Machine classification of code signals in electric train warning systems
Automatic train warning systems beeing currently in service on Russian railways use electric track circuits as signal communication media. Electric signals transmitted through a track circuit often get corrupted by the noise produced by electric locomotives and other sources. This, in most case...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education
2019-10-01
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| Series: | Омский научный вестник |
| Subjects: | |
| Online Access: | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2019/4%20(166)/39-47%20%D0%9F%D1%80%D0%B8%D1%81%D1%83%D1%85%D0%B8%D0%BD%D0%B0%20%D0%98.%20%D0%92.,%20%D0%91%D0%BE%D1%80%D0%B8%D1%81%D0%B5%D0%BD%D0%BA%D0%BE%20%D0%94.%20%D0%92..pdf |
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| Summary: | Automatic train warning systems beeing currently in service on
Russian railways use electric track circuits as signal communication
media.
Electric signals transmitted through a track circuit often get
corrupted by the noise produced by electric locomotives and
other sources. This, in most cases, causes errors in automatic
train warning systems and temporarily disrupts the operation of
a railway.
To improve the stability of such systems while receiving signals
from a track circuit, we propose a machine classification algorithm
based on a neural network.
In this article, we describe all the stages of this algorithm and
discuss the architecture of a neural network for classification of an
electric signal received from a track circuit. We also demonstrate
the successful application of the algorithm for receiving a noisy
electric signal which currently used automatic train warning
systems fail to decode. |
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| ISSN: | 1813-8225 2541-7541 |