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Usage of SaaS software delivery model in intelligent house systems

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Wykorzystanie przetwarzania w chmurze SaaS w systemach "inteligentnego domu"
Języki publikacji
EN
Abstrakty
EN
The article presents the usage of the SaaS cloud computing model in "intelligent house" systems for optimization of computation load between the client and server parts of the system. Also was developed artificial neural network model for detection of irrational electricity usage by devices of the "intellectual house".
PL
W artykule przedstawiono zastosowanie modelu przetwarzania w chmurze SaaS w systemach "inteligentnego domu" dо optymalizacji obciążenia obliczeniowego między klientem a elementami serwerowymi systemu. Opracowano także model sztucznej sieci neuronowej, aby wykryć nieracjonalne zużycie energii elektrycznej przez urządzenia "inteligentnego domu".
Rocznik
Strony
38--41
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
  • Institute of Computer Science and Information Technologies, 28a Bandera str., Building 5, Lviv
  • Lviv Polytechnic National University, Institute of Computer Science and Information Technologies, 28a Bandera str., Building 5, Lviv
  • Lviv Polytechnic National University, Institute of Computer Technologies, Automation and Metrology, 28a Bandera str., Building 5, Lviv, Ukraine
  • Lviv Polytechnic National University, Separated Structural Subdivision Educational and Research Institute of Business and Innovative Technologies, 18 Horbachevskoho St., Building 32, Lviv
Bibliografia
  • [1] Biljana L. Risteska Stojkoska, Kire V. Trivodaliev, “A review of the Internet of Things for smart home: Challenges and solutions”, Journal of Cleaner Production, Vol. 140, Part 3, 1 January 2017, pp.1454-1464.
  • [2] Teslyuk V., Beregovskyi V., Denysyuk P., Teslyuk T., Lozynskyi A. Development and Implementation of the Technical Accident Prevention Subsystem for the Smart Home System // International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.1, 2018, pp.1-8. DOI:10.5815/ijisa.2018.01.01.
  • [3] Amit Badlani, Surekha Bhano, “Smart Home System Design based on Artificial Neural Networks”, in Proceedings of the World Congress on Engineering and Computer Science 2011, San Francisco, USA, October 19-21, 2011, pp. 106-111.
  • [4] Kazarian A., Teslyuk V., Tsmots I., Mashevska M. “Units and structure of automated “smart” house system using machine learning algorithms” in Proceeding of the 14th International Conference The Experience of Designing and Application of Cad Systems in Microelectronics, CADSM’2017, 21-25 February 2017, Polyana, Lviv, Ukraine. 2017. – P. 364 – 366.
  • [5] Y. Liu, B. Qiu, X. Fan, H. Zhu, B. Han, “Review of Smart Home Energy Management Systems.”, Energy Procedia, Vol. 104, pp. 504 – 508, 2016.
  • [6] A. Veit, C. Goebel, R. Tidke, C. Doblander, “Household electricity demand forecasting: benchmarking state-of-the-art methods” in Proc. of the 5th international conference on Future energy systems. Cambridge, United Kingdom, pp. 233-234, June 11 - 13, 2014.
  • [7] Jui-Sheng Chou, Dac-Khuong Bui, “Modeling heating and cooling loads by artificial intelligence for energy-efficient building design”, Energy and Buildings Volume 82, pp. 437-446, October 2014.
  • [8] L. Magnier, F. Haghighat, “Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network”, Building and Environment, Volume 45, Issue 3, pp. 739-746, March 2010.
  • [9] G. Eason, B. Noble, and I.N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955.
  • [10] J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.
  • [11] I.S. Jacobs and C.P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G.T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271-350.
  • [12] Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740-741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
  • [13] Magali, R.G., Meireles, P.E.M. and Marcelo, G.S. (2003) A Comprehensive Review for Industrial Applicability of Artificial Neural Networks. IEEE Transactions on Industrial Electronics, 50, 585-601.
  • [14] Tobias Teich, Falko Roessler, Daniel Kretz, Susan Frank. “Design of a Prototype Neural Network for Smart Homes and Energy Efficiency” in Proceedings of 24th DAAAM International Symposium on Intelligent Manufacturing and Automation, Zwickau, Germany, 2013, pp.603-608.
  • [15] Fadare, D. and Ofidhe, U. (2009) Artificial Neural Network Model for Prediction of Friction Factor in Pipe Flow. Journal of Applied Science Research, 5, 662-670.
  • [16] Pushkar S., Sanghamitra S. “Arduino-based smart irrigation using water flow sensor, soil moisture sensor, temperature sensor and ESP8266 WiFi module”, in Proceeding of Humanitarian Technology Conference (R10-HTC), 2016 IEEE Region 10.
  • [17] Brkić, D. and Ćojbašić, Ž. (2016) Intelligent Flow Friction Estimation. Computer Intelligence and Neuroscience, 2016, Article ID: 5242596. http://dx.doi.org/10.1155/2016/5242596
  • [18] D. A. Dornfield, “Neural Network Sensor Fusion for Tool Condition Monitoring,” Annals CIRP, Vol. 39, No. 1, 1990, pp. 101-105. doi:10.1016/S0007-8506(07)61012-9
  • [19] Lytvyn, V., Peleshchak, I., Peleshchak, R. The compression of the input images in neural network that using method diagonalization the matrices of synaptic weight connections. 2nd International Conference on Advanced Information and Communication Technologies, AICT 2017 – Proceedings. 2017.
  • [20] Geche, F., Kotsovsky, V., Batyuk, A. Synthesis of the integer neural elements. Proceedings of the International Conference on Computer Sciences and Information Technologies, CSIT 2015. – Lviv, 2015. – P. 63 – 66.
  • [21] P. Tymoshchuk and S. Shatnyi, “KWTA Neural Network Hardware Implementation Using FPGA for Signals Classification”, in Proc. IX-th Int. Conf. “Perspective technologies and methods in MEMS design”, Polyana- Svalyava, Ukraine, June 22-24, 2014, pp. 82-85.
  • [22] Yazdi, M. and Bardi, A. (2011) Estimation of Friction Factor in Pipe Flow Using Artificial Neural Network. Canadian Journal on Automation, Control and Intelligent Systems, 2, pp. 52-56
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-97447832-8e8d-4897-b8cf-c6ed0202548e
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