EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 35 (2020) ITM Web Conf., 35 (2020) 01011 References
Open Access
Issue
ITM Web Conf.
Volume 35, 2020
International Forum “IT-Technologies for Engineering Education: New Trends and Implementing Experience” (ITEE-2019)
Article Number 01011
Number of page(s) 9
Section Engineering Education Technology Based on Using Digital Resources
DOI https://doi.org/10.1051/itmconf/20203501011
Published online 09 December 2020
  1. J. Li & G. Liang, Petrochemical equipment corrosion prediction based on BP artificial neural network, 2015 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 238-242, IEEE (2015) [Google Scholar]
  2. Huo Shouliang, et al., Using artificial neural network models for eutrophication prediction, Procedia Environmental Sciences, 18, pp. 310-316 (2013) [CrossRef] [Google Scholar]
  3. E.V. Panfilova, S.V. Sidorova & D.Y. Shramko, Application of Neural Network for Forecasting Reliability of Vacuum Equipment, 2019 International Russian Automation Conference (RusAutoCon), pp. 1-5, IEEE (2019) [Google Scholar]
  4. D.D. Vasilev, E.I. Malevannaya & K.M. Moiseev, Vacuum coating system for deposition of superconducting W x Si (1–x) ultrathin films used in single photon detectors, Journal of Physics: Conference Series, Vol. 872, No. 1, p. 012027, IOP Publishing (2017) [CrossRef] [Google Scholar]
  5. Y.V. Panfilov, L.L. Kolesnik, V.T. Ryabov & S.V. Sidorova, Research and development complex with remote access, Journal of Physics: Conference Series, Vol. 872, No. 1, p. 012010, IOP Publishing (2017). [CrossRef] [Google Scholar]
  6. S.V. Sidorova, M.A. Pronin & A A. Isaeva, Automated Unit for Control of Initial Stages of Metal Islands Thin Films and Nanostructures Growth, 2018 International Russian Automation Conference (RusAutoCon), pp. 1-4, IEEE (2018) [Google Scholar]
  7. L.L. Kolesnik, T.S. Zhuleva, P.O. Predtechenskiy, M. Kyaw Hlaing & Z. Phyo Aung, Processing of metallization technology aluminum oxide ceramics for electro-vacuum devices elements and power electronics devices, Journal of Physics Conference Series, Vol. 872, No. 1 (2017) [Google Scholar]
  8. A.B. Syritskii & E.V. Panfilova, Investigation of opal nanostructures using scanning probe microscopy, IOP Conference Series: Materials Science and Engineering, Vol. 443, No. 1, p. 012035, IOP Publishing (2018) [CrossRef] [Google Scholar]
  9. V.L. Kuleshova, E.V. Panfilova & E.P. Prohorov, Automated device for vertical deposition of colloidal opal films, 2018 International Russian Automation Conference (RusAutoCon), pp. 1-5, IEEE (2018) [Google Scholar]
  10. I.E. Allen & J. Seaman, Changing course: Ten years of tracking online education in the United States, Sloan Consortium. PO Box 1238, Newburyport, MA 01950 (2013). [Google Scholar]
  11. S.B. Eom, H.J. Wen & N. Ashill, The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation, Decision Sciences Journal of Innovative Education, 4(2), pp. 215-235 (2006) [CrossRef] [Google Scholar]
  12. J. Feng, T. Liu, L. Zeng, D. Wang & X. Wang, Research and application of grey neural network in equipment life prediction, 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 1990-1994, IEEE (2017) [CrossRef] [Google Scholar]
  13. S. Sapna, A. Tamilarasi & M.P. Kumar, Backpropagation learning algorithm based on Levenberg Marquardt Algorithm, Comp Sci Inform Technol (CS and IT), 2, pp. 393-398 (2012) [Google Scholar]
  14. Statistica Automated Neural Networks, http://statsoft.ru/products/Statistica_Neural_Networks/, last accessed 2019/11/20. [Google Scholar]
  15. Deep Learning Toolbox, https://www.mathworks.com/products/deep-learning.html, last accessed 2019/11/20. [Google Scholar]
  16. NeuroShell 2, http://www.neuroproject.ru/aboutproduct.php?info=ns2info, last accessed 2016/11/21. [Google Scholar]
  17. S. Dreiseitl & L. Ohno-Machado, Logistic regression and artificial neural network classification models: a methodology review, Journal of biomedical informatics, 35(56), pp. 352-359, (2002). [CrossRef] [Google Scholar]
  18. L.V. Oblonsky, V.A. Baskin, Automated method for predicting the reliability of mechanical components of technological equipmen, Electronic equipment. Series 8. Quality management and standardization, Central Scientific Research Institute Electronics, 1(128), pp. 22-25 (1988). [Google Scholar]
  19. E. Armstrong & C. O’Dwyer, Artificial opal photonic crystals and inverse opal structures–fundamentals and applications from optics to energy storage, Journal of Materials Chemistry C, 3(24), pp. 6109-6143 (2015). [CrossRef] [Google Scholar]
  20. G.A. Ozin, K. Hou, B.V. Lotsch, L. Cademartiri, D. P. Puzzo, F. Scotognella, ... & J. Thomson, Nanofabrication by self-assembly, Materials Today, 12(5), pp. 12-23 (2009). [CrossRef] [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.