Open Access
Issue
MATEC Web Conf.
Volume 178, 2018
22nd International Conference on Innovative Manufacturing Engineering and Energy - IManE&E 2018
Article Number 07003
Number of page(s) 6
Section Innovation, Creativity, Learning and Education in Engineering
DOI https://doi.org/10.1051/matecconf/201817807003
Published online 24 July 2018
  1. R.E. Barlow, F. Proschan. Mathematical Theory of Reliability (J. Wiley & Sons, New York, 1995) [Google Scholar]
  2. S. Duer. Artificial neural network in the control process of object's states basis for organization of a servicing system of a technical objects. Neural Computing & Applications 21(1), 153-160 (2012) [CrossRef] [Google Scholar]
  3. S. Duer. Examination of the reliability of a technical object after its regeneration in a maintenance system with an artificial neural network. Neural Computing & Applications, 21(3), 523-534 (2012) [CrossRef] [Google Scholar]
  4. S. Duer, K. Zajkowski. Taking decisions in the expert intelligent system to support maintenance of a technical object on the basis information from an artificial neural network. Neural Computing & Applications 23(7), 2185-2197 (2013) [CrossRef] [Google Scholar]
  5. S. Duer, K. Zajkowski, R. Duer, J. Paś. Designing of an effective structure of system for the maintenance of a technical object with the using information from an artificial neural network. Neural Computing & Applications 23(3-4), 913-925 (2013) [CrossRef] [Google Scholar]
  6. S. Duer. Applications of an artificial intelligence for servicing of a technical object. Neural Computing & Applications 22(5), 955-968 (2013) [CrossRef] [Google Scholar]
  7. M. Madan, M. Gupta, J. Liang, Homma N. Static and Dynamic Neural Networks, From Fundamentals to Advanced Theory (John Wiley & Sons, Inc, Hoboken, New Jersey, 2003) [Google Scholar]
  8. K. Zajkowski. The method of solution of equations with coefficients that contain measurement errors, using artificial neural network. Neural Computing and Applications 24(2), 431-439 (2014) [CrossRef] [Google Scholar]
  9. K. Zajkowski. An innovative hybrid insulation switch to enable/disable electrical loads without overvoltages. E3S Web of Conferences 19, 01033 (2017) [CrossRef] [EDP Sciences] [Google Scholar]
  10. K. Zajkowski. Settlement of reactive power compensation in the light of white certificates. E3S Web of Conferences, 19, 01037 (2017) [CrossRef] [EDP Sciences] [Google Scholar]
  11. S. Duer, K. Zajkowski, I. Płocha, R. Duer. Training of an artificial neural network in the diagnostic system of a technical object. Neural Computing & Applications 22(7), 1581-1590 (2013) [Google Scholar]
  12. K. Zajkowski, S. Scaticailov. Determination of the environmental impact of reactive power compensation in the power grid. Nonconventional Technologies Review, Romania XX(2), 54-61 (2016) [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.