Kai Wang
Orcid: 0000-0003-1396-9825Affiliations:
- Central South University, School of Automation, Changsha, China
- Zhejiang University, State Key Laboratory of Industrial Control Technology, Hangzhou, China (PhD 2017)
According to our database1,
Kai Wang
authored at least 38 papers
between 2018 and 2024.
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Bibliography
2024
Attention-Based Interval Aided Networks for Data Modeling of Heterogeneous Sampling Sequences With Missing Values in Process Industry.
IEEE Trans. Ind. Informatics, April, 2024
Blackout Missing Data Recovery in Industrial Time Series Based on Masked-Former Hierarchical Imputation Framework.
IEEE Trans Autom. Sci. Eng., April, 2024
Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process.
IEEE Trans. Cybern., February, 2024
Interpretable Prediction Modeling for Froth Flotation via Stacked Graph Convolutional Network.
IEEE Trans. Artif. Intell., January, 2024
Variable Correlation Analysis-Based Convolutional Neural Network for Far Topological Feature Extraction and Industrial Predictive Modeling.
IEEE Trans. Instrum. Meas., 2024
Machine learning for industrial sensing and control: A survey and practical perspective.
CoRR, 2024
2023
Neuron-Compressed Deep Neural Network and Its Application in Industrial Anomaly Detection.
IEEE Trans. Ind. Informatics, July, 2023
Semi-supervised deep embedded clustering with pairwise constraints and subset allocation.
Neural Networks, July, 2023
Inf. Sci., June, 2023
Imputation of Missing Values in Time Series Using an Adaptive-Learned Median-Filled Deep Autoencoder.
IEEE Trans. Cybern., 2023
Semi-supervised LSTM with historical feature fusion attention for temporal sequence dynamic modeling in industrial processes.
Eng. Appl. Artif. Intell., 2023
Promoting Decision-Making in Industrial Flotation Process by Collaborating Multiple Flotation Cells.
Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society, 2023
Communication and Self-Learning Strategies Incorporated State Transition Algorithm for Optimization of Complex Systems.
Proceedings of the CAA Symposium on Fault Detection, 2023
2022
Deep Neural Network-Embedded Stochastic Nonlinear State-Space Models and Their Applications to Process Monitoring.
IEEE Trans. Neural Networks Learn. Syst., 2022
Layer-Wise Residual-Guided Feature Learning With Deep Learning Networks for Industrial Quality Prediction.
IEEE Trans. Instrum. Meas., 2022
Learning Deep Multimanifold Structure Feature Representation for Quality Prediction With an Industrial Application.
IEEE Trans. Ind. Informatics, 2022
A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material.
Sensors, 2022
A reduced nonstationary discrete convolution kernel for multimode process monitoring.
Int. J. Mach. Learn. Cybern., 2022
New mode cold start monitoring in industrial processes: A solution of spatial-temporal feature transfer.
Knowl. Based Syst., 2022
Online reconstruction and diagnosibility analysis of multiplicative fault models for process-related faults.
J. Frankl. Inst., 2022
Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling.
Adv. Eng. Informatics, 2022
A multi-source transfer learning method for new mode monitoring in industrial processes.
Proceedings of the 8th International Conference on Control, 2022
2021
Deep Learning for Data Modeling of Multirate Quality Variables in Industrial Processes.
IEEE Trans. Instrum. Meas., 2021
Deep Nonlinear Dynamic Feature Extraction for Quality Prediction Based on Spatiotemporal Neighborhood Preserving SAE.
IEEE Trans. Instrum. Meas., 2021
Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring.
Neural Networks, 2021
Deep learning with neighborhood preserving embedding regularization and its application for soft sensor in an industrial hydrocracking process.
Inf. Sci., 2021
Common and specific deep feature representation for multimode process monitoring using a novel variable-wise weighted parallel network.
Eng. Appl. Artif. Intell., 2021
Deep learning with nonlocal and local structure preserving stacked autoencoder for soft sensor in industrial processes.
Eng. Appl. Artif. Intell., 2021
An online operating performance evaluation approach using probabilistic fuzzy theory for chemical processes with uncertainties.
Comput. Chem. Eng., 2021
A fault reconstruction strategy for fault diagnosis of state-related multiplicative faults.
Proceedings of the CAA Symposium on Fault Detection, 2021
Quality-Sensitive Feature Extraction for End Product Quality Prediction in Injection Molding Processes.
Proceedings of the Big Data - 9th CCF Conference, 2021
2020
Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction.
IEEE Trans. Ind. Informatics, 2020
2019
Performance Analysis of Dynamic PCA for Closed-Loop Process Monitoring and Its Improvement by Output Oversampling Scheme.
IEEE Trans. Control. Syst. Technol., 2019
Concurrent Fault Detection and Anomaly Location in Closed-Loop Dynamic Systems With Measured Disturbances.
IEEE Trans Autom. Sci. Eng., 2019
Systematic Development of a New Variational Autoencoder Model Based on Uncertain Data for Monitoring Nonlinear Processes.
IEEE Access, 2019
Proceedings of the 12th Asian Control Conference, 2019
2018
A new excitation scheme for closed-loop subspace identification using additional sampling outputs and its extension to instrumental variable method.
J. Frankl. Inst., 2018
Fault diagnosis for processes with feedback control loops by shifted output sampling approach.
J. Frankl. Inst., 2018