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Standard deviation in the simulation of statistical measurements

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Is this article simulation of statistical measurements is performed on the basis of which the analysis of the standard deviation of the obtained results is carried out. It is shown that the standard deviation is minimum and independent from measurement duration while an object is in the state of equilibrium. For objects in a stationary non-equilibrium state the standard deviation depends on the duration measurements and the parameters of the state. The influence of these factors on the standard deviation is assessed with equation which includes the relaxation time. The value of the relaxation time is determined by approximating the energy spectrum of the studied signals. The analysis of energy spectra showed that the spectrum of white noise is inherent in objects in equilibrium; the flicker component of the spectrum occurs when the state of the object deviates from equilibrium.
Rocznik
Strony
17--30
Opis fizyczny
Bibliogr. 23 poz., rys., tab., wykr., wzory
Twórcy
  • Department Automation, Lublin University of Technology, ul. Nadbystrzycka 36, 20-618 Lublin, Poland
  • Lviv Polytechnic National University, Institute of Computer Technologies, Automatics and Metrology, S. Bandera Str. 28a, 79013, Lviv, Ukraine
  • Lviv Polytechnic National University, Institute of Computer Technologies, Automatics and Metrology, S. Bandera Str. 28a, 79013, Lviv, Ukraine
  • Department of Automation and Metrology, Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
autor
  • Lviv Polytechnic National University, Institute of Computer Technologies, Automatics and Metrology, S. Bandera Str. 28a, 79013, Lviv, Ukraine
  • Lviv Polytechnic National University, Institute of Computer Technologies, Automatics and Metrology, S. Bandera Str. 28a, 79013, Lviv, Ukraine
  • Lviv Polytechnic National University, Institute of Computer Technologies, Automatics and Metrology, S. Bandera Str. 28a, 79013, Lviv, Ukraine
autor
  • Lviv Polytechnic National University, Institute of Computer Technologies, Automatics and Metrology, S. Bandera Str. 28a, 79013, Lviv, Ukraine
Bibliografia
  • [1] DeCoursey, W. (2003). Statistics and Probability for Engineering Applications. Elsevier.
  • [2] Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control (3rd ed.). Prentice-Hall.
  • [3] Jun, S., & Kochan, O. (2015). Common mode noise rejection in measuring channels. Instruments and Experimental Techniques, 58(1), 86-89. https://doi.org/10.1134/S0020441215010091
  • [4] Jebb, A. T., Tay, L., Wang, W., & Huang, Q. (2015). Time series analysis for psychological research: examining and forecasting change. Frontiers in Psychology, 6, 727. https://doi.org/10.3389/fpsyg.2015.00727
  • [5] Xu, H., Przystupa, K., Fang, C., Marciniak, A., Kochan, O., & Beshley, M. (2020). A combination strategy of feature selection based on an integrated optimization algorithm and weighted k-nearest neighbor to improve the performance of network intrusion detection. Electronics, 9(8), 1206. https://doi.org/10.3390/electronics9081206
  • [6] Kirkup, L., & Frenkel, R. B. (2006). An introduction to uncertainty in measurement: using the GUM (Guide to the Expression of Uncertainty in Measurement). Cambridge University Press.
  • [7] Gorban’, I. I. I. (2018). Estimation of statistically unpredictable changes in physical quantities over large observation intervals. Technical Physics, 63, 1722-1729. https://doi.org/10.1134/S106378421812006X
  • [8] Chichigina, O., & Valenty, D. (2021). Strongly super-Poisson statistics replaced by a wide-pulse Poisson process: the billiard random generator. Chaos, Solitons and Fractals, 153(1), 111451. https://doi.org/10.1016/j.chaos.2021.111451
  • [9] Kolodiy, Z., Stadnyk, B., & Yatsyshyn, S. (2013). Development of Noise Measurements. Part 2. Random Error. Sensors & Transducers, 151(4), 107-112.
  • [10] Curran-Everett, D. (2008). Explorations in statistics: standard deviations and standard errors. Advances in Physiology Education, 32, 203-208. https://doi.org/10.1152/advan.90123.2008
  • [11] Jun, S., Przystupa, K., Beshley, M., Kochan, O., Beshley, H., Klymash, M., Wang, J., & Pieniak, D. (2020). A cost-efficient software based router and traffic generator for simulation and testing of IP network. Electronics, 9(1), 40. https://doi.org/10.3390/electronics9010040
  • [12] Makarov, S. V., Medvedev, S. Y., & Yakimov, A. V. (2000). Correlation between the Intensities of Spectral Components of 1/f Noise. Radiophysics and Quantum Electronics, 43(11), 916-922. https://doi.org/10.1023/A:1010361619798
  • [13] Jun, S., Kochan, O., & Kochan, R. (2016). Thermocouples with built-in self-testing. International Journal of Thermophysics, 37(4), 1-9. https://doi.org/10.1007/s10765-016-2044-2
  • [14] Mohajan, H. K. (2017). Two Criteria for Good Measurements in Research: Validity and Reliability. Annals of Spiru Haret University, 17(4), 59-82.
  • [15] Jun, S., Kochan, O., Kochan, V., & Wang, C. (2016). Development and investigation of the method for compensating thermoelectric inhomogeneity error. International Journal of Thermophysics, 37(1), 1-14. https://doi.org/10.1007/s10765-015-2025-x
  • [16] Kwon, J., Delker, C. J., Harris, C. T., Das, S. R., & Janes, D. B. (2020). Experimental and modeling study of 1/f noise in multilayer MoS2 and MoSe2 field-effect transistors. Journal of Applied Physics, 128, 094501. https://doi.org/10.1063/5.0014759
  • [17] Deng, W., & Fossum, E. R. (2019). 1/f Noise Modelling and Characterization for CMOS Quanta Image Sensors. Sensors, 19(24), 5459. https://doi.org/10.3390/s19245459
  • [18] Grüneis, F. (2019). An alternative form of Hooge’s relation for 1/f noise in semiconductor materials. Physics Letters A, 383(13), 1401-1409. https://doi.org/10.1016/j.physleta.2019.02.009
  • [19] Rehman, A., Smirnov, S., Krajewska, A., But, D. B., Liszewska, M., Bartosewicz, B., Pavlov, K., Cywinski, G., Lioubtchenko, D., Knap, W., & Rumyantsev, S. (2021). Effect of ultraviolet light on 1/f noise in carbon nanotube networks. Materials Research Bulletin, 134, 111093. https://doi.org/10.1016/j.materresbull.2020.111093
  • [20] Kolodiy, Z. A. (2010). Flicker-noise of electronic equipment: Sources, ways of reduction and application. Radioelectronics and Communications Systems, 53(8), 412-417. https://doi.org/10.3103/S0735272710080030
  • [21] Kolodiy, Z. A. & Mandziy, B. A. (2016). Calculation of Flicker Noise Power. Automatic Control and Computer Sciences, 50(1), 15-19. https://doi.org/10.3103/S0146411616010041
  • [22] Ouyang, G., Hildebrandt, A., Schmitz, F., & Herrmann, C. S. (2020). Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed. NeuroImage, 205, 116304. https://doi.org/10.1016/j.neuroimage.2019.116304
  • [23] Dave, S., Brothers, T. A., & Swaab, T. Y. (2018). 1/f neural noise and electrophysiological indices of contextual prediction in aging. Brain Research, 1691, 34-43. https://doi.org/10.1016/j.brainres.2018.04.007
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fa1acd83-33f9-48e7-a024-56ec27abc82e
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