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Paper Abstract and Keywords
Presentation 2019-12-20 15:15
Classification of railway stop positions by machine learning using on-board equipment accumulated data
Naohiro Morishima, Ryota kouduki, Tomoki Kobayashi, Yukiko Sugimoto (Kyosan), Takeshi Mizuma, Upvinder Singh Upvinder, Shiva Krishna Maheshuni (The University of Tokyo) DC2019-82
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the development of AI technology has been remarkable. Analysis using machine learning is attracting attention in the railway industry, and it is expected to be used in the field of operation and maintenance. On the other hand, there are a number of problems related to data collection, such as the need to install equipment that is different from the original function. In this study, we focus on the accumulated data of the on-board equipment and investigate whether it is possible to adapt machine learning without adding new equipment by classifying the stop state..
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / on-board equipment / stop position classification / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 351, DC2019-82, pp. 21-23, Dec. 2019.
Paper # DC2019-82 
Date of Issue 2019-12-13 (DC) 
ISSN Print edition: ISSN 0913-5685  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 DC  
Conference Date 2019-12-20 - 2019-12-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Topics (in English)  
Paper Information
Registration To DC 
Conference Code 2019-12-DC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Classification of railway stop positions by machine learning using on-board equipment accumulated data 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) on-board equipment  
Keyword(3) stop position classification  
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1st Author's Name Naohiro Morishima  
1st Author's Affiliation Kyosan Electric Manufacturing Co.,Ltd. (Kyosan)
2nd Author's Name Ryota kouduki  
2nd Author's Affiliation Kyosan Electric Manufacturing Co.,Ltd. (Kyosan)
3rd Author's Name Tomoki Kobayashi  
3rd Author's Affiliation Kyosan Electric Manufacturing Co.,Ltd. (Kyosan)
4th Author's Name Yukiko Sugimoto  
4th Author's Affiliation Kyosan Electric Manufacturing Co.,Ltd. (Kyosan)
5th Author's Name Takeshi Mizuma  
5th Author's Affiliation The University of Tokyo (The University of Tokyo)
6th Author's Name Upvinder Singh Upvinder  
6th Author's Affiliation The University of Tokyo (The University of Tokyo)
7th Author's Name Shiva Krishna Maheshuni  
7th Author's Affiliation The University of Tokyo (The University of Tokyo)
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Speaker
Date Time 2019-12-20 15:15:00 
Presentation Time 25 
Registration for DC 
Paper # IEICE-DC2019-82 
Volume (vol) IEICE-119 
Number (no) no.351 
Page pp.21-23 
#Pages IEICE-3 
Date of Issue IEICE-DC-2019-12-13 


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