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T-Comm_Article 7_4_2021

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MATHEMATICAL MODELS AND RECOGNITION METHODS
FOR MOBILE SUBSCRIBERS MUTUAL PLACEMENT

Vadim V. Ziyadinov,  Moscow Technical University of Communications and Informatics, Moscow, Russia, willwillson13@gmail.com

Maxim V. Tereshonok,  Moscow Technical University of Communications and Informatics, Moscow, Russia, tereshonok@srd.mtuci.ru

Abstract
The challenge of mobile subscribers’ groups and crowd’s behavior prediction during the mass events is now increasingly important. Operative methods application of this task solution is difficult; accordingly, development and application of technical methods is necessary. The method of this problem solution consists of subscribers’ telephone conversations recording in a zone of mass action, and the following speech recognition, the semantic analysis and statistical processing application. However, there is a tendency demand decrease for mobile systems voice services with simultaneous demand growth for data traffic nowadays. The purpose of this paper is to create a mathematical model of mobile networks subscribers’ mutual placement types, applicable for automatization of the subscribers’ activities nature prediction systems. The research method consists of mathematical simulation model development for pseudo-random examples generation of subscribers’ mutual placement types set, creation of training dataset, convolution neural network training and usage of training results to recognize the new examples. The results obtained. A mathematical model is proposed allowing to create a representative training and validation dataset of mobile networks subscribers’ mutual placement types for neural network training and testing. The convolution neural network trained using these samples has shown high classification accuracy results with a wide class of subscribers’ mutual placement types.

Keywords: neural networks, mobile network subscribers’ mutual placement types, positioning, classification, shape recognition.

References

  1. Tereshonok M.V., Rautkin Yu.V. (2018). Estimation and forecasting of mass events participants activities using the intellectual analysis of mobile communication network traffic parameters. Voprosy Kiberbezopasnosti. 3(27). P. 70-76.
  2. Merkle R.C. (1978). Secure communications over insecure channels. Communications of the ACM, 21(4). P. 294-299.
  3. Diffie W. and Hellman M. (1976). New directions in cryptography. IEEE transactions on Information Theory, 22(6). P. 644-654.
  4. Adjemov, S.S., Klenov, N.V., Tereshonok, M.V. and Chirov, D.S. (2016). A neural-network method for the synthesis of informative features for the classification of signal sources in cognitive radio systems. Moscow University Physics Bulletin, 71(2). P.174-179.
  5. Tereshonok M.V. (2016). Model` ocenki haraktera deiatel`nosti grupp abonentov setei` mobil`noi` sviazi. Tekhnologii informatcionnogo obshchestva – X Mezhdunarodnaia otraslevaia nauchno—tekhnicheskaia konferentciia: sbornik trudov. Moscow. Media P. 156.
  6. Tereshonok M.V., Altukhov E.V. (2017). Situational analysis of mass events with the help of intelligent analysis of service commands of mobile communication networks when subscribers use Internet messengers. Fundamental problems of radioelectronic instrument making. Vol. 17. No. 4. P. 897-900.
  7. Vinogradov A.N., Tereshonok M.V. (2014). Features of approach to contemporary radio monitoring software development. T-Comm. Vol. 8. No. 9. P. 36-38.
  8. Ziiadinov V.V., Tereshonok M.V. (2020). Matematicheskie modeli i metody` raspoznavaniia vzaimnogo raspolozheniia mobil`ny`kh abonentov. Tekhnologii informatcionnogo obshchestva. Sbornik trudov XIV Mezhdunarodnoi` otraslevoi` nauchno-tekhnicheskoi`konferentcii. P. 157-159.
  9. Tereshonok M.V. (2008). Poisk assotciativny`kh pravil pri analize zagruzki setei` sotovoi` sviazi. E`lektrosviaz`. 6. P. 32-33.
  10. Adzhemov S.S., Tereshonok M.V., Chirov D.S. (2009). Optimizatciia algoritmov poiska ustoi`chivy`kh grupp abonentov sistem mobil`noi` radiosviazi. T-Comm. No. S6. P. 14-15.
  11. Zheng W., Huang X. and Li Y. (2017). Understanding the tourist mobility using GPS: Where is the next place? Tourism Management. Vol. 59. P. 267-280.
  12. Roetman J. (2020). Using WiFi Signals in Combination with GPS to Track Human Traffic in a Space (No. 3902). EasyChair.
  13. Renfro B.A., Stein M., Boeker N. and Terry A. (2018). An analysis of global positioning system (GPS) standard positioning service (SPS) performance for 2017. See https://www. gps. gov/systems/gps/performance/2014-GPS-SPS-performance-analysis. pdf.
  14. CS231n Convolutional Neural Networks for Visual Recognition. URL: https://cs231n.github.io/convolutional-networks/; Retrieved Nov 25 2020.
  15. Plokhyh G.I. (2014). Spetsyal’naya podgotovka sotrudnikov organov vnetrennykh del. Ucheb. Posobie. Kursk: Yugo-Zap. Gos. un-t. 350 p.
  16. Land Warfare Doctrine 3-0-3, Land Tactics (Developing Doctrine). 2009. Australian Army. URL: https://www.army.gov.au/sites/default/files/lwd_3-0-3_formation_tactics.pdf?acsf_files_redirect; Retrieved Dec 12, 2020.
  17. Nair V. and Hinton G.E. (2010), January. Rectified linear units improve restricted boltzmann machines. In ICML.
  18. Hinkelmann, K., Neural Networks, p. 7. University of Applied Sciences Northwestern Switzerland. http://didattica. cs. unicam. it/lib/exe/fetch. php.
  19. Hubel D.H. and Wiesel T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of physiology, 160(1), p.106.
  20. Julien Despois. Memorizing is not learning! — 6 tricks to prevent overfitting in machine learning. URL: https://hackernoon.com/memorizing-is-not-learning-6-tricks-to-prevent-overfitting-in-machine-learning-820b091dc42 Retrieved Oct 17 2020.
  21. Klenov N.V., Kuznetsov A.V., Schegolev A.E., Soloviev I.I., Bakurskiy S.V., Kupriyanov M.Y. and Tereshonok M.V. (2019).
    A neuron based on a single flux qubit. Low Temperature Physics, 45(7). P. 769-775.
  22. Soloviev I.I., Schegolev A.E., Klenov N.V., Bakurskiy S.V., Kupriyanov M.Y., Tereshonok M.V., Shadrin A.V., Stolyarov V.S. and Golubov A.A. (2018). Adiabatic superconducting artificial neural network: Basic cells. Journal of applied physics, 124(15). P. 152113.

Information about authors:
Vadim V. Ziyadinov, engineer, Moscow Technical University of Communications and Informatics (MTUCI), Moscow, Russia
Maxim V. Tereshonok, Head of scientific laboratory, Moscow Technical University of Communications and Informatics (MTUCI), Associated Professor, D.Sc., Moscow, Russia