Generative Adversarial Networks |
Sub Title (in English) |
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generative adversarial networks |
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deep learning |
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ground penetrating radar |
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inversion |
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FDTD method |
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GPU |
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1st Author's Name |
Jun Sonoda |
1st Author's Affiliation |
National Institute of Technology, Sendai College (NIT, Sendai) |
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Tomoyuki Kimoto |
2nd Author's Affiliation |
National Institute of Technology, Oita College (NIT, Oita) |
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Speaker |
Author-1 |
Date Time |
2018-10-19 13:35:00 |
Presentation Time |
25 minutes |
Registration for |
EST |
Paper # |
EMCJ2018-53, MW2018-89, EST2018-75 |
Volume (vol) |
vol.118 |
Number (no) |
no.247(EMCJ), no.248(MW), no.249(EST) |
Page |
pp.115-119 |
#Pages |
5 |
Date of Issue |
2018-10-11 (EMCJ, MW, EST) |
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