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DOI: 10.15507/0236-2910.028.201802.148-163
Dielectric Permeability of Forestry Depending on Environmental Parameters in Radio Frequency Monitoring
Vladimir V. Pobedinsky
Professor, Chair of Service and Technical Operation, Ural State Forestry University (37 Sibirskiy Trakt St., 620100 Ekaterinburg, Russia), D.Sc. (Engineering), ResearcherID: G-3245-2018, ORCID: https://orcid.org/0000-0001-6318-3447, This email address is being protected from spambots. You need JavaScript enabled to view it.
Asgat M. Gazizov
Professor, Chair of Fire Protection and Industrial Safety, Ufa State Oil Technical University (5/14 Kosmonavtov St., Ufa 500064, Russia), D.Sc. (Engineering), ResearcherID: G-4307-2018, ORCID: https://orcid.org/0000-0001-7940-8444, This email address is being protected from spambots. You need JavaScript enabled to view it.
Sergey P. Sannikov
Associate Professor, Chair of Automation of Production Process, Ural State Forestry University (37 Sibirskiy Trakt St., 620100 Ekaterinburg, Russia), ResearcherID: G-4047-2018, ORCID: https://orcid.org/0000-0002-6135-6954, This email address is being protected from spambots. You need JavaScript enabled to view it.
Andrey A. Pobedinskiy
Senior Lecturer, Chair of Forestry and Applied Mechanics, State Agrarian University of the Northern Trans-Urals (18, Roshinskoye Shosse, Tyumen 625621, Russia), ResearcherID: G-3777-2018, ORCID: https://orcid.org/0000-0001-7548-3076, This email address is being protected from spambots. You need JavaScript enabled to view it.
Introduction. New approach to forestry monitoring is presented. The relevance of the study is caused by the need to improve the forest management system based of modern information technologies. The paper demonstrates radio frequency monitoring as a most effective solution using a network of radio-frequency devices. This system quickly tracks the movement of forest resources, detect forest fires at the beginning of smoke and perform other functions. Complex dielectric permittivity is one of the main parameters for the design and operation of the system. The usual statistical methods do not allow obtaining such permeability. Thus, the aim of the study is to find a functional dependence of the permittivity on the parameters of the forest environment based on fuzzy inference.
Materials and Methods. Mathematical and fuzzy modeling are methods of theoretical research in this paper. In addition, methods of forest inventory and logging processes, information theory and signaling, mathematical statistics, and experimental theory were used to perform experimental studies on approbation of a radio frequency monitoring system and to verify the adequacy of the proposed fuzzy model. We used Fuzzy Logic Toolbox software with MatLab technical computing software as a tool for synthesis.
Results.The dependence of the permittivity on the parameters of the forest environment based on fuzzy inference was obtained. Formally, the total complex dielectric constant εк of the forest area (canopy) and the values of the input quantities are determined as follows: εк = f (Vi , α), where Vi – volume fraction of i component of the forest environment (in real conditions, according to experimental data, is from 0 to 0,5); α – constant, taking into account the type of forest (from 0 to 0,5 – open area; from 0,5 to 1,5 – pine forest of standard height 25 m; from 1,5 to 2,5 – mixed forest; from 3,5 to 4,5 – birch grove; from 4,5 to 5,0 – spruce forest). A feature of the proposed approach is a discrete representation of the forest environment as a sum of forest elements. This approach provides an accurate measure of the permittivity of the forest environment.
Conclusions. The practical significance of the results lies in the possibility of creating an information support structure for an automated forest management system based on forestry monitoring. The proposed function of the permittivity of the forest area takes into account the main parameters of the forest environment, so it is sufficiently correct. This function is necessary for the design of radio frequency monitoring systems of forestry and allows us to implement a fundamentally new approach to solving the tasks of forest fund monitoring.
Keywords: radio frequency monitoring, forestry, forest, permittivity, complex dielectric permittivity, fuzzy modeling, fuzzy output
For citation: Pobedinsky V. V., Gazizov A. M., Sannikov S. P., Pobedinskiy A. A. Dielectric Permeability of Forestry Depending on Environmental Parameters in Radio Frequency Monitoring. Vestnik Mordovskogo universiteta = Mordovia University Bulletin. 2018; 28(2):148–163. DOI: https://doi.org/10.15507/0236-2910.028.201802.148-163
Authors’ contribution: V. V. Pobedinsky ‒ scientific management, mathematical formulation of the problem and development of a fuzzy model; A. M. Gazizov ‒ writing the draft; S. P. Sannikov ‒ development of experimental studies, testing of the model; A. A. Pobedinskiy ‒ software development for implementing the model, data processing and interpretation.
All authors have read and approved the final version of the paper.
REFERENCES
1. Sannikov S. P., Pobedinsky V. V., Borodulin I. V., Pobedinsky A. A. Method of monitoring of illegal tree felling with the use of radio-frequency devices and wireless sensor network. Sistemy. Metody. Tekhnologii = Systems. Methods. Technologies. 2017; 1(33):118–123.
2. Sannikov S. P., Pobedinsky V. V., Borodulin I. V., Pobedinsky A. A. Model of information support of the forest management system on the basis of radio frequency monitoring of the forest fund. Sistemy. Metody. Tekhnologii = Systems. Methods. Technologies. 2017; 2(34):109–115.
3. Shtovba S., Rotshtein A., Pankevich O. Fuzzy rule based system for diagnosis of stone construction cracks of buildings // Advances in Computational Intelligence and Learning, Methods and Applications. Dordrecht : Kluwer Academic Publishers, 2002. P. 401–411.
4. Zadeh L. A. Fuzzy sets. Information and Control. 1965; 8(3):338–353.
5. Zadeh L. A. Fuzzy logic. IEEE Transactions on Computers. 1988; 21(4):83–93.
6. Mamdani E. H. Application of fuzzy logic to approximate reasoning using linguistic synthesis // IEEE Transactions on Computers. 1977; 26(12):1182–1191.
7. Takagi T., Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics. 1985; 15(1):116–132.
8. Garg H. A linear programming method based on an improved score function for interval-valued Pythagorean fuzzy numbers and its application to decision-making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2018; 26(01):67–80.
9. Yue H, Li J., Shi J., Yang W. Adaptive fuzzy tracking control for stochastic nonlinear systems with time-varying input delays using the quadratic functions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2018; 26(01):109–142.
10. Schneider J., Urban R. Proof of Donsker’s invariance principle based on support functions of fuzzy random vectors. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2018; 26(01):27–42.
11. Carneiro A. L. G., Porto Jr. A. C. S. An integrated approach for process control valves diagnosis using fuzzy logic. World Journal of Nuclear Science and Technology. 2014; 4:148–157.
12. Lin J.-J., Chuang C.-J., Ko C.-F. Applying GA and fuzzy logic to breakdown diagnosis for spinning process. Intelligent Information Management. 2017; 9:21–38.
13. Ali A., El-Serafi K., Mostafa S. A. K., El-Sheimy N. Frequency features based fuzzy system for rotating machinery vibration analysis using smartphones low-cost MEMS sensors. Journal of Sensor Technology. 2016; 6:56–74.
14. Uraon K. K., Kumar S. Analysis of defuzzification method for rainfall event. International Journal of Computer Science and Mobile Computing. 2016; 1(1):341–354.
15. Alguliyev R., Abdullayeva F. Development of fuzzy risk cal-culation method for a dynamic federation of clouds. Intelligent Information Management. 2015; 7:230–241.
16. Moises S. A., Pereira S. do L. Dealing with empty and overabundant answers to flexible queries. Journal of Data Analysis and Information Processing. 2014; 2(1):12–18.
17. Perez O. Fuzzy law: A theory of quasi-legality. Canadian Journal of Law & Jurisprudence. 2015; 28(2):343–370.
18. Xu W., Liu G., Yu X. A binomial tree approach to pricing vulnerable option in a vague world. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2018; 26(01):143–162.
19. Ntaganda J. M., Haggar M. S. D., Mampassi B. Fuzzy logic strategy for solving an optimal control problem of therapeutic hepatitis C virus dynamics. Open Journal of Applied Sciences. 2015; 5(9):527–541.
20. Gour A., Pardasani K. R. Statistical and soft fuzzy set based analysis of amino acid association patterns in peptide sequence of swine influenza virus. Advanced Science, Engineering and Medicine. 2018; 10(2):137–144.
21. Miranda G. H. B., Felipe J. C. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Computers in Biology and Medicine. 2015; 64:334–346.
22. Sannikov S. P., Pobedinsky V. V., Gazizov A. M., Borodulin I. V., Chernitsyn M. A., Kuzminov N. S. Dependence of the signal power loss on the forest ambience parameters under radio-frequency monitoring of forest fund. Sistemy. Metody. Tekhnologii = Systems. Methods. Technologies. 2016; 32(4):181–187.
23. Sannikov S. P., Pobedinskiy V. V., Pobedinskiy A. A. Model scattering of radio waves in a forest. Izmereniye. Monitoring. Upravleniye. Kontrol = Measurement. Monitoring. Control. 2017; 21(3):39–48.
24. Sannikov S. P., Pobedinskiy V. V., Pobedinskiy A. A. Experimental study of characteristics of a signal at the radio-frequency monitoring of forest environment. Vestnik Povolzhskogo gosudarstvennogo tekhnologicheskogo universiteta. Ser.: Les. Ekologiya. Prirodopolzovaniye = Volga State Technological University Bulletin. Ser.: Forest. Ecology. Nature Management. 2017; 36(4):48–58.
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