| A SVM framework for fault detection of the braking system in a high speed train J Liu, YF Li, E Zio Mechanical Systems and Signal Processing 87, 401-409, 2017 | 129 | 2017 |
| A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time … HP Nguyen, J Liu, E Zio Applied Soft Computing 89, 106116, 2020 | 114 | 2020 |
| Nuclear power plant components condition monitoring by probabilistic support vector machine J Liu, R Seraoui, V Vitelli, E Zio Annals of Nuclear Energy 56, 23-33, 2013 | 100 | 2013 |
| A novel dynamic-weighted probabilistic support vector regression-based ensemble for prognostics of time series data J Liu, V Vitelli, E Zio, R Seraoui IEEE Transactions on Reliability 64 (4), 1203-1213, 2015 | 79 | 2015 |
| An adaptive online learning approach for Support Vector Regression: Online-SVR-FID J Liu, E Zio Mechanical Systems and Signal Processing 76, 796-809, 2016 | 64 | 2016 |
| System dynamic reliability assessment and failure prognostics J Liu, E Zio Reliability Engineering & System Safety 160, 21-36, 2017 | 62 | 2017 |
| Reinforcement learning-driven maintenance strategy: A novel solution for long-term aircraft maintenance decision optimization Y Hu, X Miao, J Zhang, J Liu, E Pan Computers & industrial engineering 153, 107056, 2021 | 58 | 2021 |
| A scalable fuzzy support vector machine for fault detection in transportation systems J Liu, E Zio Expert Systems with Applications 102, 36-43, 2018 | 50 | 2018 |
| Integration of feature vector selection and support vector machine for classification of imbalanced data J Liu, E Zio Applied Soft Computing 75, 702-711, 2019 | 49 | 2019 |
| SVM hyperparameters tuning for recursive multi-step-ahead prediction J Liu, E Zio Neural Computing and Applications, 1-15, 2016 | 44 | 2016 |
| Fuzzy support vector machine for imbalanced data with borderline noise J Liu Fuzzy sets and systems 413, 64-73, 2021 | 42 | 2021 |
| Degradation state mining and identification for railway point machines C Bian, S Yang, T Huang, Q Xu, J Liu, E Zio Reliability Engineering & System Safety 188, 432-443, 2019 | 40 | 2019 |
| A SVM-based framework for fault detection in high-speed trains J Liu, Y Hu, S Yang Measurement 172, 108779, 2021 | 33 | 2021 |
| High-speed train fault detection with unsupervised causality-based feature extraction methods Y Xu, J Liu Advanced Engineering Informatics 49, 101312, 2021 | 31 | 2021 |
| T-Friedman test: a new statistical test for multiple comparison with an adjustable conservativeness measure J Liu, Y Xu International Journal of Computational Intelligence Systems 15 (1), 29, 2022 | 26 | 2022 |
| A modified generative adversarial network for fault diagnosis in high-speed train components with imbalanced and heterogeneous monitoring data C Wang, J Liu, E Zio Journal of Dynamics, Monitoring and Diagnostics, 84-92, 2022 | 24 | 2022 |
| Importance-SMOTE: a synthetic minority oversampling method for noisy imbalanced data J Liu Soft Computing 26 (3), 1141-1163, 2022 | 22 | 2022 |
| A minority oversampling approach for fault detection with heterogeneous imbalanced data J Liu Expert Systems with Applications 184, 115492, 2021 | 19 | 2021 |
| Fault information mining with causal network for railway transportation system J Liu, Y Xu, L Wang Reliability Engineering & System Safety 220, 108281, 2022 | 18 | 2022 |
| A SVR-based ensemble approach for drifting data streams with recurring patterns J Liu, E Zio Applied Soft Computing 47, 553-564, 2016 | 18 | 2016 |
Enrico ZioProfessor Mines Paris/PSL and Politecnico di. Milano在 mines-paristech.fr 的电子邮件经过验证
