Open Access
Issue |
E3S Web Conf.
Volume 244, 2021
XXII International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies (EMMFT-2020)
|
|
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Article Number | 11001 | |
Number of page(s) | 11 | |
Section | Energy Management and Policy | |
DOI | https://doi.org/10.1051/e3sconf/202124411001 | |
Published online | 19 March 2021 |
- S. Barkalov, P. Kurochka, T. Nasonova, Optimal placement of maintenance facilities MATEC Web of Conferences conference proceedings, 01124 (2018) [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- B. Hakan, V. Andrea, Integrated perception with recurrent multi-task neural networks. In Advances in neural information processing systems, 235–243 (2016) [Google Scholar]
- 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]
- S. Lambert-Lacroix, L. Zwald, “Robust regression through the Hubers criterion and adaptive lasso penalty,” Electron. J. Statist., 5, 1015–1053 (2011) [Google Scholar]
- 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]
- M. Martinez, R. Stiefelhagen, Taming the Cross Entropy Loss. Computer Science, Mathematics, 3 (2018) [Google Scholar]
- 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]
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