Jie Liu
Orcid: 0000-0003-0895-7598Affiliations:
- University of Paris-Saclay, CentraleSupélec, Chatenay-Malabry, France
According to our database1,
Jie Liu
authored at least 34 papers
between 2015 and 2024.
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Bibliography
2024
Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths.
Reliab. Eng. Syst. Saf., March, 2024
IEEE Trans. Ind. Informatics, February, 2024
Empirical study on fine-tuning pre-trained large language models for fault diagnosis of complex systems.
Reliab. Eng. Syst. Saf., 2024
2023
Common stochastic effects induced multivariate degradation process with temporal dependency in degradation characteristic and unit dimensions.
Reliab. Eng. Syst. Saf., November, 2023
Causal Graph Attention Network with Disentangled Representations for Complex Systems Fault Detection.
Reliab. Eng. Syst. Saf., July, 2023
Fault diagnosis for high-speed train braking system based on disentangled causal representation learning.
Expert Syst. J. Knowl. Eng., March, 2023
Deep adversarial learning system for fault diagnosis in fused deposition modeling with imbalanced data.
Comput. Ind. Eng., February, 2023
Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning.
Reliab. Eng. Syst. Saf., 2023
2022
Importance-SMOTE: a synthetic minority oversampling method for noisy imbalanced data.
Soft Comput., 2022
Reliab. Eng. Syst. Saf., 2022
T-Friedman Test: A New Statistical Test for Multiple Comparison with an Adjustable Conservativeness Measure.
Int. J. Comput. Intell. Syst., 2022
Robust state-of-charge estimation of Li-ion batteries based on multichannel convolutional and bidirectional recurrent neural networks.
Appl. Soft Comput., 2022
2021
Fuzzy Sets Syst., 2021
A minority oversampling approach for fault detection with heterogeneous imbalanced data.
Expert Syst. Appl., 2021
High-speed train fault detection with unsupervised causality-based feature extraction methods.
Adv. Eng. Informatics, 2021
An Efficient Anomaly Detection for High-Speed Train Braking System Using Broad Learning System.
IEEE Access, 2021
2020
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-series data of NPP steam generators.
Appl. Soft Comput., 2020
IEEE Access, 2020
First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data.
IEEE Access, 2020
2019
Reliab. Eng. Syst. Saf., 2019
Integration of feature vector selection and support vector machine for classification of imbalanced data.
Appl. Soft Comput., 2019
2018
A scalable fuzzy support vector machine for fault detection in transportation systems.
Expert Syst. Appl., 2018
Particle Filtering for Prognostics of a Newly Designed Product With a New Parameters Initialization Strategy Based on Reliability Test Data.
IEEE Access, 2018
Proceedings of the 3rd International Conference on System Reliability and Safety, 2018
Proceedings of the 2018 IEEE International Conference on Prognostics and Health Management, 2018
A Bayesian Network Approach for Imbalanced Fault Detection in High Speed Rail Systems.
Proceedings of the 2018 IEEE International Conference on Prognostics and Health Management, 2018
2017
Weighted-feature and cost-sensitive regression model for component continuous degradation assessment.
Reliab. Eng. Syst. Saf., 2017
Reliab. Eng. Syst. Saf., 2017
Neural Comput. Appl., 2017
Proceedings of the 2nd International Conference on System Reliability and Safety, 2017
2016
Feature vector regression with efficient hyperparameters tuning and geometric interpretation.
Neurocomputing, 2016
Appl. Soft Comput., 2016
2015
A Novel Dynamic-Weighted Probabilistic Support Vector Regression-Based Ensemble for Prognostics of Time Series Data.
IEEE Trans. Reliab., 2015