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A model of destructive processes based on interval fuzzy rough soft sets

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Warianty tytułu
PL
Model procesów destrukcyjnych opartych na interwałowych rozmytych zbiorach przybliżonych
Języki publikacji
EN
Abstrakty
EN
This work presents a spatial model of destructive processes for the real-time GIS-based decision support systems. A dynamic fuzzy rough soft topology represents a structure of a geoecotechnogenic system that contains a multitude of interacting processes, which evolve in space and time. In disaster conditions, some of the interacting processes can be destructive. Their dynamics are modeled using the spread model. The area of interest is represented as an approximation by a grid of cubic cells. This allows taking into account the peculiarities of the initial information obtained from drones using remote sensing techniques and having a significant uncertainty. The proposed model reduces the computational complexity and provides the acceptable performance of real-time DSS.
PL
W niniejszej pracy przedstawiono przestrzenny model destrukcyjnych procesów dla systemów wspomagania decyzji opartych na GIS w czasie rzeczywistym. Dynamicznie rozmyta topologia przybliżona reprezentuje strukturę geo-eko-techno-gennego systemu, który zawiera wiele interakcji procesów, które ewoluują w przestrzeni i czasie. W warunkach katastrofy niektóre z oddziałujących procesów mogą być destrukcyjne. Ich dynamika jest modelowana przy użyciu modelu spreadu. Obszar zainteresowania jest reprezentowany jako przybliżenie przez siatkę komórek sześciennych. Pozwala to na uwzględnienie specyfiki początkowej informacji uzyskanej z dronów za pomocą technik teledetekcji i posiadającej znaczną niepewność. Proponowany model zmniejsza złożoność obliczeniową i zapewnia akceptowalną wydajność DSS w czasie rzeczywistym.
Rocznik
Strony
132--137
Opis fizyczny
Bibliogr. 32 poz., rys., wykr.
Twórcy
  • Kherson National Technical University, Berislavskojy shosse, 24, 73040, Kherson, Ukraine
  • Kherson National Technical University, Berislavskojy shosse, 24, 73040, Kherson, Ukraine
  • Lublin University of Technology, Institute of Electronics and Information Technology, Nadbystrzycka 38A, 20-618 Lublin, Poland
  • Sarsen Amanzholov East Kazakhstan State University
  • Kazakh National Research Technical University named after K.I.Satpayev
Bibliografia
  • [1] Sherstjuk V., Zharikova M., Sokol I., Approximate spatial model based on fuzzy-rough to-pology for real-time decision support systems, Proc. on IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 1037–1042, Kyiv (2017).
  • [2] Chen G., Zhao J., Yuan L., Ke Z., Gu M., Wang T. Implementation of a geological dis-aster monitoring and early warning system based on multi-source spatial data: a case study of Deqin Country, Yunnan Province. Hazards Earth Syst. Sci., 15 (2017).
  • [3] Chi M., Plaza A., Beneditsson J.A., Sun Z., Shen J., Zhu Y. Big data for remote sensing: challenges and opportunities. Proc. of the IEEE 104 (11) (2016), 2207-2219.
  • [4] Yuan C., Zhang Y., Liu Z. A Survey on Technologies for Automatic Forest Fire Monitoring, Detection and Fighting Using UAVs and Remote Sensing Techniques // Canadian Journal of Forest Research 45(7) (2015), 783-792.
  • [5] Martínez de Dios J., Arrue B., Merino L., Ollero A., Gómez- Rodríguez F. Computer vision techniques for forest fire perception. Image and Vision Computing 26(4) (2007), 550- 562.
  • [6] Merino L., Caballero F., Martinez-de-Dios J., Maza I., Ollero A. An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement. Journal of Intelligent & Robotic Systems 65 (2012), 533-548.
  • [7] Zharikova M., Sherstjuk V. Development of the Model of Natural Emergencies in Decision Support System. Eastern European Journal of Enterprise Technologies 4(1) (2015), 62- 69.
  • [8] Zadeh L.A. Fuzzy sets. Information and Control 8 (1965), 338- 353.
  • [9] Pawlak Z., Jerzy W., Slowinski R., Ziarko W. Rough Sets. Comm. of ACM 38(11), 88–95 (1995).
  • [10] Molodtsov D.A. Soft set theory – first results. Computer & Mathematics with Applica-tions, pp. 19–31 (1999).
  • [11] Maji P. K., Roy A. R., Iswas R. B. An application of soft sets in a decision making problem. Computers and Mathematics with Applications 44, 1077–1083 (2002).
  • [12] Maji P. K., Iswas R. B., Roy A. R. Fuzzy soft sets. Journal of Fuzzy Mathematics 9 (2001), 589–602.
  • [13] Meng D., Zhang X., Qin K. Soft rough fuzzy sets and soft fuzzy rough sets. Computers and Mathematics with Applications 62 (2011), 4635-4645.
  • [14] Zharikova M., Sherstjuk V. Threat assessment method for intelligent disaster decision support system. Advances in Int. Systems and Computing 512 (2016), 81-99.
  • [15] Allam A.A., Bakeir M.Y., Abo-Tabl E.A. Some Methods for Generating Topologies by Relations. Bull. Malays. Math. Sci. Soc. 2(31) (2008), 35–45.
  • [16] Varol B.P., Aygun H. Fuzzy soft topology. Hacettepe Journal of Mathematics and Statistics 41(3) (2012), 407–419.
  • [17] El-Diafy S.N. Comparative study of fuzzy topology. Thesis submitted in partial fulfillment of the requirement for the degree of master of mathematics, Gaza, The Islamic University of Gaza, (2014).
  • [18] Meng D., Zhang X., Qin, K. Soft rough fuzzy sets and soft fuzzy rough sets. Computers and Mathematics with Applications 62 (2011), 4635–4645.
  • [19] Tang W., Wu J., Zheng D. On fuzzy rough sets and their topological structures. Mathematical problems in engineering 4 (2014), 17.
  • [20] Feng F., Li Y., Leoreanu-Fotea V. Application of level soft sets in decision making based on interval-valued fuzzy soft sets. Computers and Mathematics with Applications 60 (2010), 1756-1767.
  • [21] Kondratiuk, S., Kruchinin, K., Krak, I., Kruchinin, S. Information technology for security system based on cross platform software (2018), NATO Science for Peace and Security Series A: Chemistry and Biology, Part F1, 331-339.
  • [22] Krak, I., Kondratiuk, S. Cross-platform software for the development of sign communication system: Dactyl language modelling, Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017, 1, 167-170. DOI: 10.1109/STCCSIT. 2017.8098760.
  • [23] Kryvonos, I.G., Krak, I.V., Barmak, O.V., Kulias, A.I. Methods to Create Systems for the Analysis and Synthesis of Communicative Information. Cybernetics and Systems Analysis, 53 (6) (2017), 847-856. DOI: 10.1007/s10559-017- 9986-7.
  • [24] Smolarz A., Wojcik W., Gromaszek., K., Fuzzy modeling for optical sensor for diagnostics of pulverized coal burner, Procedia Engineering, 47 (2012), 1029-1032.
  • [25] Pavlov S.V., Kozhemiako V.P., Kolesnik P.F. et al., Physical principles of biomedical optics. VNTU, Vinnytsya 2010.
  • [26] Vassilenko, S Valtchev, JP Teixeira, S Pavlov. Energy harvesting: an interesting topic for education programs in engineering specialities. Internet, Education, Science (IES- 2016) – 2016. – P. 149-156
  • [27] Analysis of microcirculatory disorders in inflammatory processes in the maxillofacial region on based of optoelectronic methods / Pavlov, S.V., Barylo, A.S., Kozlovska, T.I. and etc., Przegląd Elektrotechniczny 93 (5) (2017), 114-117.
  • [28] Valentina K. Serkova, Sergey V. Pavlov and etc. "Medical expert system for assessment of coronary heart disease destabilization based on the analysis of the level of soluble vascular adhesion molecules", Proc. SPIE 10445, Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017, 104453O (7 August 2017).
  • [29] Vysotskaya E. V., Porvan A. P., Bespalov Yu. G., Nosov K. V., Klimenko V. A., and Trubitsyn A. A. (2014). Prognozirovanie techeniya atopicheskogo dermatita u detey s ispol'zovaniem diskretnogo modelirovaniya dinamicheskikh system. Vostochno-Evropeyskiy zhurnal peredovykh tekhnologiy, Vol.3, No 4 (69). pp. 21-25.
  • [30] Grigor'ev A. Ya., Zholtkevich G. N., Nosov K. V., Gamulya Yu. G., Bespalov Yu. G., Vysotskaya E. V., and Pecherskaya A. I., Diskretnye modeli dinamicheskikh sistem, opredelyayushchikh stabil'nost' gidrobiotsenozov. Veterinarnaya meditsina 99 (2014), 164–167.
  • [31] Borovska T., "Optimal Aggregation Models for the Problem of Minimizing the Total Expenses of Multiproduct Production", Proceedings of the XI International Scientific and Technical Conference “Computer science and information technologies” CSIT’2016, Lviv, Ukraine, 6-10 September 2016 (13 October 2016). – Lviv: IEEE, 2016. – P.P. 136-139, 16377666; DOI: 10.1109/STC-CSIT.2016.7589889.
  • [32] Karpinski M., Piontko N., Karpinskyi V., Automatic identification method of blurred images. Informatyka Automatyka i Pomiary w Gospodarce i Ochronie Środowiska IAPGOŚ 5(1)/2015, 59-61.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-482a2284-3411-4d91-8c4a-396d4d1c8d60
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