| Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE X Yuan, B Huang, Y Wang, C Yang, W Gui IEEE Transactions on Industrial Informatics 14 (7), 3235-3243, 2018 | 595 | 2018 |
| Nonlinear dynamic soft sensor modeling with supervised long short-term memory network X Yuan, L Li, Y Wang IEEE transactions on industrial informatics 16 (5), 3168-3176, 2019 | 517 | 2019 |
| Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development X Yuan, L Li, YAW Shardt, Y Wang, C Yang IEEE Transactions on Industrial Electronics 68 (5), 4404-4414, 2020 | 407 | 2020 |
| A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network Y Wang, Z Pan, X Yuan, C Yang, W Gui ISA transactions 96, 457-467, 2020 | 385 | 2020 |
| Hierarchical quality-relevant feature representation for soft sensor modeling: A novel deep learning strategy X Yuan, J Zhou, B Huang, Y Wang, C Yang, W Gui IEEE transactions on industrial informatics 16 (6), 3721-3730, 2019 | 258 | 2019 |
| Weighted linear dynamic system for feature representation and soft sensor application in nonlinear dynamic industrial processes X Yuan, Y Wang, C Yang, Z Ge, Z Song, W Gui IEEE Transactions on Industrial Electronics 65 (2), 1508-1517, 2017 | 174 | 2017 |
| Semisupervised JITL framework for nonlinear industrial soft sensing based on locally semisupervised weighted PCR X Yuan, Z Ge, B Huang, Z Song, Y Wang IEEE Transactions on Industrial Informatics 13 (2), 532-541, 2016 | 173 | 2016 |
| Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes X Yuan, Z Ge, Z Song Industrial & Engineering Chemistry Research 53 (35), 13736-13749, 2014 | 171 | 2014 |
| A layer-wise data augmentation strategy for deep learning networks and its soft sensor application in an industrial hydrocracking process X Yuan, C Ou, Y Wang, C Yang, W Gui IEEE transactions on neural networks and learning systems 32 (8), 3296-3305, 2019 | 148 | 2019 |
| A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data X Yuan, S Qi, Y Wang, H Xia Control Engineering Practice 104, 104614, 2020 | 134 | 2020 |
| Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression X Yuan, Z Ge, Z Song Chemometrics and Intelligent Laboratory Systems 138, 97-109, 2014 | 133 | 2014 |
| A deep supervised learning framework for data-driven soft sensor modeling of industrial processes X Yuan, Y Gu, Y Wang, C Yang, W Gui IEEE transactions on neural networks and learning systems 31 (11), 4737-4746, 2019 | 128 | 2019 |
| A probabilistic just-in-time learning framework for soft sensor development with missing data X Yuan, Z Ge, B Huang, Z Song IEEE Transactions on Control Systems Technology 25 (3), 1124-1132, 2016 | 128 | 2016 |
| Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE X Yuan, C Ou, Y Wang, C Yang, W Gui Neurocomputing 396, 375-382, 2020 | 120 | 2020 |
| Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder Y Wang, H Yang, X Yuan, YAW Shardt, C Yang, W Gui Journal of Process Control 92, 79-89, 2020 | 116 | 2020 |
| Soft sensor modeling of nonlinear industrial processes based on weighted probabilistic projection regression X Yuan, Z Ge, Z Song, Y Wang, C Yang, H Zhang IEEE Transactions on Instrumentation and Measurement 66 (4), 837-845, 2017 | 108 | 2017 |
| Soft sensor model for dynamic processes based on multichannel convolutional neural network X Yuan, S Qi, YAW Shardt, Y Wang, C Yang, W Gui Chemometrics and Intelligent Laboratory Systems 203, 104050, 2020 | 107 | 2020 |
| Deep learning for quality prediction of nonlinear dynamic processes with variable attention‐based long short‐term memory network X Yuan, L Li, Y Wang, C Yang, W Gui The Canadian Journal of Chemical Engineering 98 (6), 1377-1389, 2020 | 104 | 2020 |
| 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 | 96 | 2021 |
| A novel semi-supervised pre-training strategy for deep networks and its application for quality variable prediction in industrial processes X Yuan, C Ou, Y Wang, C Yang, W Gui Chemical Engineering Science 217, 115509, 2020 | 95 | 2020 |
Lingjian YeHuzhou Universityبريد إلكتروني تم التحقق منه على zjhu.edu.cn