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Development of the railway point electric heating intellectual control algorithm

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article reviews and describes the problems of the railway point heating conventional control system considering its structure, simple design, disadvantages and nonconformity. As a solution to the described problem, an innovative and advanced point heating control algorithm is proposed based on Mathlab’s Fuzzy Logic Designer module, which will allow control of heating more effectively and intellectually. The tasks for the advanced and intellectual point heating control system were set. The interdependency of different input variables and their weights of the proposed algorithm are shown and described. Conclusions show that the approach of introducing a control algorithm based on Fuzzy Logic will allow to control point heating in a more advanced and efficient way -switching on and off based on the interdependency of different weather conditions and eather forecast; the input data control system will decide automatically when to switch the heating on and off.
Czasopismo
Rocznik
Strony
71--79
Opis fizyczny
Bibliogr. 15 poz.
Twórcy
  • Riga Technical University, Azenes st. 12-K1, LV-1048, Riga, Latvia
  • Riga Technical University, Azenes st. 12-K1, LV-1048, Riga, Latvia
autor
  • Riga Technical University, Azenes st. 12-K1, LV-1048, Riga, Latvia
Bibliografia
  • 1. Теег, Г. & Власенко, С. (ред.). Системы автоматики и телемеханики на железных дорогах мира. Москва: Интекст. 2009. 496 p. [In Russian: Teeg, G. & Vlasenko, S. (eds.). Automation and telematics systems on the world’s railways. Moscow: Intext].
  • 2. Kargin, A. & Panchenko, S. & Vasiljevs, A. & Petrenko, T. Implementation of cognitive perception functions in fuzzy situational control model. Procedia Computer Science. 2019. Vol. 149. P. 231-238.
  • 3. Panchenko, S. & Siroklyn, I. & Lapko, A. & Kameniev, A. & Buss, D. Critical failures of turnouts: expert approach. Procedia Computer Science. 2019. Vol. 149. P. 422-429.
  • 4. Леоненков, А.В. Нечеткое моделирование в среде MATLAB и fuzzyTECH. Санкт-Петербург: БХВ-Петербург. 2005. 736 p. [In Russian: Leonenkov, A.V. Fuzzy modelling in MATLAB and fuzzyTECH. Saint-Petersburg: BHV- Petersburg].
  • 5. Dorokhov, O. & Dorokhova, L. Fuzzy model in fuzzyTECH environment for the evaluation of transportation’s quality for cargo enterprises in Ukraine. Transport and Telecommunication. 2011. Vol. 12. No. 1. P. 25-33.
  • 6. Dorokhov, O. & Chernov, V. The desirability and feasibility of using fuzzy modeling for risk assessment in information systems. Securitatea informaţională. The VII Conf. Intern. Chichineu. 2010. P. 18-22.
  • 7. Lukasik, Z. & Nowakowski, W. & Ciszewski, T. & Freimane, J. A fault diagnostic methodology for railway automatics systems. Procedia Computer Science. 2019. Vol. 149. P. 159-166.
  • 8. Ribickis, L. & Gorobecs, M. & Ļevčenkovs, A. Neuro-Immune Algorithm for Embedded Real-Time Control System in Transport Safety Tasks. Frontiers in Artificial Intelligence and Applications. Las Palmas de Gran Canaria. 2018. P. 251-265.
  • 9. Klir, G.J. & Yuan, B. Fuzzy Sets and Fuzzy Logic: Theory and Applications. New Jersey: Prentice Hall. 1995. 592 p.
  • 10. Горелик, А.В. (ред.). Системы железнодорожной автоматики, телемеханики и связи. Москва: Учебно-методический центр по образованию на железнодорожном транспорте. 2012. 205 p. [In Russian: Gorelik, A.V. (ed.). Systems of railway automation, telemechanics and communication. Moscow: Training centre for education in railway transport].
  • 11. Munakata, T. Fundamentals of the New Artificial Intelligence. London: Springer. 2008. 256 p.
  • 12. Ross, T.J. Fuzzy logic with engineering applications. Third Edition. Chichester: John Wiley & Sons. 2010. 585 p.
  • 13. Latvian Railway - Key Performance Indicators. Available at: https://www.ldz.lv/sites/default/files/LDZ-raditaji-2019-LV-web.pdf.
  • 14. Сапожников, В.В. & Сапожников, Вл.В. & Шаманов, В.И. Надежность систем железнодорожной автоматики, телемеханики и связи. Москва: Маршрут. 2003. 263 p. [In Russian: Sapozhnikov, V.V. & Sapozhnikov, Vl.V. & Shamanov, V.I. Reliability of railway automation, telemechanics and communication systems. Moscow: Marshrut].
  • 15. Russell, S. & Norvig, P. Artificial intelligence: a modern approach. New Jersey: Prentice Hall. 2009. 932 p.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a1b4733f-b2ad-48d7-83f4-e158ff69dcef
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