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The Stochastic Recurrence Structure of Geophysical Phenomena

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Cyclostationarity: Theory and Methods - II (CSTA 2014)

Part of the book series: Applied Condition Monitoring ((ACM,volume 3))

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Abstract

The results of using methods of theory and statistics of periodically correlated random processes (PCRP) for probability structure of annual and daily variations of geophysical phenomena investigation are presented. Properties of estimators for mean function, covariance function, spectral density and their Fourier coefficients, calculated for series of natural phenomena on the basis of observation data, are described. The approach to building the annual and daily rhythmic parametric model, based on PCRP harmonic representation, is proposed. The problem of estimation accuracy of the obtained processing results is considered.

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References

  1. Yavorskyj, I., Yuzefovych, R., Kravets, I., & Matsko, I. (2014). Methods of periodically correlated random processes and their generalizations. In F. Chaari, J. Leskow, & A. Sanches-Ramirez (Eds.), Cyclostationarity: Theory and methods. Lecture Notes in Mechanical Engineering. (pp. 73–93), New York: Springer.

    Google Scholar 

  2. Hurd, H. L., & Miamee, A. (2007). Periodically correlated random sequences. Spectral theory and practice (p. 353). New Jersey: Wiley-Interscience.

    Book  MATH  Google Scholar 

  3. Dragan, Ya., Rozhkov, V., & Javorskyj, I. (1987). Methods of probabilistic analysis of oceanological processes rhythmics. Leningrad: Gidrometeoizdat (in Russian).

    Google Scholar 

  4. Gardner, W. A. (1987). Statistical spectral analysis: A nonprobabilistic theory (p. 566). New York: Prentice Hall.

    Google Scholar 

  5. Gardner, W. A. (1985). Introduction to random processes with application to signals and systems (p. 434). New York: Macmillan.

    Google Scholar 

  6. Gardner, W. A. (Ed.). (1994). Cyclostationarity in communications and signal processing (p. 504). New York: IEEE Press.

    MATH  Google Scholar 

  7. Gardner, W. A., Napolitano A., Paural L. (2006). Cyclostationarity: Half century of research. Signal processing. (Vol. 86, pp. 639–697).

    Google Scholar 

  8. Napolitano, A. (2012). Generations of cyclostationarity signal processing: Spectral analysis and applications. Wiley: IEEE Press.

    Book  Google Scholar 

  9. Javorskyj, I., Isayev, I., Zakrzewski, Z., & Brooks S. P. (2007). Coherent covariance analysis of periodically correlated random processes. Signal processing. (Vol. 87, pp. 13–32).

    Google Scholar 

  10. Javorskyj, I., Isayev, I., Majewski, J., & Yuzefovych R. (2010). Component covariance analysis for periodically correlated random processes. Signal processing. (Vol. 90, pp. 1083–1102).

    Google Scholar 

  11. Javorskyj, I., Leskow, J., Kravets, I., Isayev, I., & Gajecka, E. (2012). Linear filtration methods for statistical analysis of periodically correlated random processes—Part I: Coherent and component methods and their generalization. Signal processing. (Vol. 92, pp. 1559–1566).

    Google Scholar 

  12. Javorskyj, I., Leskow, J., Kravets, I., Isayev, I., & Gajecka, E. (2011). Linear filtration methods for statistical analysis of periodically correlated random processes—Part II: Harmonic series representation. Signal processing. (Vol. 91, pp. 2506–2519).

    Google Scholar 

  13. Javorskyy, I., Yuzefovych, R., Krawets, I., & Zakrzewski, Z. (2011). Least squares method in the statistic analysis of periodically correlated random processes. Radioelectronics and communications systems. (Vol. 54(1), pp. 45–59).

    Google Scholar 

  14. Rozhkov, V. A. (1974). Probabilistic analysis methods for oceanic processes (in Russian). Leningrad: Gidrometeoizdat.

    Google Scholar 

  15. Mykhailyshyn, V. Y., & Javorskyj, I. N. (1994). Probabilistic structure of air temperature seasonal variety. Meteorologia i gidrologia. No. 2, pp. 20–35 (in Russian).

    Google Scholar 

  16. Dragan, Y. P., Rozhkov, V. A., & Javorskyj, I. N. (1984). CRP methods usage for probabilistic analysis of oceanic time series (in Russian) (pp. 4–23). Leningrad: Gidrometeoizdat.

    Google Scholar 

  17. Rozhkov, V. A., Cherneshov, S. Y., Trapeznikov, Y. A., & Javorskyj, I. N. (1984). Comparison of estimation methods for probabilistic characteristics of oceanic processes seasonal variety (in Russian). Mode forming factors, information bases and methods of their analysis (pp. 138–149). Leningrad: Gidrometeoizdat.

    Google Scholar 

  18. Myakisheva, N. V., Rozhkov, V. A., Ulyanich, I. G., & Javorskyj, I. N. (1986). Probabilistic characteristics estimators for seasonal and daily varieties hydro-meteorological processes (in Russian). Mode forming factors, information bases and methods of their analysis (pp. 140–152). Leningrad: Gidrometeoizdat.

    Google Scholar 

  19. Mezentsev, V. P., & Javorskyj, I. N. (1989). Probabilistic methods for analysis of daily and seasonal varieties radiophysical processes. All-USSR scientific and technical conference “Problems of radiophysical complexes and flight service enhancement”. Kyiv. pp. 71–72 (in Russian).

    Google Scholar 

  20. Javorskyy, I. (1984). The application of Buys-Ballot scheme for statistical analysis of rhythmic signals. Izvestiya Vysshikh Uchebnykh Zavedeniĭ Radioelectronika, 27(11), 31–37. (in Russian).

    Google Scholar 

  21. Javorskyj, I., & Mykhajlyshyn, V. (1996). Probabilistic models and investigation of hidden periodicities. Applied Mathematics Letters, 9(2), 21–23.

    Article  Google Scholar 

  22. Mezentsev, V. P., & Javorskyj, I. N. (1987). Methods for statistical analysis of natural radiophysical processes daily rhythmic (in Russian). All-USSR scientific and technical conference “Registration and analysis of very low-frequency oscillations with natural derivation”. Voronezh. pp. 50–51.

    Google Scholar 

  23. Javorskyy, I. (1985). On statistical analysis of periodically correlated random processes. Radiotekh. Electron. (Vol. 6, pp. 1096–1104).

    Google Scholar 

  24. Yavorskyj, I., Dehay, D., & Kravets, I. (2014). Component statistical analysis of second order hidden periodicities. Digital signal processing. (Vol. 26, pp. 50–70).

    Google Scholar 

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Authors and Affiliations

  1. Karpenko Physico-Mechanical Institute of National Academy of Sciences of Ukraine, Naukova Str. 5, L’viv, 79060, Ukraine

    I. Javors’kyj, R. Yuzefovych, I. Matsko & I. Kravets

  2. Institute of Telecommunication, University of Technology and Life Sciences, Al. Prof. S. Kaliskiego 7, 85789, Bydgoszcz, Poland

    I. Javors’kyj

Authors
  1. I. Javors’kyj
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  2. R. Yuzefovych
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  3. I. Matsko
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  4. I. Kravets
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Corresponding author

Correspondence to I. Matsko .

Editor information

Editors and Affiliations

  1. National School of Engineers of Sfa, Sfax, Tunisia

    Fakher Chaari

  2. Institute of Mathematics, Cracow University of Technology, Krakow, Poland

    Jacek Leskow

  3. Department of Engineering, University of Napoli “Parthenope”, Napoli, Italy

    Antonio Napolitano

  4. Institute of Mining Engineering, Wroclaw University of Technology, Wroclaw, Poland

    Radoslaw Zimroz

  5. Institute of Mathematics and Computer Sc, Wroclaw University of Technology, Wroclaw, Poland

    Agnieszka Wylomanska

  6. Department of Applied Mathematics, University of Science and Technology, Krakow, Poland

    Anna Dudek

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Javors’kyj, I., Yuzefovych, R., Matsko, I., Kravets, I. (2015). The Stochastic Recurrence Structure of Geophysical Phenomena. In: Chaari, F., Leskow, J., Napolitano, A., Zimroz, R., Wylomanska, A., Dudek, A. (eds) Cyclostationarity: Theory and Methods - II. CSTA 2014. Applied Condition Monitoring, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-16330-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-16330-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16329-1

  • Online ISBN: 978-3-319-16330-7

  • eBook Packages: EngineeringEngineering (R0)

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