THE METHOD OF DETERMINING THE PROBABILISTIC CHARACTERISTICS OF THE MONITORING SYSTEM TRAFFIC OF THE AFFECTED OBJECTS
DOI:
https://doi.org/10.26906/SUNZ.2022.4.104Keywords:
monitoring, traffic, affected object, distribution density function, majorantAbstract
Due to the incoming traffic of the monitoring system, the condition of the affected objects is significantly uneven. Therefore, there is a need to make a quick short-term forecast of traffic behavior based on a small number of counts. Such a forecast will allow determining the main probabilistic characteristics of traffic. The purpose of the article is to develop a method that allows you to estimate the density function of the traffic distribution of the system for monitoring the condition of affected objects. The assessment is carried out on a given sample. The sample is expanded by the majorant of the distribution function. The following results were obtained. The majorant of the traffic distribution function based on its current counts is constructed. Proposed estimation of traffic distribution density function. A comparative analysis of the obtained estimate of the traffic distribution density function was carried out. Conclusion. The proposed method provides a more accurate and stable estimate than existing methods. It is more efficient when analyzing traffic with long ripples, as well as traffic with long dependencies. Such a trafie is inherent in the data processing center for monitoring the state of affected objects.Downloads
References
Sofina, Natalia; Ehlers, Manfred; Michel, Ulrich. Object-based detection of destroyed buildings based on remotely sensed data and GIS. The International Society for Optical Engineering, 2011, vol. 8181, 87385/ DOI: https://doi.org/10.1117/12.898469
Davydov, Boris, Chebotarev, Vladimir, Kablukova, Kseniya. Evolution of Probabilistic Characteristics in the Train Traffic Process. Lecture Notes in Networks and Systems, 2022, Vol. 352, pp. 101–109. DOI: https://doi.org/10.1007/978-3-030-91156-0_8
Татарнікова, Т. М., Вольський A. В. Оцінка можливо-часових характеристик мережних вузлів з диференціацією трафіка. Інформаційно-керуючі системи, 2018, № 3(94), pp. 54–60.
Кучук Г. А. Метод оценки характеристик АТМ-трафика. Інформаційно-керуючі системи на залізничному транспорті. 2003. № 6 (44). С. 25-29.
Vapnik V. Estimation of Dependences based on Empirical Data. N.-Y., Berlin : Springer – Verlag, 1987. 326 p.
Silverman B.W. Choosing the window wiolth when estimating a density. Biotechnika. 1988. V. 65, № 1. P. 1–11.
Xing, Viktoria. Dynamical Borel-Cantelli lemmas. Discrete and Continuous Dynamical Systems, 2022, Vol. 41, Is. 4, pp. 1737– 1754. DOI: https://doi.org/10.3934/dcds.2020339
. Svyrydov, A., Kuchuk, H., Tsiapa, O. (2018), “Improving efficienty of image recognition process: Approach and case study”, Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, pp. 593-597, DOI: http://dx.doi.org/10.1109/DESSERT.2018.8409201
Eramili A., Narayan, O., Willinger W. Experimental queuing analyzes with long-range dependent packet traffic. IEEE/ACM Transactions on Networking. 1996. V.4. P. 209–223.
Kovalenko, А., Kuchuk H. Methods for synthesis of informational and technical structures of critical application object’s control system, Advanced Information Systems, 2018, Vol. 2, No. 1, pp. 22–27. DOI: https://doi.org/10.20998/2522-9052.2018.1.04
Moraes, Caroline P.A., Fantinato, Denis G., Neves, Aline. Epanechnikov kernel for PDF estimation applied to equalization and blind source separation. Signal Processing, 2021, Vol. 189, 108251. DOI: https://doi.org/10.1016/j.sigpro.2021.108251
