| Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application L Yao, Z Ge IEEE Transactions on Industrial Electronics 65 (2), 1490-1498, 2017 | 296 | 2017 |
| Big data quality prediction in the process industry: A distributed parallel modeling framework L Yao, Z Ge Journal of Process Control 68, 1-13, 2018 | 108 | 2018 |
| Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure B Shen, L Yao, Z Ge Control Engineering Practice 94, 104198, 2020 | 100 | 2020 |
| Scalable semisupervised GMM for big data quality prediction in multimode processes L Yao, Z Ge IEEE Transactions on Industrial Electronics 66 (5), 3681-3692, 2018 | 90 | 2018 |
| Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data L Yao, Z Ge IEEE Transactions on Automation Science and Engineering 14 (1), 126-138, 2016 | 80 | 2016 |
| Distributed parallel deep learning of hierarchical extreme learning machine for multimode quality prediction with big process data L Yao, Z Ge Engineering Applications of Artificial Intelligence 81, 450-465, 2019 | 64 | 2019 |
| Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis L Yao, Z Ge Control Engineering Practice 61, 72-80, 2017 | 62 | 2017 |
| Cooperative deep dynamic feature extraction and variable time-delay estimation for industrial quality prediction L Yao, Z Ge IEEE Transactions on Industrial Informatics 17 (6), 3782-3792, 2020 | 61 | 2020 |
| Online updating soft sensor modeling and industrial application based on selectively integrated moving window approach L Yao, Z Ge IEEE Transactions on Instrumentation and Measurement 66 (8), 1985-1993, 2017 | 61 | 2017 |
| Parallel computing and SGD-based DPMM for soft sensor development with large-scale semisupervised data W Shao, L Yao, Z Ge, Z Song IEEE Transactions on Industrial Electronics 66 (8), 6362-6373, 2018 | 53 | 2018 |
| Industrial big data modeling and monitoring framework for plant-wide processes L Yao, Z Ge IEEE Transactions on Industrial Informatics 17 (9), 6399-6408, 2020 | 49 | 2020 |
| Virtual sensing f-CaO content of cement clinker based on incremental deep dynamic features extracting and transferring model L Yao, X Jiang, G Huang, J Qian, B Shen, L Xu, Z Ge IEEE Transactions on Instrumentation and Measurement 70, 1-10, 2020 | 47 | 2020 |
| Refining data-driven soft sensor modeling framework with variable time reconstruction L Yao, Z Ge Journal of Process Control 87, 91-107, 2020 | 46 | 2020 |
| Attention-based feature fusion generative adversarial network for yarn-dyed fabric defect detection H Zhang, G Qiao, S Lu, L Yao, X Chen Textile Research Journal 93 (5-6), 1178-1195, 2023 | 44 | 2023 |
| Predictive modeling with multiresolution pyramid VAE and industrial soft sensor applications B Shen, L Yao, Z Ge IEEE Transactions on Cybernetics 53 (8), 4867-4879, 2022 | 40 | 2022 |
| Semi-supervised deep dynamic probabilistic latent variable model for multimode process soft sensor application L Yao, B Shen, L Cui, J Zheng, Z Ge IEEE Transactions on Industrial Informatics 19 (4), 6056-6068, 2022 | 37 | 2022 |
| Hierarchical quality monitoring for large-scale industrial plants with big process data L Yao, W Shao, Z Ge IEEE Transactions on Neural Networks and Learning Systems 32 (8), 3330-3341, 2019 | 37 | 2019 |
| Causal variable selection for industrial process quality prediction via attention-based GRU network L Yao, Z Ge Engineering Applications of Artificial Intelligence 118, 105658, 2023 | 36 | 2023 |
| Dynamic features incorporated locally weighted deep learning model for soft sensor development L Yao, Z Ge IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2021 | 36 | 2021 |
| Bayesian nonlinear Gaussian mixture regression and its application to virtual sensing for multimode industrial processes W Shao, Z Ge, L Yao, Z Song IEEE Transactions on Automation Science and Engineering 17 (2), 871-885, 2019 | 33 | 2019 |
Zhiqiang GeZhejiang University, Peng Cheng Laboratory, Southeast UniversityEmail được xác minh tại seu.edu.cn
