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Paper Abstract and Keywords
Presentation 2023-09-21 17:55
Development and Evaluation of Construction Method of Machine Learning Model for Automated Design of Automotive Ethernet
Yasuhiro Mori, Hiroshi Yamamoto (Ritsumeikan Univ.), Yojiro Suyama, Tatsuya Izumi (Sumitomo Electric Industries, Ltd.), Hirofumi Urayama (AutoNetwork Technologies, Ltd.), Shiho Kobayashi, Hideaki Tani (Sumitomo Electric System Solutions, Ltd) IA2023-20
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, Ethernet is attracting attention as a technology for constructing in-vehicle networks. As a result, there is a concern that bottlenecks may occur. In the existing study, by repeatedly conducting simulations, appropriate QoS control parameters to prevent the occurrence of the bottlenecks can be identified for the in-vehicle Ethernet. However, it is difficult to run the dedicated simulation software using only the onboard computing resources. Therefore, we propose a new automated design system of in-vehicle Ethernet utilizing a machine learning technology. In the proposed system, the ECU has a function of operating the lightweight machine learning model that estimates the presence and severity of bottlenecks on the network. Here, the machine learning model is constructed based on the results of computer simulations conducted beforehand, but it is required to conduct a vast number of simulations with various settings. Therefore, we propose a method for efficiently collecting training data by managing the simulation logs conducted beforehand.
Keyword (in Japanese) (See Japanese page) 
(in English) IoT / In-vehicle Ethernet / QoS controll / Machine Learning / Meta-heuristic / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 193, IA2023-20, pp. 54-59, Sept. 2023.
Paper # IA2023-20 
Date of Issue 2023-09-14 (IA) 
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 IA2023-20

Conference Information
Committee IA  
Conference Date 2023-09-21 - 2023-09-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univeristy 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet Operation and Management, Network Architecture, Communication Protocols, IoT, etc. 
Paper Information
Registration To IA 
Conference Code 2023-09-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Development and Evaluation of Construction Method of Machine Learning Model for Automated Design of Automotive Ethernet 
Sub Title (in English)  
Keyword(1) IoT  
Keyword(2) In-vehicle Ethernet  
Keyword(3) QoS controll  
Keyword(4) Machine Learning  
Keyword(5) Meta-heuristic  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Yasuhiro Mori  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Hiroshi Yamamoto  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
3rd Author's Name Yojiro Suyama  
3rd Author's Affiliation Sumitomo Electric Industries, Ltd. (Sumitomo Electric Industries, Ltd.)
4th Author's Name Tatsuya Izumi  
4th Author's Affiliation Sumitomo Electric Industries, Ltd. (Sumitomo Electric Industries, Ltd.)
5th Author's Name Hirofumi Urayama  
5th Author's Affiliation AutoNetwork Technologies, Ltd. (AutoNetwork Technologies, Ltd.)
6th Author's Name Shiho Kobayashi  
6th Author's Affiliation Sumitomo Electric System Solutions, Ltd (Sumitomo Electric System Solutions, Ltd)
7th Author's Name Hideaki Tani  
7th Author's Affiliation Sumitomo Electric System Solutions, Ltd (Sumitomo Electric System Solutions, Ltd)
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Speaker Author-1 
Date Time 2023-09-21 17:55:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2023-20 
Volume (vol) vol.123 
Number (no) no.193 
Page pp.54-59 
#Pages 6 
Date of Issue 2023-09-14 (IA) 

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