THEORETICAL FUNDAMENTALS OF ESTIMATABILITY ASSESSMENT OF TRAIN SITUATION SIGNS FOR WORK OF INTELLECTUAL LOCOMOTIVE CONTROL SYSTEMS
DOI:
https://doi.org/10.32703/2617-9040-2021-38-220-21Keywords:
locomotive, information sign, intelligent system, management, train situation, train management.Abstract
The article is devoted to the problem of implementation of intelligent control systems in transport. An important task is to assess the information parameters of the control systems. In the existing works the question of definition of one of the basic parameters of functioning of locomotive control systems - information value of separate signs of a train situation is not considered. This does not make it possible to determine the order of signal processing at the input and assess their contribution to the adoption of a control decision. Moreover, informativeness is a relative value, which is expressed in the different information value of a particular feature for the classification of different train situations. Also, the informativeness of the feature may depend on the type of decisive rules in the classification procedure. The quality of recognition of a train situation in which the locomotive crew is, depends on the quality of the features used by the classification system. The decisive criterion for the informativeness of the features in the problem of pattern recognition is the magnitude of losses from errors. To determine the range of the most informative features of train situations, the method of random search with adaptation was used. The results of the work make it possible to optimize the operation of automated and intelligent train control systems by reducing the amount of calculations and simplifying their algorithm.
References
ЛІТЕРАТУРА
T. Wen, G. Xie, Y. Cao and B. Cai, "A DNN-Based Channel Model for Network Planning in Train Control Systems," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2021.3093025.
Горобченко О., Неведров, O., Незліна, O., Ткаченко, В. Розробка методу кластеризації поїзних ситуацій. Транспортні системи і технології, 2021, (37), 187-195. https://doi.org/10.32703/2617-9040-2021-37-18
I. S. Durgaryan, A. F. Pashchenko, Y. S. Rodomanova, H. H. Do and T. A. Pham, "Improving the Accuracy of Measuring and Evaluation of Parameters of Large-scale Information Control Systems," 2018 Eleventh International Conference "Management of large-scale system development" (MLSD, 2018, pp. 1-4, doi: 10.1109/MLSD.2018.8551781.
L. S. Zvyagin, "Process of information processing when realizing the concept of ―soft‖ measurements," 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), 2017, pp. 70-73, doi: 10.1109/SCM.2017.7970498.
A. V. Bogomolov, G. A. Sviridyuk, A. V. Keller, V. N. Zinkin and M. D. Alekhin, "Information-logical Modeling of Information Collection and Processing at the Evaluation of the Functional Reliability of the Aviation Ergate Control System Operator," 2018 Third International Conference on Human Factors in Complex Technical Systems and Environments (ERGO)s and Environments (ERGO), 2018, pp. 106-110, doi: 10.1109/ERGO.2018.8443849.
Rothery, C., Strong, M., Koffijberg, H. E., Basu, A., Ghabri, S., Knies, S., ... & Fenwick, E. Value of information analytical methods: report 2 of the ISPOR value of information analysis emerging good practices task force. Value in health, 2020, 23(3), 277-
Ayan, O., Vilgelm, M., Klügel, M., Hirche, S., & Kellerer, W. (2019, April). Age-of-information vs. value-of-information scheduling for cellular networked control systems. In Proceedings of the 10th ACM/IEEE International Conference on Cyber-
Physical Systems, 2019, pp. 109-117.
Viet, N. Q., Behdani, B., & Bloemhof, J. The value of information in supply chain decisions: A review of the literature and research agenda. Computers & Industrial Engineering, 2018 120, 68-82., 9. Kuric, I., Gorobchenko, O., Litikova, O., Gritsuk, I., Mateichyk, V., Bulgakov, M., & Klackova, I. Research of vehicle control informative functioning capacity. Paper presented at the IOP Conference Series: Materials Science and Engineering, 2020 , 776(1) doi:10.1088/1757-899X/776/1/012036.
Бабанін, О. Б., Горобченко, О. М. Визначення цільової функції для оптимізації процесу керування в ергатичній системі Машиніст–СППР–Поїзд на підставі критерію корисності. Збірник наукових праць Державного економіко-
технологічного університету транспорту. Серія: Транспортні системи і технології, 2014, (25), с. 92-98.
King-Sun Fu The Optimal secventional decisions. Lafayette: Purdue Univ. Press, 1967.
Загоруйко, Н.Г. Прикладные методы анализа данных и знаний. Издательство: Институт математики, 1999, 270 с. ISBN:5-86134-060-9.
Kaufman, L., & Rousseeuw, P. J. Finding groups in data: an introduction to cluster analysis, 2009, (Vol. 344). John Wiley & Sons.
Alpaydin, E. Introduction to machine learning. MIT press., 2020.
Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses,. John Wiley & Sons., 2013, Vol. 578
Загоруйко Н.Г. Когнитивный анализ данных. Новосибирск: Академическое издательство ГЕО,. 2013, 186 с
REFERENCES
T. Wen, G. Xie, Y. Cao and B. Cai, "A DNN-Based Channel Model for Network Planning in Train Control Systems," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2021.3093025.
Gorobchenko, O., Nevedrov, O., Nezlina, O., & Tkachenko, V. (2021). Development of a method for clustering train situations. Transport systems and technologies, (37), 187-195. https://doi.org/10.32703/2617-9040-2021-37-18
I. S. Durgaryan, A. F. Pashchenko, Y. S. Rodomanova, H. H. Do and T. A. Pham, (2018) "Improving the Accuracy of Measuring and Evaluation of Parameters of Large-scale Information Control Systems," 2018 Eleventh International Conference "Management of large-scale system development" (MLSD, 2018, pp. 1-4, doi: 10.1109/MLSD.2018.8551781.
L. S. Zvyagin, (2017) "Process of information processing when realizing the concept of ―soft‖ measurements," 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), 70-73, doi: 10.1109/SCM.2017.7970498.
A. V. Bogomolov, G. A. Sviridyuk, A. V. Keller, V. N. Zinkin and M. D. Alekhin. (2018)"Information-logical Modeling of Information Collection and Processing at the Evaluation of the Functional Reliability of the Aviation Ergate Control System Operator," 2018 Third International Conference on Human Factors in Complex Technical Systems and Environments (ERGO)s and Environments (ERGO), 106-110, doi: 10.1109/ERGO.2018.8443849.
Rothery, C., Strong, M., Koffijberg, H. E., Basu, A., Ghabri, S., Knies, S., ... & Fenwick, E. (2020). Value of information
analytical methods: report 2 of the ISPOR value of information analysis emerging good practices task force. Value in health, 23(3), 277-286.
Ayan, O., Vilgelm, M., Klügel, M., Hirche, S., & Kellerer, W. (2019, April). Age-of-information vs. value-of-information scheduling for cellular networked control systems. In Proceedings of the 10th ACM/IEEE International Conference on Cyber-
Physical Systems (pp. 109-117).
Viet, N. Q., Behdani, B., & Bloemhof, J. (2018). The value of information in supply chain decisions: A review of the literature and research agenda. Computers & Industrial Engineering, 120, 68-82., 9. Kuric, I., Gorobchenko, O., Litikova, O., Gritsuk, I., Mateichyk, V., Bulgakov, M., & Klackova, I. (2020). Research of vehicle control informative functioning capacity. Paper presented at the IOP Conference Series: Materials Science and Engineering, , 776(1) doi:10.1088/1757-899X/776/1/012036.
Babanin, O, Gorobchenko, O (2014). Determination of the objective function for optimization of the control process in the ergatic system Machinist – DSS – Train on the basis of the utility criterion. Collection of scientific works of the State Economic and Technological University of Transport. Series: Transport systems and technologies, (25), p. 92-98.
King-Sun Fu (1967) The Optimal secventional decisions. Lafayette: Purdue Univ. Press, 1967.
Zagoruiko, N.G. (1999). Applied methods of data and knowledge analysis. Publisher: Institute of Mathematics. 270 p.
ISBN:5-86134-060-9.
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis (Vol. 344). John Wiley & Sons.
Alpaydin, E. (2020). Introduction to machine learning. MIT press.
Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses (Vol. 578). John Wiley & Sons.
Zagoruiko N.G (2013). Cognitive data analysis. Novosibirsk: Academic publishing house GEO ,. 186 p.
Downloads
Published
How to Cite
Issue
Section
License
Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.