Kai Wang
Cited by
Cited by
Deep learning of complex batch process data and its application on quality prediction
K Wang, RB Gopaluni, J Chen, Z Song
IEEE Transactions on Industrial Informatics 16 (12), 7233-7242, 2018
Systematic development of a new variational autoencoder model based on uncertain data for monitoring nonlinear processes
K Wang, MG Forbes, B Gopaluni, J Chen, Z Song
IEEE Access 7, 22554-22565, 2019
Data-driven sensor fault diagnosis systems for linear feedback control loops
K Wang, J Chen, Z Song
Journal of Process Control 54, 152-171, 2017
Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines
W Shao, Z Ge, Z Song, K Wang
Control Engineering Practice 91, 104098, 2019
Performance analysis of dynamic PCA for closed-loop process monitoring and its improvement by output oversampling scheme
K Wang, J Chen, Z Song
IEEE Transactions on Control Systems Technology 27 (1), 378-385, 2017
Sampling-interval-aware LSTM for industrial process soft sensing of dynamic time sequences with irregular sampling measurements
X Yuan, L Li, K Wang, Y Wang
IEEE Sensors Journal 21 (9), 10787-10795, 2021
Data-driven dynamic modeling and online monitoring for multiphase and multimode batch processes with uneven batch durations
K Wang, L Rippon, J Chen, Z Song, RB Gopaluni
Industrial & Engineering Chemistry Research 58 (30), 13628-13641, 2019
Deep learning with neighborhood preserving embedding regularization and its application for soft sensor in an industrial hydrocracking process
C Liu, K Wang, L Ye, Y Wang, X Yuan
Information Sciences 567, 42-57, 2021
Accelerated kernel canonical correlation analysis with fault relevance for nonlinear process fault isolation
J Yu, K Wang, L Ye, Z Song
Industrial & Engineering Chemistry Research 58 (39), 18280-18291, 2019
Fault diagnosis for processes with feedback control loops by shifted output sampling approach
K Wang, J Chen, Z Song
Journal of the Franklin Institute 355 (7), 3249-3273, 2018
Deep learning for data modeling of multirate quality variables in industrial processes
X Yuan, L Feng, K Wang, Y Wang, L Ye
IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2021
Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring
K Wang, X Yuan, J Chen, Y Wang
Neural Networks 136, 54-62, 2021
A sparse loading-based contribution method for multivariate control performance diagnosis
K Wang, J Chen, Z Song
Journal of Process Control 85, 199-213, 2020
Fault detection based on variational autoencoders for complex nonlinear processes
K Wang, J Chen, Z Song
2019 12th Asian Control Conference (ASCC), 1352-1357, 2019
Learning deep multi-manifold structure feature representation for quality prediction with an industrial application
C Liu, K Wang, Y Wang, X Yuan
IEEE Transactions on Industrial Informatics, 2021
Deep neural network-embedded stochastic nonlinear state-space models and their applications to process monitoring
K Wang, J Chen, Z Song, Y Wang, C Yang
IEEE Transactions on Neural Networks and Learning Systems, 2021
Quality variable prediction for nonlinear dynamic industrial processes based on temporal convolutional networks
X Yuan, S Qi, Y Wang, K Wang, C Yang, L Ye
IEEE Sensors Journal 21 (18), 20493-20503, 2021
Concurrent fault detection and anomaly location in closed-loop dynamic systems with measured disturbances
K Wang, J Chen, Z Song
IEEE Transactions on Automation Science and Engineering 16 (3), 1033-1045, 2018
A SIA-LSTM based virtual metrology for quality variables in irregular sampled time sequence of industrial processes
X Yuan, Z Jia, L Li, K Wang, L Ye, Y Wang, C Yang, W Gui
Chemical Engineering Science 249, 117299, 2022
Deep learning with nonlocal and local structure preserving Stacked Autoencoder for soft sensor in industrial processes
C Liu, Y Wang, K Wang, X Yuan
Engineering Applications of Artificial Intelligence 104, 104341, 2021
The system can't perform the operation now. Try again later.
Articles 1–20