Open Access
MATEC Web Conf.
Volume 329, 2020
International Conference on Modern Trends in Manufacturing Technologies and Equipment: Mechanical Engineering and Materials Science (ICMTMTE 2020)
Article Number 03071
Number of page(s) 9
Section Mechanical Engineering
Published online 26 November 2020
  1. W. Michael, Grieves Digital Twin: Manufacturing Excellence through Virtual Factory Replication. LLC, 7 p. (2014) [Google Scholar]
  2. D. Tarkhov, A. Vasilyev, Semi-empirical Neural Network Modeling and Digital Twins Development. London. Academic Press. 240 p. (2020) [Google Scholar]
  3. R. Kharat, V. Bavane, S. Jadhao, R. Marode, Digital Twin: Manufacturing Excellence through Virtual Factory Replication. Global journal of engineering science and researches. 10 p. (2018) [Google Scholar]
  4. A.G. Kravets, A.A. Bolshakov, M.V. Shcherbakov, Cyber-Physical Systems: Advances in Design & Modelling. Springer. 340 p. (2020) [Google Scholar]
  5. E.A. Lee, Cyberphysical systems: Design challenges. 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC). Orlando. IEEE. pp. 363-369. (2020) [Google Scholar]
  6. K. Sang-Hoon, Electric Motor Control DC, AC and BLDC Motors. Elsevier Inc., 426 p. (2017) [Google Scholar]
  7. P. Stepanov, Yu. Nikitin, Diagnostics of Mechatronic Systems on the Basis of Neural Networks with High-Performance Data Collection. Mechatronics 2013: Recent Technological and Scientific Advances. Springer Intrenational Publishing Swizerland, 2014. 7–9.10.2013, Brno, Czech Republic. Pp. 433-440. (2013) [Google Scholar]
  8. Yu. Turygin, P. Božek, I. Abramov, Yu. Nikitin, Reliability Determination and Diagnostics of a Mechatronic System. Advances in Science and Technology. Vol. 12, No. 2, June, pp. 274-290. (2018) [Google Scholar]
  9. S. Trefilov, Y. Nikitin, Automatic warehouses with transport robots of increased reliability. ActaLogistica, vol. 5, no. 3. Pp. 19-23. (2018) [Google Scholar]
  10. P. Bozek, A. Shchenyatsky, Yu. Turygin, Yu. Nikitin, Yu. Karavaev, Reverse validation of a programmed robot trajectory based on INS. 12th International Conference ELEKTRO 2018. IEEE, Mikulov, Czech Republic. 21-23 May 2018. Pp. 1-4. (2018) [Google Scholar]
  11. S.A. Trefilov, Yu.R. Nikitin, Robot drives diagnostics by identifiability criterion based on state matrix. Instrumentation Engineering, Electronics and Telecommunications - 2019 : Proceedings of the V International Forum (Izhevsk, Russia, November 20-22, 2019). Izhevsk. Publishing House of Kalashnikov ISTU. 123 p. Pp.105-114. (2019) [Google Scholar]
  12. P. Eykhoff, System Identification: Parameter and State Estimation. Wiley-Interscience, New York. 555 p. (1974) [Google Scholar]
  13. P. Eykhoff, ed., Trends and progress in system identification. Oxford, England: Pergamon. 402 p. (1981) [Google Scholar]
  14. D. Graupe, Identification of system. New York, R.E.Krieger Publishing Company. 302 p. (1976) [Google Scholar]
  15. L. Ljung, System identification. Theory for the user. 2nd ed. PTR Prentice Hall, Upper Saddle River. 609 p. (1999) [Google Scholar]
  16. A. Sage, J. Melsa, System identification. New York, Academic press. 221 p. (1999) [Google Scholar]
  17. A. Sage, J. Melsa, Estimation Theory with Applications to Communications and Control. New York, McGraw-Hill. 496 p. (1971) [Google Scholar]
  18. H. Luo, Plug-and-Play Monitoring and Performance Optimization for Industrial Automation Processes. Springer Fachmedien Wiesbaden GmbH. 158 p. (2017) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.