Dynamics of skewness and kurtosis of dangerous environmental parameters in the event of fire

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.288938

Keywords:

skewness, kurtosis, sampling distribution, dangerous parameters, gas environment, ignition

Abstract

The object of this study is the dynamics of skewness and kurtosis of the selective distribution of dangerous parameters of the gas environment in the current time when materials are ignited. The theoretical substantiation of the methodology for determining the dynamics of skewness and kurtosis based on a sample of an arbitrary size of dangerous parameters of the gas medium moving in the current time of observation has been performed. Thresholds for current skewness and kurtosis are determined depending on sample size and null hypothesis significance levels. The procedure makes it possible to investigate the peculiarities of the dynamics of skewness and kurtosis and to identify moments of time for which alternative hypotheses (stability of parameter dynamics) are valid. Laboratory experiments were conducted to study the dynamics of skewness and kurtosis in terms of carbon monoxide concentration, smoke density, and the temperature of the gas environment during the ignition of alcohol and textiles. The results indicate that the investigated dangerous parameters are generally not Gaussian in the observation interval. It was found that the nature of the dynamics of measures of the current sample distributions of dangerous parameters depends on the type of ignition material and the dangerous parameter. It was established that in the absence of ignition, the dynamics of skewness and kurtosis of dangerous parameters is characterized by different directional skewness and kurtosis. In the event of ignition, the dynamics of skewness and kurtosis are fluctuating (from –4 to 18), which indicates the instability of the development of the dangerous parameter over time. The specified procedure creates an opportunity to detect the instability of the development of a dangerous parameter, which in practice makes it possible to detect the occurrence of fires (with a given reliability) in order to eliminate them and prevent the occurrence of a fire

Author Biographies

Boris Pospelov, Scientific-Methodical Center of Educational Institutions in the Sphere of Civil Defence

Doctor of Technical Sciences, Professor

Vladimir Andronov, National University of Civil Defence of Ukraine

Doctor of Technical Sciences, Professor

Research Center

Yuliia Bezuhla, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Prevention Activities and Monitoring

Roman Lukysha, National University of Civil Defence of Ukraine

PhD

Master's Degree Courses

Tatiana Lutsenko, National University of Civil Defence of Ukraine

PhD, Associate Professor

Department of Prevention Activities and Monitoring

Yurii Kozar, Luhansk State Medical University

Doctor of Law Sciences, Professor

Department of Biology, Histology, Pathomorphology and Forensic Medicine

Mikhail Kravtsov, Kharkiv National Automobile and Highway University

PhD, Associate Professor

Department of Metrology and Life Safety

Larisa Gula, Mykolayiv National Agrarian University

Department of Vocational Training Methodology

Oleksandr Nepsha, Bogdan Khmelnitsky Melitopol State Pedagogical University

Department of Geography and Tourism

Tetiana Zavialova, Bogdan Khmelnitsky Melitopol State Pedagogical University

Department of Geography and Tourism

References

  1. Tiutiunyk, V. V., Ivanets, H. V., Tolkunov, I. A., Stetsyuk, E. I. (2018). System approach for readiness assessment units of civil defense to actions at emergency situations. Scientific Bulletin of National Mining University, 1, 99–105. doi: https://doi.org/10.29202/nvngu/2018-1/7
  2. Semko, A. N., Beskrovnaya, M. V., Vinogradov, S. A., Hritsina, I. N., Yagudina, N. I. (2014). The usage of high speed impulse liquid jets for putting out gas blowouts. Journal of Theoretical and Applied Mechanics, 52 (3), 655–664. Available at: http://jtam.pl/The-usage-of-high-speed-impulse-liquid-jets-for-putting-out-gas-blowouts-,102145,0,2.html
  3. Loboichenko, V. M., Vasyukov, A. E., Tishakova, T. S. (2017). Investigations of Mineralization of Water Bodies on the Example of River Waters of Ukraine. Asian Journal of Water, Environment and Pollution, 14 (4), 37–41. doi: https://doi.org/10.3233/ajw-170035
  4. Vambol, S., Vambol, V., Kondratenko, O., Koloskov, V., Suchikova, Y. (2018). Substantiation of expedience of application of high-temperature utilization of used tires for liquefied methane production. Journal of Achievements in Materials and Manufacturing Engineering, 2 (87), 77–84. doi: https://doi.org/10.5604/01.3001.0012.2830
  5. Popov, O., Iatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V. et al. (2019). Physical Features of Pollutants Spread in the Air During the Emergency at NPPs. Nuclear and Radiation Safety, 4 (84), 88–98. doi: https://doi.org/10.32918/nrs.2019.4(84).11
  6. Popov, O., Іatsyshyn, A., Kovach, V., Artemchuk, V., Taraduda, D., Sobyna, V. et al. (2018). Conceptual Approaches for Development of Informational and Analytical Expert System for Assessing the NPP impact on the Environment. Nuclear and Radiation Safety, 3 (79), 56–65. doi: https://doi.org/10.32918/nrs.2018.3(79).09
  7. Vambol, S., Vambol, V., Sobyna, V., Koloskov, V., Poberezhna, L. (2019). Investigation of the energy efficiency of waste utilization technology, with considering the use of low-temperature separation of the resulting gas mixtures. Energetika, 64 (4). doi: https://doi.org/10.6001/energetika.v64i4.3893
  8. Barannik, V., Ryabukha, Y., Barannik, N., Barannik, D. (2020). Indirect Steganographic Embedding Method Based on Modifications of the Basis of the Polyadic System. 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). doi: https://doi.org/10.1109/tcset49122.2020.235522
  9. Barannik, V., Babenko, Y., Kulitsa, O., Barannik, V., Khimenko, A., Matviichuk-Yudina, O. (2020). Significant Microsegment Transformants Encoding Method to Increase the Availability of Video Information Resource. 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT). doi: https://doi.org/10.1109/atit50783.2020.9349256
  10. Sadkovyi, V., Andronov, V., Semkiv, O., Kovalov, A., Rybka, E., Otrosh, Yu. et. al.; Sadkovyi, V., Rybka, E., Otrosh, Yu. (Eds.) (2021). Fire resistance of reinforced concrete and steel structures. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 180. doi: https://doi.org/10.15587/978-617-7319-43-5
  11. Ragimov, S., Sobyna, V., Vambol, S., Vambol, V., Feshchenko, A., Zakora, A. et al. (2018). Physical modelling of changes in the energy impact on a worker taking into account high-temperature radiation. Journal of Achievements in Materials and Manufacturing Engineering, 1 (91), 27–33. doi: https://doi.org/10.5604/01.3001.0012.9654
  12. Otrosh, Y., Rybka, Y., Danilin, O., Zhuravskyi, M. (2019). Assessment of the technical state and the possibility of its control for the further safe operation of building structures of mining facilities. E3S Web of Conferences, 123, 01012. doi: https://doi.org/10.1051/e3sconf/201912301012
  13. Kovalov, A., Otrosh, Y., Rybka, E., Kovalevska, T., Togobytska, V., Rolin, I. (2020). Treatment of Determination Method for Strength Characteristics of Reinforcing Steel by Using Thread Cutting Method after Temperature Influence. Materials Science Forum, 1006, 179–184. doi: https://doi.org/10.4028/www.scientific.net/msf.1006.179
  14. Kondratenko, O. M., Vambol, S. O., Strokov, O. P., Avramenko, A. M. (2015). Mathematical model of the efficiency of diesel particulate matter filter. Naukovyi visnyk Natsionalnoho hirnychoho universytetu, 6, 55–61. Available at: https://nvngu.in.ua/index.php/en/component/jdownloads/finish/57-06/8434-2015-06-kondratenko/0
  15. Vasyukov, A., Loboichenko, V., Bushtec, S. (2016). Identification of bottled natural waters by using direct conductometry. Ecology, Environment and Conservation, 22 (3), 1171–1176.
  16. Pospelov, B., Kovrehin, V., Rybka, E., Krainiukov, O., Petukhova, O., Butenko, T. et al. (2020). Development of a method for detecting dangerous states of polluted atmospheric air based on the current recurrence of the combined risk. Eastern-European Journal of Enterprise Technologies, 5 (9 (107)), 49–56. doi: https://doi.org/10.15587/1729-4061.2020.213892
  17. World Fire Statistics (2022). CTIF, 27. Available at: https://www.ctif.org/sites/default/files/2022-08/CTIF_Report27_ESG.pdf
  18. Kovalov, A., Otrosh, Y., Ostroverkh, O., Hrushovinchuk, O., Savchenko, O. (2018). Fire resistance evaluation of reinforced concrete floors with fire-retardant coating by calculation and experimental method. E3S Web of Conferences, 60, 00003. doi: https://doi.org/10.1051/e3sconf/20186000003
  19. Chernukha, A., Teslenko, A., Kovalov, P., Bezuglov, O. (2020). Mathematical Modeling of Fire-Proof Efficiency of Coatings Based on Silicate Composition. Materials Science Forum, 1006, 70–75. doi: https://doi.org/10.4028/www.scientific.net/msf.1006.70
  20. Pospelov, B., Andronov, V., Rybka, E., Popov, V., Romin, A. (2018). Experimental study of the fluctuations of gas medium parameters as early signs of fire. Eastern-European Journal of Enterprise Technologies, 1 (10 (91)), 50–55. doi: https://doi.org/10.15587/1729-4061.2018.122419
  21. Pospelov, B., Rybka, E., Meleshchenko, R., Krainiukov, O., Biryukov, I., Butenko, T. et al. (2021). Short-term fire forecast based on air state gain recurrence and zero-order brown model. Eastern-European Journal of Enterprise Technologies, 3 (10 (111)), 27–33. doi: https://doi.org/10.15587/1729-4061.2021.233606
  22. Pospelov, B., Rybka, E., Krainiukov, O., Yashchenko, O., Bezuhla, Y., Bielai, S. et al. (2021). Short-term forecast of fire in the premises based on modification of the Brown’s zero-order model. Eastern-European Journal of Enterprise Technologies, 4 (10 (112)), 52–58. doi: https://doi.org/10.15587/1729-4061.2021.238555
  23. Pospelov, B., Andronov, V., Rybka, E., Samoilov, M., Krainiukov, O., Biryukov, I. et al. (2021). Development of the method of operational forecasting of fire in the premises of objects under real conditions. Eastern-European Journal of Enterprise Technologies, 2 (10 (110)), 43–50. doi: https://doi.org/10.15587/1729-4061.2021.226692
  24. Muhammad, K., Ahmad, J., Baik, S. W. (2018). Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing, 288, 30–42. doi: https://doi.org/10.1016/j.neucom.2017.04.083
  25. Gottuk, D. T., Wright, M. T., Wong, J. T., Pham, H. V., Rose-Pehrsson, S. L., Hart, S. et al. (2002). Prototype Early Warning Fire Detection System: Test Series 4 Results. NRL/MR/6180–02–8602. Naval Research Laboratory. Available at: https://apps.dtic.mil/sti/pdfs/ADA399480.pdf
  26. Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., Baik, S. W. (2018). Convolutional Neural Networks Based Fire Detection in Surveillance Videos. IEEE Access, 6, 18174–18183. doi: https://doi.org/10.1109/access.2018.2812835
  27. Andronov, V., Pospelov, B., Rybka, E., Skliarov, S. (2017). Examining the learning fire detectors under real conditions of application. Eastern-European Journal of Enterprise Technologies, 3 (9 (87)), 53–59. doi: https://doi.org/10.15587/1729-4061.2017.101985
  28. Cheng, C., Sun, F., Zhou, X. (2011). One fire detection method using neural networks. Tsinghua Science and Technology, 16 (1), 31–35. doi: https://doi.org/10.1016/s1007-0214(11)70005-0
  29. Ding, Q., Peng, Z., Liu, T., Tong, Q. (2014). Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory. Algorithms, 7 (4), 523–537. doi: https://doi.org/10.3390/a7040523
  30. Wu, Y., Harada, T. (2004). Study on the Burning Behaviour of Plantation Wood. Scientia Silvae Sinicae, 40 (2), 131. doi: https://doi.org/10.11707/j.1001-7488.20040223
  31. Ji, J., Yang, L., Fan, W. (2003). Experimental Study on Effects of Burning Behaviors’ of Materials Caused by External Heat Radiation. JCST, 9, 139.
  32. Peng, X., Liu, S., Lu, G. (2005). Experimental Analysis on Heat Release Rate of Materials. Journal of Chongqing University, 28, 122.
  33. Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Karpets, K., Pirohov, O. et al. (2019). Development of the correlation method for operative detection of recurrent states. Eastern-European Journal of Enterprise Technologies, 6 (4 (102)), 39–46. doi: https://doi.org/10.15587/1729-4061.2019.187252
  34. Pospelov, B., Rybka, E., Togobytska, V., Meleshchenko, R., Danchenko, Y., Butenko, T. et al. (2019). Construction of the method for semi-adaptive threshold scaling transformation when computing recurrent plots. Eastern-European Journal of Enterprise Technologies, 4 (10 (100)), 22–29. doi: https://doi.org/10.15587/1729-4061.2019.176579
  35. Sadkovyi, V., Pospelov, B., Andronov, V., Rybka, E., Krainiukov, O., Rud, A. et al. (2020). Construction of a method for detecting arbitrary hazard pollutants in the atmospheric air based on the structural function of the current pollutant concentrations. Eastern-European Journal of Enterprise Technologies, 6 (10 (108)), 14–22. doi: https://doi.org/10.15587/1729-4061.2020.218714
  36. Pospelov, B., Rybka, E., Meleshchenko, R., Krainiukov, O., Harbuz, S., Bezuhla, Y. et al. (2020). Use of uncertainty function for identification of hazardous states of atmospheric pollution vector. Eastern-European Journal of Enterprise Technologies, 2 (10 (104)), 6–12. doi: https://doi.org/10.15587/1729-4061.2020.200140
  37. Sadkovyi, V., Pospelov, B., Rybka, E., Kreminskyi, B., Yashchenko, O., Bezuhla, Y. et al. (2022). Development of a method for assessing the reliability of fire detection in premises. Eastern-European Journal of Enterprise Technologies, 3 (10 (117)), 56–62. doi: https://doi.org/10.15587/1729-4061.2022.259493
  38. Pospelov, B., Rybka, E., Samoilov, M., Morozov, I., Bezuhla, Y., Butenko, T. et al. (2022). Defining the features of amplitude and phase spectra of dangerous factors of gas medium during the ignition of materials in the premises. Eastern-European Journal of Enterprise Technologies, 2 (10 (116)), 57–65. doi: https://doi.org/10.15587/1729-4061.2022.254500
  39. Pospelov, B., Rybka, E., Savchenko, A., Dashkovska, O., Harbuz, S., Naden, E. et al. (2022). Peculiarities of amplitude spectra of the third order for the early detection of indoor fires. Eastern-European Journal of Enterprise Technologies, 5 (10 (119)), 49–56. doi: https://doi.org/10.15587/1729-4061.2022.265781
  40. Pospelov, B., Andronov, V., Rybka, E., Chubko, L., Bezuhla, Y., Gordiichuk, S. et al. (2023). Revealing the peculiarities of average bicoherence of frequencies in the spectra of dangerous parameters of the gas environment during fire. Eastern-European Journal of Enterprise Technologies, 1 (10 (121)), 46–54. doi: https://doi.org/10.15587/1729-4061.2023.272949
  41. Du, L., Liu, H., Bao, Z. (2005). Radar HRRP target recognition based on higher order spectra. IEEE Transactions on Signal Processing, 53 (7), 2359–2368. doi: https://doi.org/10.1109/tsp.2005.849161
  42. Hayashi, K., Mukai, N., Sawa, T. (2014). Simultaneous bicoherence analysis of occipital and frontal electroencephalograms in awake and anesthetized subjects. Clinical Neurophysiology, 125 (1), 194–201. doi: https://doi.org/10.1016/j.clinph.2013.06.024
  43. Polstiankin, R. M., Pospelov, B. B. (2015). Stochastic models of hazardous factors and parameters of a fire in the premises. Problemy pozharnoy bezopasnosti, 38, 130–135. Available at: http://nbuv.gov.ua/UJRN/Ppb_2015_38_24
  44. Mykhailiuk, O. P. (2018). Osoblyvosti otsinky nebezpechnykh faktoriv pozhezhi. Materialy IXh Mizhnarodnoi naukovo-praktychnoi konferentsiyi «Teoriya i praktyka hasinnia pozhezh ta likvidatsii nadzvychainykh sytuatsiy». Cherkasy, 270–271. Available at: https://nuczu.edu.ua/images/topmenu/science/konferentsii/2018/5.pdf
  45. Pasport. Spovishchuvach pozhezhnyi teplovyi tochkovyi. Arton. Available at: https://ua.arton.com.ua/files/passports/%D0%A2%D0%9F%D0%A2-4_UA.pdf
  46. Pasport. Spovishchuvach pozhezhnyi dymovyi tochkovyi optychnyi. Arton. Available at: https://ua.arton.com.ua/files/passports/spd-32_new_pas_ua.pdf
  47. Optical/Heat Multi-sensor Detector (2019). Discovery, 1.
  48. McGrattan, K., Hostikka, S., McDermott, R., Floyd, J., Weinschenk, C., Overholt, K. (2016). Fire Dynamics Simulator Technical Reference Guide. National Institute of Standards and Technology. Vol. 3. NIST. Available at: https://www.fse-italia.eu/PDF/ManualiFDS/FDS_Validation_Guide.pdf
  49. Floyd, J., Forney, G., Hostikka, S., Korhonen, T., McDermott, R., McGrattan, K. (2013). Fire Dynamics Simulator (Version 6) User’s Guide. National Institute of Standard and Technology. Vol. 1. NIST.
  50. Levin, B. R. (1989). Teoreticheskie osnovy statisticheskoy radiotekhniki. Moscow: Radio i svyaz', 656.
  51. Orlov, Yu. N., Osminin, K. P. (2008). Postroenie vyborochnoy funktsii raspredeleniya dlya prognozirovaniya nestatsionarnogo vremennogo ryada. Matematicheskoe modelirovanie, 20 (9), 23–33.
  52. NIST/SEMATECH (2012). e-Handbook of Statistical Methods. doi: https://doi.org/10.18434/M32189
  53. Dragotti, P. L., Vetterli, M., Blu, T. (2007). Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix. IEEE Transactions on Signal Processing, 55 (5), 1741–1757. doi: https://doi.org/10.1109/tsp.2006.890907
  54. An introduction to kernel density estimation (2001). Available at: https://www.mvstat.net/tduong/research/seminars/seminar-2001-05/
  55. Derr, V. Ya. (2021). Teoriya veroyatnostey i matematicheskaya statistika. Sankt-Peterburg: Lan', 596.
  56. Rakhmangulov, R. S., Ishbirdin, A. R., Salpagarova, A. S. (2014). Is fluctuating asymmetry an index of destabilization or finding ways to adaptive morphogenesis? Vestnik Bashkirskogo universiteta, 19 (3).
  57. Baranov, S. G. Burdakova, N. E. (2015). Otsenka stabil'nosti razvitiya. Metodicheskie podkhody. Vladimir: VlGU, 72.
Dynamics of skewness and kurtosis of dangerous environmental parameters in the event of fire

Downloads

Published

2023-10-31

How to Cite

Pospelov, B., Andronov, V., Bezuhla, Y., Lukysha, R., Lutsenko, T., Kozar, Y., Kravtsov, M., Gula, L., Nepsha, O., & Zavialova, T. (2023). Dynamics of skewness and kurtosis of dangerous environmental parameters in the event of fire. Eastern-European Journal of Enterprise Technologies, 5(10 (125), 53–62. https://doi.org/10.15587/1729-4061.2023.288938

Issue

Vol. 5 No. 10 (125) (2023): Ecology

Section

Ecology

License

Copyright (c) 2023 Boris Pospelov, Vladimir Andronov, Yuliia Bezuhla, Roman Lukysha, Tatiana Lutsenko, Yurii Kozar, Mikhail Kravtsov, Larisa Gula, Oleksandr Nepsha, Tetiana Zavialova

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.