Open Access
E3S Web Conf.
Volume 244, 2021
XXII International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies (EMMFT-2020)
Article Number 11001
Number of page(s) 11
Section Energy Management and Policy
Published online 19 March 2021
  1. S. Barkalov, P. Kurochka, T. Nasonova, Optimal placement of maintenance facilities MATEC Web of Conferences conference proceedings, 01124 (2018) [Google Scholar]
  2. C. Zheng, W. Liu, B. Chen, D. Gao, Y. Cheng, Y. Yang, J. Peng, A Data-driven Approach for Remaining Useful Life Prediction of Aircraft Engines. In Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4-7 November, 184-189 (2018) [Google Scholar]
  3. Z. Chen, S. Cao, Z. Mao, Remaining useful life estimation of aircraft engines using a modified similarity and supporting vector machine (SVM) approach. Energies, 11, 28 (2018) [Google Scholar]
  4. J. B. Ali, B. Chebel-Morello, L. Saidi, S. Malinowski, Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mech. Syst. Signal Process, 1–23 (2014) [Google Scholar]
  5. A. Al-Dulaimi, S. Zabihi, A. Asif, A. Mohammadi, A multimodal and hybrid deep neural network model for Remaining Useful Life estimation. Comput. Ind., 108, 186–96 (2019) [Google Scholar]
  6. C. Zhao, V. Badrinarayanan, C.-Y. Lee, A. Rabinovich, Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In Proceedings of the International Conference on Machine Learning (2018) [Google Scholar]
  7. A. Kendall, Y. Gal, R. Cipolla, Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7482–7491 (2018) [Google Scholar]
  8. A.R. Zamir, A. Sax, W. Shen, L.J. Guibas, J. Malik, S. Savarese, Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3712–3722 (2018) [Google Scholar]
  9. Z. Zhanpeng, L. Ping, C. L. Chen, T. Xiaoou, Facial landmark detection by deep multitask learning. In European conference on computer vision, 94–108 Springer (2014) [Google Scholar]
  10. V. E. Belousov, P. N. Kurochka, T. A. Averina, Algorithms of a logical conclusion of knowledge in difficult technical systems on the basis of indistinct rules. 11th IEEE International Conference on Application of Information and Communication Technologies, AICT 2017 - Proceedings, 11, 8687040 (2019) [Google Scholar]
  11. D. K. Frederick, J. A. DeCastro, Litt, J.S. User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS); NASA Glenn Research Center (Cleveland, OH, USA, 2007) [Google Scholar]
  12. A. Saxena, K. Goebel, Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository (https://tiarcnasagov/tech/dash/groups/pcoe/prognostic-data-repository/), NASA Ames Research Center, Moffett Field, CA (2008) [Google Scholar]
  13. L. Wen, Y. Dong, L. Gao, A new ensemble residual convolutional neural network for remaining useful life estimation. Math. Biosci. Eng., 16, 862–880 (2019) [PubMed] [Google Scholar]
  14. B. Hakan, V. Andrea, Integrated perception with recurrent multi-task neural networks. In Advances in neural information processing systems, 235–243 (2016) [Google Scholar]
  15. X. Li, Q. Ding, J. Q. Sun, Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab. Eng. Syst. Saf., 172, 1–11 (2018) [Google Scholar]
  16. S. Lambert-Lacroix, L. Zwald, “Robust regression through the Hubers criterion and adaptive lasso penalty,” Electron. J. Statist., 5, 1015–1053 (2011) [Google Scholar]
  17. A. Bhardwaj, W. Di, J. Wei, Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling. Birmingham: Packt Publishing Limited (2018) [Google Scholar]
  18. M. Martinez, R. Stiefelhagen, Taming the Cross Entropy Loss. Computer Science, Mathematics, 3 (2018) [Google Scholar]
  19. J. T. Barron, A General and Adaptive Robust Loss Function. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2019) [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.