PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Design of intelligent component of hierarchical control system

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the main tasks of modern hierarchical systems management are integration of technological, organizational and economic management functions and processes. Another important task is creation of unified information space with accurate, complete and current information. Efficient hierarchical systems management requires intelligent processing of large amounts of heterogeneous information. It is appropriate to process information via intelligent components that are built using artificial neural networks. Strategic information about macroenvironments, microenvironments and internal environments are the input data for intelligent components of upper management levels. Intelligent components may solve different tasks which could have features like: large amount of data, data diversity (quantitative, qualitative, text), contradiction and incomplete data, consistency and high intensity of incoming data, high computing amount with dominance of computing operations over logical operations, recursions and regularity of data processing using neural network algorithms, continuous complications of processing algorithms and increasing requirements for results accuracy, possibility to process data in parallel. The method of synchronized spatiotemporal mapping algorithms for intelligent operation component that provides synchronization of data flow intensity with computing intensity (hardware implementation) and takes into account the processor architecture (software implementation) has been designed. It has been proposed to use following principles: conveyor and spatial parallelism in data processing, modularity, specialization, uniformity and regularity of the structure, programmability architecture. during design of intelligent hardware components. Evaluation of structure of intelligence hardware components carried out using test equipment efficiency. Equipment efficiency takes into account number of interface outputs and number of interneuron connections. At the next step it links performance costs of equipment and evaluates elements by device performance. The method for designing of intelligent component management system that uses synchronized spatio-temporal mapping algorithm has been described. Current method takes into account the components, the requirements of the specific application and provides implementation of intelligent components with high efficiency.
Twórcy
autor
  • Lviv Polytechnic National University
  • Lviv Polytechnic National University
  • Lviv Polytechnic National University
autor
  • Lviv Polytechnic National University
Bibliografia
  • 1. Rashkevych Y., Tkachenko R., Tsmots I., Peleshko D. 2014. Neural-like methods, algorithms and structures of signal and image processing in real time: Monograph /. – Lviv: Lviv Polytechnic National University Publishing House – p.256. (In Ukrainian).
  • 2. Medykovskyy M., Tkachenko R., Tsmots I., Tsymbal Y., Doroshenko A., Skorokhoda A. 2015. Intelligent components of integrated automated control systems: Monograph / – Lviv: Lviv Polytechnic National University Publishing House. – p.280. (In Ukrainian).
  • 3. Tsmots I. 2005. Information technologies and specialized tools for signal and image processing in real time. - Lviv: UAD. – p.227. (In Ukrainian).
  • 4. Lytvyn V., Oborska O., Vovnjanka R. 2015. Approach to decision support Intelligent Systems development based on Ontologies. - ECONTECHMOD. An international quarterly journal. Vol. 4. No. 4, Pp.29 – 35.
  • 5. Pukach A., Teslyuk V., Tkachenko R., Ivantsiv R.- A. 2011. Implementation of neural networks for fuzzy and semistructured data, 11th International Conference - The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM 2011). – Polyana-Svalyava, Ukraine. – Pp.350 – 352.
  • 6. Thomas E. Vollmann, William L. Berry, D. Clay Whybark, F. Robert Jacobs. 2005. Manufacturing planning and control for supply chain management. – New York: McGraw-Hill, p.622.
  • 7. O'Leary D. 2000. Enterprise resource planning systems: systems, life cycle, electronic commerce, and risk. Cambridge university press, p.242.
  • 8. Leon A. 2008. Enterprise resource planning. Tata McGraw-Hill, p.370.
  • 9. Monk E., Wagner B. 2013. Concepts in enterprise resource planning. Cengage Learning, p.272.
  • 10. Drexl A., Kimms A. 2013. Beyond Manufacturing Resource Planning (MRP II): advanced models and methods for production planning. Springer Science & Business Media, p.413.
  • 11. Meyer H., Fuchs F., Thiel K. 2009. Manufacturing Execution Systems: Optimal Design, Planning, and Deployment. New York: McGraw Hill, p.248.
  • 12. Helo P. 2014. Toward a cloud-based manufacturing execution system for distributed manufacturing. Computers in Industry, 65.4: Pp. 646 - 656.
  • 13. Zhang L. 2014. Cloud manufacturing: a new manufacturing paradigm. Enterprise Information Systems, 8.2: Pp. 167-187.
  • 14. Boyer S. 2010. SCADA: supervisory control and data acquisition. International Society of Automation, p.257.
  • 15. Gupta S., Sharma S. 2005. Selection and application of advance control systems: PLC, DCS and PC-based system. Journal of scientific & industrial research, 64: Pp.249-255.
  • 16. Sebastian R., Quesada J. 2006. Distributed control system for frequency control in a isolated wind system. Renewable Energy, 31.3: Pp.285-305.
  • 17. Voyevodin V., Voyevodina Vl. 2002. Parallel computing. - SPb: BHV-Petersburg. – p.608. (in Russian).
  • 18. Holovatyy A., Teslyuk V., Lobur M. 2014. Verilog- AMS model of comb-drive sensitive element of integrated capacitive microaccelerometer for behavioral level of computer-aided design - ECONTECHMOD. An international quarterly journal. Vol. 3, No. 4. Pp.49 – 53.
  • 19. Boreskov A. 2012. Parallel computing on GPU. Architecture and software model of CUDA. Publishing of the Moscow University, p.336. (in Russian).
  • 20. Gergel V. 2010. High throughput computing for multi-processor and multi-core systems. Publishing of the Moscow University, p.544.
  • 21. Palahin A., Opanasenko V. 2006. Reconfigurable computing systems. - K .: Prosvita, p.293. (in Russian).
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-58c1dbf2-c54d-47fe-8623-2781c207ccaa
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.