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Scientific and technical journal established by OSTU. Media registration number: ПИ № ФС77-75780 dated May 23, 2019. ISSN: 2220-4245. Subscription index in the online catalog «Subscription Press» (www.akc.ru): E28002. Subscription to the electronic version is available on the «Rucont» platform.
The journal is included in the Russian Science Citation Index and in the List of Russian Scientific Journals .

Search results

  • V.1(29), 2017
    2-9

    The uninterrupted state evaluation of backward traction circuit in heavyweight traffic conditions

    In the article there is the increase of rail circuits work stability in conditions of heavyweight traffic for AC traction sections. There is a coordinate plane graph of backward traction circuit base diagnostic states in the paper. We analyzed the results gained in the researching and suggested the way to coordinate rail shunt location and to evaluate the insulation resistance in backward traction circuit.
  • V.1(17), 2014
    94-99

    Application of the apparatus of conformal mappings for continuous monitoring coordinates rolling stock on the railway

    This paper deals with the determination of the coordinates of the rolling stock for the railway station. Proposed and justified the use of mathematical tools of conformal mappings.
  • V.3(19), 2014
    104-109

    Continuous monitoring of coordinates rolling stock on the railway track of hump yard

    This paper deals with the determination of the coordinates of the rolling stock for the railway station. Proposed and justified the use of mathematical tools of conformal mappings. The results of the experiment are shown.
  • V.4(32), 2017
    111-121

    A mathematical model of a track circuit for data set generationin machine classification applications

    The quality of a track circuit monitoring system is defined to the great extent by its ability to automatically analyse the acquired data, i. e. identify the state of a track circuit. Machine learning techniques can be applied to implement this functionality. Designing a machine learning algorithm requires a learning data set from the subject domain. In this article we discuss a basic principle of track circuit mathematical model design that could be used to generate such data set. We also apply this principle and a combination of some existing methods to design and demonstrate a mathematical model of the 25 Hz AC track circuit.