| Learning deep multimanifold structure feature representation for quality prediction with an industrial application C Liu, K Wang, Y Wang, X Yuan IEEE Transactions on Industrial Informatics 18 (9), 5849-5858, 2021 | 85 | 2021 |
| 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 | 83 | 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 | 56 | 2019 |
| 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 | 46 | 2019 |
| 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 | 44 | 2021 |
| 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 | 44 | 2021 |
| 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 | 41 | 2017 |
| 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 | 35 | 2021 |
| 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 | 32 | 2021 |
| Imputation of missing values in time series using an adaptive-learned median-filled deep autoencoder Z Pan, Y Wang, K Wang, H Chen, C Yang, W Gui IEEE Transactions on Cybernetics 53 (2), 695-706, 2022 | 29 | 2022 |
| 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 | 29 | 2022 |
| 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 | 29 | 2021 |
| Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling Y Wang, D Liu, C Liu, X Yuan, K Wang, C Yang Advanced Engineering Informatics 52, 101590, 2022 | 28 | 2022 |
| Multiscale feature fusion and semi-supervised temporal-spatial learning for performance monitoring in the flotation industrial process Y Wang, S Li, C Liu, K Wang, X Yuan, C Yang, W Gui IEEE Transactions on Cybernetics, 2023 | 26 | 2023 |
| 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 | 26 | 2017 |
| 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 | 25 | 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 | 22 | 2019 |
| 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 33 (12), 7682-7694, 2021 | 20 | 2021 |
| Blackout missing data recovery in industrial time series based on masked-former hierarchical imputation framework D Liu, Y Wang, C Liu, K Wang, X Yuan, C Yang IEEE Transactions on Automation Science and Engineering, 2023 | 18 | 2023 |
| Semi-supervised LSTM with historical feature fusion attention for temporal sequence dynamic modeling in industrial processes Y Tang, Y Wang, C Liu, X Yuan, K Wang, C Yang Engineering Applications of Artificial Intelligence 117, 105547, 2023 | 18 | 2023 |
Xiaofeng YuanCentral South UniversityVerified email at csu.edu.cn
