Jingxin Zhang

Orcid: 0000-0002-7564-6581

Affiliations:
  • 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 20 papers between 2014 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
An efficient multimodal attentional principal component analysis for continual learning-based dynamic process monitoring.
Neurocomputing, 2025

2024
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-Hoc Interpretable Fault Diagnosis With Limited Fault Samples.
IEEE Trans. Neural Networks Learn. Syst., May, 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

Self-Learning Sparse PCA for Multimode Process Monitoring.
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
Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings.
Neurocomputing, 2021

Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation.
CoRR, 2021

Sparse DiPCA for dynamic process monitoring.
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

Dynamic Laplacian eigenmaps for process monitoring.
Proceedings of the CAA Symposium on Fault Detection, 2019

A Non-Greedy Algorithm Based L1-Norm LDA Method for Fault Detection.
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


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