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DOI: https://doi.org/10.15407/techned2017.04.003

COMPARISON OF THE SMOOTHING EFFICIENCY OF SIGNALS OF VOLTAGE ON THE PLASMA-EROSIVE LOAD AND ITS CURRENT BY MULTI-ITERATIVE FILTRATION METHODS

Journal Tekhnichna elektrodynamika
Publisher Institute of Electrodynamics National Academy of Science of Ukraine
ISSN 1607-7970 (print), 2218-1903 (online)
Issue No 4, 2017 (July/August)
Pages 3 – 13

 

Authors
N.A. Shydlovska, S.M. Zakharchenko, O.P. Cherkaskyi
Institute of Electrodynamics National Academy of Sciences of Ukraine,
pr. Peremohy, 56, Kyiv, 03057, Ukraine,
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Abstract

Characterized signals of voltage on the multichannel plasma-erosive load and its current, as a result of capacitor discharge on it in general. The influence of parameters of elements of the discharge circuit and the initial conditions for them in the transition process in plasma-erosive load and a factor of stochastic amplitude modulation of the discharge current and voltage at the load are considered. A critical analysis of the signal filtering methods is given. Described filtering of signals voltage on plasma-erosive load and its current by method of their partially recovery by their incomplete mode decomposition. The algorithm of new multi-iterative method of moving average with increasing width of the filtering window of non-stationary non-periodic signals is described. The comparative analysis of efficiency of filtration of signals of voltage on plasma-erosive load and its current by a new method and a method of their partially recovery by their incomplete mode decomposition is given. References 22, figures 5, tables 2.

 

Key words: plasma-erosive load, discharge current, non-stationary non-periodic signals, methods of signal filtering.

 

Received:    07.04.2017
Published:   15.06.2017

 

References

1. Bodina N.N., Kalmychkov A.S., Kriventsov V.E. Comparative analysis of filtering algorithms of medical images. Vesnyk Natsionalnoho Tekhnicheskoho Universytetu "Kharkivskyi Politekhnichnyi Instytut". 2012. No 38. Pp. 14–25. (Rus)
2. Greshilov A.A., Stakun V.A., Stakun A.A. Mathematical methods of construction of prognoses. Moskva: Radio i Sviaz, 1997. 112 p. (Rus)
3. Davydov A.V. Signals and Systems. Lectures and practical work on the PC. Introduction to Signals and Systems Theory. Available at: http://www.geoin.org/signals/index.html. (Accessed 06.02.2017). (Rus)
4. Zagretdinov A.R., Busarov A.V., Busarov V.V. Comparison of methods for stopping sifting in the empirical mode decomposition of signals. Inzhenerniy vestnik Dona. 2015. No 3. Available at: http://www.ivdon.ru/ru/magazine/archive/n3y2015/3238. (Accessed 06.02.2017). (Rus)
5. Zyuko A.G., Klovskiy D.D., Korzhik V.I., Nazarov M.V. The theory of electric communication. Moskva: Radio i Sviaz, 1999. 32 p. (Rus)
6. Ivanov M.T., Sergiyenko A.B., Ushakov V.N. Radio engineering circuits and signals. S–Pb.: Piter, 2014. 336 p. (Rus)
7. Kramer G. Mathematical methods of statistics. Moskva: Mir, 1975. 648 p. (Rus)
8. Maksimchuk I.V., Gergel L.G., Osadchiy O.V. The Comparative Analysis Fourier and Wavelets Transformations for the Signal Analysis of Photoplethysmogram. Sovremennye nauchnye issledovaniya i innovatsii. 2013. No 6. Available at: http://www.web.snauka.ru/issues/2013/06/25060. (Accessed 06.02.2017). (Rus)
9. Matveyev Yu.N., Simonchik K.K., Tropchenko A.Yu., Khitrov M.V. Digital processing of signals. S–Pb: S–PbNIU ITMO, 2013. 166 p. (Rus)
10. Muratov V.A. Semiconductor converters for a supply of technological devices electroerosive dispersion. Diss. … cand. tech. sci.: 05.09.12. Кiev, 1986. 279 p. (Rus)
11. Physical Foundations of Electrical Engineering. Мoskva-Leningrad: Gosudarstvennoe energeticheskoe izdatelstvo, 1950. 556 p. (Rus)
12. Huang T.S., Eklund Dzh.-O., Nussbaumer G.Dzh., Zokhar Sh., Iustusson B.I., Tian Sh.-G. Fast Algorithms in Digital Image Processing. Transforms and Median Filters. Moskva: Radio i Sviaz, 1984. 224 p. (Rus)
13. Shydlovskaya N.A., Zakharchenko S.N., Cherkassky A.P. Non-linear-parametrical Model of Electrical Resistance of Conductive Granulated Media for a Wide Range of Applied Voltage. Tekhnichna Elektrodynamika. 2014. No 6. Pp. 3–17. (Rus)
14. Shydlovska N.A., Zakharchenko S.M., Cherkaskyi O.P. Parametric Model of Resistance of Plasma-erosive Load, Adequate in the Wide Range of Change of Applied Voltage. Tekhnichna Elektrodynamika. 2017. No 3. Pp. 3–12. (Ukr)
15. Shydlovska N.A., Zakharchenko S.M., Cherkaskyi O.P. Physical Prerequisites of Construction of Mathematical Models of Electric Resistance of Plasma-erosive Loads. Tekhnichna Elektrodynamika. 2017. No 2. Pp. 5–12. (Ukr)
16. Hoaglin D.C., Mosteller F., Tukey J.W. Understanding Robust and Exploratory Data Analysis. New York: John Wiley & Sons, 2000. 472 p.
17. Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen N.-Ch., Tung C.C, Liu H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. London A, Math. Phys. Sci. 1998. Vol. 454. Issue 1971. Pр. 903–995.
18. Kabir M.A., Shahnaz C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomedical Signal Processing and Control. 2012. No 7. Pp. 481–489.
19. Li L., Chai X., Zheng Sh., Zhu W. A De-Noising Method for Track State Detection Signal Based on EMD. Journal of Signal and Information Processing. 2014. No 5. Pp.104–111.
20. Meyer Y. Wavelets and operators. Cambridge: Cambridge Univ. Press, 1992. 223 p.
21. Robert G.B., Patrick Y.C. Hwang Introduction to Random Signals and Applied Kalman Filtering. New York: John Wiley & Sons, 2012. 400 p.
22. Tukey J.W. The Future of Data Analysis. The Annals of Mathematical Statistics. 1962. Vol. 33. No 1. Pр. 1–67.

 

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