Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models W Shao, X Tian Chemical Engineering Research and Design 95, 113-132, 2015 | 145 | 2015 |
Online soft sensor design using local partial least squares models with adaptive process state partition W Shao, X Tian, P Wang, X Deng, S Chen Chemometrics and Intelligent Laboratory Systems 144, 108-121, 2015 | 76 | 2015 |
Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development W Shao, X Tian Neurocomputing 222, 91-104, 2017 | 65 | 2017 |
Soft-sensor development for processes with multiple operating modes based on semisupervised Gaussian mixture regression W Shao, Z Ge, Z Song IEEE Transactions on Control Systems Technology 27 (5), 2169-2181, 2018 | 53 | 2018 |
Semisupervised Robust Modeling of Multimode Industrial Processes for Quality Variable Prediction Based on Student's t Mixture Model W Shao, Z Ge, Z Song, J Wang IEEE Transactions on Industrial Informatics 16 (5), 2965-2976, 2019 | 48 | 2019 |
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 | 47 | 2018 |
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 chemical processes based on semisupervised Dirichlet process mixture of Gaussians W Shao, Z Ge, Z Song Chemical Engineering Science 193, 394-410, 2019 | 44 | 2019 |
Bayesian just-in-time learning and its application to industrial soft sensing W Shao, Z Ge, Z Song IEEE Transactions on Industrial Informatics 16 (4), 2787-2798, 2019 | 39 | 2019 |
Adaptive soft sensor development for multi-output industrial processes based on selective ensemble learning W Shao, S Chen, CJ Harris IEEE Access 6, 55628-55642, 2018 | 33 | 2018 |
Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division W Shao, X Tian, P Wang Chinese Journal of Chemical Engineering 22 (7), 828-836, 2014 | 29 | 2014 |
Semisupervised Bayesian Gaussian mixture models for non-Gaussian soft sensor W Shao, Z Ge, Z Song IEEE Transactions on Cybernetics 51 (7), 3455-3468, 2019 | 28 | 2019 |
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 | 27 | 2019 |
Semi-supervised mixture of latent factor analysis models with application to online key variable estimation W Shao, Z Ge, Z Song Control Engineering Practice 84, 32-47, 2019 | 27 | 2019 |
Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor W Shao, X Tian, P Wang Chinese Journal of Chemical Engineering 23 (12), 1925-1934, 2015 | 26 | 2015 |
Autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development K Liu, W Shao, G Chen ISA transactions 103, 143-155, 2020 | 23 | 2020 |
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 | 22 | 2019 |
Plastic bag model of the artificial gas lift system for slug flow analysis W Shao, I Boiko, A Al-Durra Journal of Natural Gas Science and Engineering 33, 573-586, 2016 | 21 | 2016 |
Control-oriented modeling of gas-lift system and analysis of casing-heading instability W Shao, I Boiko, A Al-Durra Journal of Natural Gas Science and Engineering 29, 365-381, 2016 | 20 | 2016 |
Semi-supervised variational Bayesian Student’st mixture regression and robust inferential sensor application J Wang, W Shao, Z Song Control engineering practice 92, 104155, 2019 | 18 | 2019 |