SIGNAL SMOOTHING OPTIMIZATION

UDC 681.53

  • Hryniuk Dzmitry Anatol’yevich – PhD (Engineering), Associate Professor, Assistant Professor, the Department of Automation of Production Processes and Electrical Engineering. Belarusian State Technological University (13a, Sverdlova str., 220006, Minsk, Republic of Belarus). E-mail: hryniuk@tut.by

  • Oliferovich Nadezhda Mikhaylovna – Assistant Lecturer, the Department of Automation of Production Processes and Electrical Engineering. Belarusian State Technological University (13a, Sverdlova str., 220006, Minsk, Republic of Belarus). E-mail: oliferovich@belstu.by

  • Suhorukova Irina Gennad’yevna – Senior Lecturer, the Department of Software Engineering. Belarusian State Technological University (13a, Sverdlova str., 220006, Minsk, Republic of Belarus). E-mail: irina_x@rambler.ru

DOI: https://doi.org/10.52065/2520-6141-2021-248-2-72-79

Key words: measuring trend, time series, smoothing.

For citation: Hryniuk D.A., Oliferovich N. M., Suhorukova I. G. Signal smoothing optimization. Proceedings of BSTU, issue 3, Physics and Mathematics. Informatics, 2021, no. 2 (248), pp. 72–79 (In Russian). DOI: https://doi.org/10.52065/2520-6141-2021-248-2-72-79.

Abstract

The article deals with the issues of data processing from primary measuring converters. Increasing the sensitivity of modern converters is associated with a high level of interference. However, in many cases it is required to preserve the useful signal with minimal distortion after processing.

As an object, the signal from the sensor of geometric displacements when measuring nonlinear deformation is considered. For the problem, a variant of piecewise approximation by a quadratic function was chosen. The optimization of the smoothing algorithm was carried out by examining the model signal, which is similar to the measured one. To increase the signal-to-noise ratio, it is proposed to use double anti-aliasing. A mathematical experiment was carried out to optimize the proposed scheme. The main goal was to determine the optimal value of the approximation window for primary and secondary smoothing and to compare with a single use of this algorithm. For this, a signal was generated with the presence of five harmonics of an informative signal with additive noise and high-frequency parasitic harmonics. The main criterion was the conservation of the power of the harmonics of the information signal. The results showed that the selected smoothing method is sensitive to the optimal window value. Despite the theoretical prerequisites for the absence of the effect of multiple smoothing on the result, the experiment showed the effectiveness of this approach. The use of multiple anti-aliasing allows to reduce the load on computational resources during processing while improving the quality for this method.

The obtained results of smoothing on a mathematical model made it possible to optimize the process of processing measurement information in a physical experiment.

Download

References

  1. Bogoslav N. M., Hryniuk D. A., Orobei I. O. Experimental research of the dynamics of circular impregnation. Trudy BGTU [Proceedings of BSTU], 2013, no. 6: Physics and Mathematics. Informatics, pp. 99–103 (In Russian).
  2. Suhorukova I. G., Hryniuk D. A., Orobei I. O. Increased sensitivity stand leaks stop valves. Trudy BGTU [Proceedings of BSTU], 2015, no. 6: Physics and Mathematics. Informatics, pp. 132–136 (In Russian).
  3. Oliferovich N., Hryniuk D., Orobei I. Measuring the speed of capillary soaking with adaptation regarding coordinates. 2015 Open Conference of Electrical, Electronic and Information Sciences (eStream 2015), Vilnius, Lithuania, 21 April 2015. Vilnius, 2015, pр. 1–4.
  4. Hryniuk D. A., Suhorukova I. G., Oliferovich N. M. Using Approximation Algorithms to Smooth Transmitter Trends. Trudy BGTU [Proceedings of BSTU], 2017, no. 2: Physics and Mathematics. Informatics, pp. 82–87 (In Russian).
  5. Felinger A. Data analysis and signal processing in chromatography. Data Handling in Science and Technology. 1998, Elsevier, vol. 21. 413 p.
  6. Grushka E. Characterization of Exponentially Modified Gaussian Peaks in Chromatography. Anal. Chem., 1972, vol. 44, no. 11, pp. 1733–1738. DOI: 10.1021/ac60319a011.
  7. Kalambet Y. A., Kozmin Y. P., Mikhailova K. V., Nagaev I. Y., Tikhonov P. N. Reconstruction of chromatographic peaks using the exponentially modified Gaussian function. J. Chemom, 2011, vol. 25, no. 7, pp. 352–356. DOI: 10.1002/cem.1343.
  8. Kalambet Yu. A. Optimization of parameters of linear smoothing of chromatographic peaks. Nauchnoye priborostroyeniye [Scientific instrumentation], 2019, vol. 29, no 3 pp. 51–60.
  9. Hangos K. M., Cameron I. T. Process modelling and model analysis. San Diego, Academic Press Publ., 2001. 543 p.
  10. Mathematical Statistics. Samuel S. Wilks. Wiley, New York, 1962. XVI. 644 p.
  11. Mallat S. A wavelet tour of signal processing. San Diego, Academic Press Publ., 1999. 620 с.
  12. Savitzky A., Golay M .J. E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem., 1964, vol. 36, no. 8, pp. 1627–1639. DOI: 10.1021/ac60214a047.
  13. Delley R. Series for the exponentially modified Gaussian peak shape. Anal. Chem., 1985, vol. 57, no. 1, pp. 388–388. DOI: 10.1021/ac00279a094.
  14. Wentzel E. S. Teoriya veroyatnostey [Probability theory]. Мoscow, Vysshaya Shkola Publ., 1999, 576 с.
  15. Kaniewski P., Gil R., Konatowski S. Estimation of UAV position with use of smoothing algorithms. Metrol. Meas. Syst., 2017, vol. 24, no. 1, pp. 127–142.
  16. Kalman R. E. A new approach to linear filtering and prediction problems. J. Basic Eng., 1960, vol. 82, no. 1, pp. 35–45. DOI: 10.1115/1.3662552.
  17. Hryniuk D. Suhorukova I. Oliferovich N. Adaptive smoothing and filtering in transducers. 2016 Open Conference of Electrical, Electronic and Information Sciences (eStream 2016), Vilnius, Lithuania, 21 April 2016. Vilnius, 2016, pp. 1–4.
  18. Katkovnik V. Ya. Neparametricheskaya identifikatsiya i sglazhivaniye dannykh: metod lokal’noy approksimatsii [Nonparametric Identification and Data Smoothing: Method of Local Approximation]. Moscow, Nauka Publ., 1985. 336 p.
15.06.2021