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Complex algorithm for detecting anomalous features in natural data
A.R. Liss 1, B.S. Mandrikova 2, O.V. Mandrikova 2

1 Saint Petersburg Electrotechnical University,
197376, Russia, St. Petersburg, st. Professora Popova, 5;
2 Institute of Cosmophysical Research and Radio Wave Propagation, Far Eastern Branch of the Russian Academy of Sciences,
684034, Kamchatskiy Kray, Paratunka, Russia, Mirnaya st. 7

 PDF, 1389 kB

DOI: 10.18287/2412-6179-CO-1652

Pages: 1030-1036.

Full text of article: Russian language.

Abstract:
A complex automated algorithm for analyzing natural data and detecting anomalous features is proposed. The algorithm includes an algorithm for determining the information components of a signal and an algorithm for adaptive wavelet filtering of a signal. The algorithm for determining the information components of a signal suppresses correlated noise and determines the information components of a signal. The algorithm for adaptive wavelet filtering of a signal detects anomalous features and estimates their intensity. The algorithms are based on the rules developed by the authors. Based on the rules, the parameters of the threshold function are estimated and the best approximating wavelet is determined. The article describes the operations of the complex algorithm and presents a block diagram of its implementation. Also presented are the results of applying a complex algorithm using data from secondary cosmic rays and model data constructed in their likeness. The results confirmed the effectiveness of the developed rules and the proposed complex algorithm.

Keywords:
wavelet transform, risk theory, natural anomalies, cosmic rays.

Citation:
Liss AR, Mandrikova BS, Mandrikova OV. Complex algorithm for detecting anomalous features in natural data. Computer Optics 2025; 49(6): 1030-1036. DOI: 10.18287/2412-6179-CO-1652.

Acknowledgements:
The work was supported by IKIR FEB RAS State Task (subject registration No. 124012300245-2).

References:
  1. Murzagulov DA, Zamyatin AV. The process signal anomaly detection using classifier ensemble and wavelet transforms [In Russian]. Automated Control Systems 2021; 1(63): 20-26. DOI: 10.35752/1991-2927-2021-1-63-20-26.
  2. Murzagulov DA, Zamyatin AV, Romanovich OV. Approach to the detection of anomalies in process signals by using the Hilbert–Huang transform. Optoelectron Instrum Data Process 2021; 57(1); 27-36. DOI: 10.3103/S8756699021010076.
  3. Davydov NS, Evdokimova VV, Serafimovich PG, Protsenko VI, Khramov AG, Nikonorov AV. Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation. Computer Optics 2023; 47(6): 991-1001. DOI: 10.18287/2412-6179-CO-1337.
  4. Borog VV, Kryanev AV, Udumyan DK. Combined method for detecting hidden anomalies in galactic cosmic ray variations. Geomagn Aeron 2011; 51(4); 475-482. DOI: 10.1134/S0016793211040086.
  5. Vorobev AV, Vorobeva GR. Geoinformation system for amplitude-frequency analysis of geomagnetic variations and space weather observation data. Computer Optics 2017; 41(6): 963-972. DOI: 10.18287/2412-6179-2017-41-6-963-972.
  6. Mandrikova OV. Intelligent methods for natural data analysis: application to space weather. Computer Optics 2024; 48(1): 139-148. DOI: 10.18287/2412-6179-CO-1367.
  7. Kuznetsov VD. Space weather and risks of space activity [In Russian]. Space Engineering and Technology 2014; 3(6): 3-13.
  8. Mandrikova O, Mandrikova B. Hybrid model of natural time series with neural network component and adaptive nonlinear scheme: Application for anomaly detection. Mathematics 2024; 12(7): 1079. DOI: 10.3390/math12071079.
  9. Mallat SG. A wavelet tour of signal processing. San Diego, CA: Academic Press; 1999.
  10. Levin BR. Theoretical foundations of statistical radio engineering [In Russian]. Moscow: "Radio i Svyaz" Publisher; 1989.
  11. Polyakova VV, Shabrova NV. Fundamentals of the theory of statistics [In Russian]. Ekaterinburg: Publishing House of the Ural University; 2015.
  12. Daubechies I. Ten lectures on wavelets. Philadelphia, Pennsylvania: Society for Industrial and Applied Mathematics; 1992. ISBN: 978-0-898712-74-2.
  13. Chui CK. An introduction in wavelets. Vol 1. New York, USA: Academic Press; 1992. ISBN: 978-0121745844.
  14. Mandrikova OV, Geppener VV, Mandrikova BS. Method of cosmic ray data analysis based on vector quantization neural networks. J Phys: Conf Ser 2019; 1368(5): 052026. DOI: 10.1088/1742-6596/1368/5/052026.
  15. Neutron Monitor Network Database. 2024. Source: <www.nmdb.eu>.
  16. Samsonov SN, Manykina VI, Parshina SS. Space weather influence on cardiovascular system of healthy people and people with weakened adaptive capability [In Russian]. Psychosomatic and Integrative Research 2016; 2(1): 0102.
  17. Dorman LI. Cosmic ray variations and space weather. Phys–Usp 2010; 53(5): 496-503. DOI: 10.3367/UFNe.0180.201005g.0519.
  18. Firoz KA, Gan WQ, Li YP, Rodríguez-Pacheco J, Kudela K. On the possible mechanism of GLE initiation. Astrophys J 2019; 872(178): 178. DOI: 10.3847/1538-4357/ab0381.
  19. Space Weather Forecasting Center IZMIRAN. 2024. Source: <http://spaceweather.izmiran.ru/rus/>.
  20. Java and Kotlin IDE. 2024. Source: <https://www.jetbrains.com/idea/>.

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