Transformation of the structure of complex technical systems with partially unusable elements to the visual image
DOI:
https://doi.org/10.15587/1729-4061.2015.51186Keywords:
artificial intelligence, visual display, network structures, elements under redundancy, hardly usable elementsAbstract
The issues of structure state recognition of the hidden part of complex network objects under limited information from their hardly usable elements, including intellectual transformation of information from usable elements into some visual image of the entire object, followed by its recognition and restoration of damaged structures were considered.
The proposed method for the state recognition of network objects formed the basis for constructing the intelligent decision support system during operation and re-engineering of renewable wireless computer networks with the elements, unusable for direct monitoring that increase the structural reliability of these networks.
To achieve the goal, the following tasks were solved: the overall structure of the method for the structure transformation to the visual image was proposed; the theoretical basis of the method, which is the scientific novelty of the work was formulated.
Testing of the proposed method within the common system of maintaining performance and re-engineering of damaged wireless computer networks was performed.
References
- Saveleva, O. S., Maksimov, V. G., Purich, D. A. (2012). Metod distantsionnoy strukturnoy diagnostiki nizkochastotnoy analogovoy seti, chastichno nedostupnoy monitoringu. Pratsi Odeskogo politehnichnogo universytetu. 2 (39), 208–213.
- Nesterenko, S. A., Purich, D. A., Stanovskiy, An. A. (2012). Strukturnaya diagnostika chastichno nedostupnyih monitoringu neftegazovyih ob'ektov. Tehnika i progresivni tehnologiyi u naftogazoviy inzheneriyi – 2012, 181–183.
- Gavrilova, T. A., Horoshevskiy, V. F. (2000). Bazyi znaniy intellektualnyih system. SPb.: Piter, 384.
- Bondarenko, M. F., Shabanov-Kushnarenko, Yu. P. (2006). Teoriya intellekta: uchebnik. Harkov: SMIT, 576.
- Besprovodnyie seti. Available at: http://xreferat.com/33/466-1-besprovodnye-seti.html (Last accessed: 14.08.2015).
- Ponyatie i vidyi besprovodnyih setey. Available at: http://inphormatika.ru /lectures/ponyatie_i_vidy_besprovodnyh_setei.html (Last accessed: 13.07.2015).
- Nesterenko, S. A., Purich, D. A., Stanovskiy, An. A. (2012). Otsenka sostoyaniya setevyih struktur s latentnyimi elementami s pomoschyu skryityih markovskih modeley. Mizhnarodna konferentsiya z upravlinnya «Avtomatika – 2012», 231.
- Pahomov, S., Afanasev, M. Besprovodnyie seti: lomaem, chtobyi zaschischat. Metodyi zaschityi besprovodnyih setey. Available at: http://compress. ru/article.aspx?id=19154 (Last accessed: 11.07.2015).
- Saveleva, O. S., Plachinda, O. E., Purich, D. A (2011). Morphological models of fault-tolerance of complex technical systems. Eastern-European Journal of Enterprise Technologies, 3/2 (51), 39–42. Available at: http://journals.uran.ua/eejet/article/view/1496/1394
- Purich, D. A., Saveleva, O. S., Tonkonogiy, V. M. (2013). Ekspress-analiz strukturnoy nadezhnosti slozhnyih tehnicheskih sistem s nagruzhennyim rezervirovaniem. Suchasni tehnologiyi v mashinobuduvanni, 8, 272–280.
- Ryabinin, I. A. (2000). Nadezhnost i bezopasnost strukturno-slozhnyih system. SPb: Politehnika, 248.
- Saveleva, O. S., Stanovskiy, O. L., Purich, D.O. (2009). Pidvischennya nadiynosti sistem distantsiynogo diagnostuvannya. Naukovi visti «Galitska akademiya», 15 (1), 58–63.
- Naleva, G. V., Saveleva O. S., Purich, D. A. (2009). Intellektualnyie metodyi povyisheniya nadezhnosti telemetricheskoy diagnostiki oborudovaniya. Teoriya i praktika protsesiv. Podribnennya, rozdilennya, zmishuvannya i uschilnennya: zbirnyk naukovyh prats, 14, 95–103.
- Akimov, S. V. (2004). Kompyuternyie modeli dlya avtomatizirovannogo strukturno-parametricheskogo sinteza. Kompyuternoe modelirovanie 2004: Trudy 5-y mezhdunarodnoy konferentsii. Part 1. SPb.: Nestor, 191–197.
- Chetverikov, G. G., Vechirskaya, I. D. (2008). Formalnoe opisanie logicheskogo prostranstva. Shtuchniy Intelekt, 3, 781–789.
- Zinko, R. V. Morfologichne seredovysche dlya doslidzhennya tehnichnih sistem: monografIya. Lviv: Vidavnytstvo Lvivskoyi politehniky, 386.
- Ivchenko, B. P., Martyischenko, L. A., Monastyirskiy, M. L. (1997). Teoreticheskie osnovyi informatsionno-statisticheskogo analiza slozhnyih sistem SPb.: Lan, 320.
- Pankratova, N. D., Savchenko, I. O. (2009). Strategiya zastosuvannya metodu morfologichnogo analizu v protsesi tehnologichnogo. Naukovi visti NTUU «KPI», 2, 35–44.
- Zinko, R. V. (2012). Morfologichne seredovysche dlya modelyuvannya tehnichnyh system. Mizhvuzivskiy zbirnyk «Naukovi notatky», 38, 61–66.
- Solomon, C. J., Breckon, T. P. (2010). Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley-Blackwell, 328. doi: 10.1002/9780470689776
- Burger, W., Burge, Mark J. (2007). Digital Image Processing: An Algorithmic Approach Using Java. Springer, 565.
- Fisher, R., Dawson-Howe, K., Fitzgibbon, A., Robertson, C., Trucco, E. (2005). Dictionary of Computer Vision and Image Processing. John Wiley.
- Bhat, P., Zitnick, C. L., Cohen, M., Curless, B. (2010). Gradientshop: A gradient-domain optimization framework for image and video filtering. ACM Transactions on Graphics, 29 (2), 1–14. doi: 10.1145/1731047.1731048
- Malahov, E. V., Stanovskiy, P. A. (2008). Kodirovanie informatsii dlya poiska videopotokov v hranilischah dannyih. Trudy ONPU, 2 (30), 156–159.
- Stanovskiy, P. A. (2009). Kodirovanie i poisk podvizhnyih i nepodvizhnyih izobrazheniy v hranilischah dannyih. Elektromashinobuduvannya ta elektroobladnannya. Tematichniy vipusk «Komp’yuterni sistemi ta merezhi», Kyiv: TehnIka, 72, 231–234.
- Wolfram Language Artificial Intelligence: The Image Identification Project. Available at: http://blog.stephenwolfram.com/2015/05/wolfram-language-artificial-intelligence-the-image-identification-project/ (Last accessed: 14.05.2015).
- Fooprateepsiri, R., Kurutach, W. (2010). A Highly Robust Approach Image Identification based-on Hausdorff- Trace Transform. International Journal of Digital Content Technology and Its Applications, 4 (1), 26–31. doi: 10.4156/jdcta.vol4.issue1.3
- Srisuk, S., Fooprateepsiri, R., Petrou, M., Waraklang, S., Sunat, K. (2003). A General Framework for Image Retrieval using Reinforcement Learning. The Image and Vision Computing New Zealand 2003, Massey University, New Zealand, 36–41.
- Fizika vizualizatsii izobrazheniy v meditsine. Vol. 1, 2 (1991). Moscow: Mir, 156.
- Harel, D. (1987). Statecharts: a visual formalism for complex systems. Science of Computer Programming, 8 (3), 231–274. doi: 10.1016/0167-6423(87)90035-9
- Kuzo, I. V., Zinko, R. V., Lozoviy, I. S. (2010). Zastosuvannya grafiv pry doslidzhenni funktsionuvannya transportnih zasobiv z pruzhno zchlenovanimi elementami. Naukoviy visnyk NLTU, 20.12, 111–116.
- Cherevko, Yu. M., Zinko, R. V., Lozoviy, I. S. (2009). Vikoristannya grafiv strukturi zv’yazkiv dlya analizu mehanichnih sistem z pruzhno zchlenovanimi elementami. Avtoshlyahovik UkraYini, 4, 12–15.
- Zinko, R. V. (2011). Metodika vikoristannya grafiv pri doslidzhenni roboty mashini z gusenichnim rushiem. Naukoviy visnyk NLTU, 21.13, 117–122.
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Copyright (c) 2015 Сергій Анатолійович Нестеренко, Андрій Олександрович Становський, Алла Володимирівна Торопенко, Павло Степанович Швець
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