| Presentation | 2019-12-20 Classification of railway stop positions by machine learning using on-board equipment accumulated data Naohiro Morishima, Ryota kouduki, Tomoki Kobayashi, Yukiko Sugimoto, Takeshi Mizuma, Upvinder Singh Upvinder, Shiva Krishna Maheshuni, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Machine learning / on-board equipment / stop position classification |
| Paper # | DC2019-82 |
| Date of Issue | 2019-12-13 (DC) |
| Conference Information | |
| Committee | DC |
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| Conference Date | 2019/12/20(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Satoshi Fukumoto(Tokyo Metropolitan Univ.) |
| Vice Chair | Hiroshi Takahashi(Ehime Univ.) |
| Secretary | Hiroshi Takahashi(Nihon Univ.) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Dependable Computing |
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| Language | JPN |
| 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 |
| 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) |
| Date | 2019-12-20 |
| Paper # | DC2019-82 |
| Volume (vol) | vol.119 |
| Number (no) | DC-351 |
| Page | pp.pp.21-23(DC), |
| #Pages | 3 |
| Date of Issue | 2019-12-13 (DC) |