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Paper Abstract and Keywords
Presentation 2021-12-17 11:15
Building Surrogate Model Using Convolutional Autoencoder for Fast Frequency Response Calculation of Planar BPFs
Ren Shibata, Masataka Ohira, Ma Zhewang (Saitama Univ.) MW2021-99 Link to ES Tech. Rep. Archives: MW2021-99
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, surrogate models using deep learning are introduced to speed up electromagnetic (EM) analysis. For instance, a surrogate model, of which the input is image of geometry under analysis and the output is its characteristics, has been constructed by convolutional neural network. However, a large amount of EM analysis results is required to build such surrogate models. To solve this problem, this paper proposes a novel surrogate model consisting of an encoder and fully connected layers for an extremely fast calculation of frequency response of planar BPFs (bandpass filters). In the proposed surrogate model, the amount of data prepared using EM analysis can be reduced by a pre-generated encoder that can extract features of geometry of BPFs from their images. As an example, we construct the surrogate model for third-order planar BPF by using convolutional autoencoder and transfer learning. The generalization performance of the surrogate model is evaluated by comparing the calculation accuracy of frequency responses between the proposed and conventional approaches.
Keyword (in Japanese) (See Japanese page) 
(in English) convolutional autoencoder / planar bandpass filter / surrogate model / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 303, MW2021-99, pp. 85-90, Dec. 2021.
Paper # MW2021-99 
Date of Issue 2021-12-09 (MW) 
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)
Download PDF MW2021-99 Link to ES Tech. Rep. Archives: MW2021-99

Conference Information
Committee MW  
Conference Date 2021-12-16 - 2021-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Kawasaki City Industrial Promotion Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Microwave/Presentation and Award Ceremony of SDC 
Paper Information
Registration To MW 
Conference Code 2021-12-MW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Building Surrogate Model Using Convolutional Autoencoder for Fast Frequency Response Calculation of Planar BPFs 
Sub Title (in English)  
Keyword(1) convolutional autoencoder  
Keyword(2) planar bandpass filter  
Keyword(3) surrogate model  
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1st Author's Name Ren Shibata  
1st Author's Affiliation Saitama University (Saitama Univ.)
2nd Author's Name Masataka Ohira  
2nd Author's Affiliation Saitama University (Saitama Univ.)
3rd Author's Name Ma Zhewang  
3rd Author's Affiliation Saitama University (Saitama Univ.)
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Speaker Author-1 
Date Time 2021-12-17 11:15:00 
Presentation Time 25 minutes 
Registration for MW 
Paper # MW2021-99 
Volume (vol) vol.121 
Number (no) no.303 
Page pp.85-90 
#Pages
Date of Issue 2021-12-09 (MW) 


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