Jingxin Zhang
Orcid: 0000-0002-7564-6581Affiliations:
- Chinese Academy of Sciences, Haixi Institutes, Quanzhou Institute of Equipment Manufacturing, Jinjiang, China
- Tsinghua University, Department of Automation, Beijing, China (PhD 2018)
- Harbin Institute of Technology, Research Institute of Intelligent Control and Systems, Harbin, China (former)
According to our database1,
Jingxin Zhang
authored at least 18 papers
between 2014 and 2024.
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Bibliography
2024
Continual Learning-Based Probabilistic Slow Feature Analysis for Monitoring Multimode Nonstationary Processes.
IEEE Trans Autom. Sci. Eng., January, 2024
2023
Monitoring Multimode Nonlinear Dynamic Processes: An Efficient Sparse Dynamic Approach With Continual Learning Ability.
IEEE Trans. Ind. Informatics, July, 2023
IEEE Trans. Ind. Informatics, 2023
Adaptive Cointegration Analysis and Modified RPCA With Continual Learning Ability for Monitoring Multimode Nonstationary Processes.
IEEE Trans. Cybern., 2023
Continual Learning for Multimode Dynamic Process Monitoring With Applications to an Ultra-Supercritical Thermal Power Plant.
IEEE Trans Autom. Sci. Eng., 2023
2022
Structure Parameter Optimized Kernel Based Online Prediction With a Generalized Optimization Strategy for Nonstationary Time Series.
IEEE Trans. Signal Process., 2022
Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring.
CoRR, 2022
2021
Neurocomputing, 2021
Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation.
CoRR, 2021
Proceedings of the CAA Symposium on Fault Detection, 2021
2020
Monitoring multimode processes: a modified PCA algorithm with continual learning ability.
CoRR, 2020
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation.
CoRR, 2020
Multimode process monitoring based on fault dependent variable selection and moving window-negative log likelihood probability.
Comput. Chem. Eng., 2020
2019
An Improved Mixture of Probabilistic PCA for Nonlinear Data-Driven Process Monitoring.
IEEE Trans. Cybern., 2019
Proceedings of the CAA Symposium on Fault Detection, 2019
Proceedings of the CAA Symposium on Fault Detection, 2019
2017
A Data-Driven Learning Approach for Nonlinear Process Monitoring Based on Available Sensing Measurements.
IEEE Trans. Ind. Electron., 2017
2014
Fault diagnosis of the continuous stirred tank heater using fuzzy-possibilistic c-means algorithm.
Proceedings of the 23rd IEEE International Symposium on Industrial Electronics, 2014