Method for detecting anomalies in an interactive mode in observations scalar fields gradients

Y.E. Shishkin1,2, A.V. Skatkov1

1 Federal State Educational Institution of Higher Education «Sevastopol State University»,

Russian Federation, Sevastopol, Universitetskaya St., 33

2 Institute of Natural and Technical Systems, Russian Federation, Sevastopol, Lenin St., 28


DOI: 10.33075/2220-5861-2018-2-30-37

UDC 004.9:004.78


   An approach to solve the problem of operative anomalies recognition in scalar fields data for monitoring objects and processes using the gradient method and the system of specific information metrics is proposed. Metrics of module and direction standard deviation of the gradient fields difference are used. A binary classifier system method and a mathematical model and a recognition procedure that works in an interactive mode are constructed. The process of proposed approach operation is illustrated using the example of water temperature vertical spatial sounding data sample of Kruglaya Bay area of the city of Sevastopol.

Keywords: monitoring, mathematical modeling, Big Data, digital filtering, gradient, scalar field, detection of anomalies, clustering, critical systems, data mining.

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  1. Unsupervised real-time anomaly detection for streaming data / S. Ahmad, A. Lavin, S. Purdy [et al.] // Neurocomputing. 2017. Vol. 262. P 134–147. DOI: j.neucom.2017.04.070.
  2. Derkahov S.E., Manashov A.N. Anomalous dimensions of composite operators in scalar field theories // Journal of Mathematical Sciences. 2010. Vol. 168: P. 837–855. DOI: 10.1007/s10958-010-0032-9.
  3. Fursaev D.V., Miele G. Finite-temperature scalar field theory in static de Sitter space // Phys. Rev. D. 1993. Vol. 49. P. 987-998. DOI: 10.1103/PhysRevD.49.987.
  4. A.V. Skatkov, Y.E. Shishkin Data clustering in anomalies detection tasks based on orthogonal filters // Monitoring systems of environment. 2018. № 11 (31). P. 36–43.
  5. A.V. Skatkov, Y.E. Shishkin Anomaly identification model in the observation field using parametric monitoring systems // Monitoring systems of environment. 2017. № 10 (30). P. 48–53.
  6. Pääkkönen P., Pakkala D. Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems  // Big Data Research. 2015. Vol. 2. P. 166–186. DOI: 10.1016/j.bdr.2015.01.001.
  7. Skatkov A.V., Shishkin Yu. E. Monitoring of qualitative changes in network traffic States in cloud computing environments // Automation and instrument engineering: problems, solutions: materials of the international conference. scientific-technical Conf. / under the scientific ed. of V. ya. Kopp. Sevastopol, September 11-15, 2017 Sevastopol: Sevgu, 2017. P. 137-138.
  8. Shishkin Y.E. Big Data visualization in decision making // Science in Progress: тез. Всерос. науч.-практ. конф. магистрантов и аспирантов. Новосибирск, 20 октября 2016 г. Новосибирск: НГТУ, 2016. C. 203–205. ISBN 978-5-7782-3094-1.
  9. Skatkov A.V., Shishkin Yu. E. Development of a model for monitoring optimization under partial uncertainty in the form of a Queuing system // Development of the methodology of modern economic science and management: materials of the first Interdisciplinary all-Russian scientific and practical conference. Sevastopol, may 4-5, 2017 Sevastopol: Sevgu, 2017. P. 611-618.
  10. Bâki H. Is the global sea surface temperature rise accelerating? // Geodesy and Geodynamics. 2018. Vol. 2. P. 109–116. DOI: 10.1016/j.geog.2018.04.002.
  11. Burmistrova N. V., Zhuk V. F., Melnikova E. B. Thermohaline water structure on the beam of Kruglaya Bay and its influence on the intensity of the bioluminescence field // Nature almanac. 2011. Issue 15. P. 14-25.
  12. Skatkov A.V., Shishkin Yu. E. Model of monitoring system in risk conditions // Environmental, industrial and energy security-2017: collection of articles based on the materials of the scientific and practical conference with international participation / ed. by Yu. a. Omelchuk, N. V. Lyamina, G. V. Kucherik. 2017. P. 1239-1242.
  13. Spectral analysis of the earth’s anomalous magnetic field for multi-altitude surveys / V. I. Odintsov, N. M. Romanova, Yu. P. Tsvetkov [et al.] / / Geomagnetism and Aeronomy. 2000. Vol. 40. No. 2. P. 59-66.
  14. Dorit S. Complexity and approximations for submodular minimization problems on two variables per inequality constraints // Discrete Applied Mathematics. 2018. Vol. 1. P. 12–20 DOI: 10.1016/j.dam.2018.04.012.
  15. Bryukhovetsky A. A., Skatkov A.V., Shishkin Yu. E. Modeling of anomaly detection processes in complex structured monitoring data / / Monitoring systems of environment. 2017. No. 9 (29). P. 45-49.
  16. A lock-free approach to parallelizing stochastic gradient descent / B. Recht, W. Re, S. Wright [et al.] //Advances in Neural Information Processing Systems – 24. Curran Associates, Inc. 2011. P. 693–701.