| Review of recent research on data-based process monitoring Z Ge, Z Song, F Gao Industrial & Engineering Chemistry Research 52 (10), 3543-3562, 2013 | 1081 | 2013 |
| Data mining and analytics in the process industry: The role of machine learning Z Ge, Z Song, SX Ding, B Huang Ieee Access 5, 20590-20616, 2017 | 990 | 2017 |
| Review on data-driven modeling and monitoring for plant-wide industrial processes Z Ge Chemometrics and Intelligent Laboratory Systems 171, 16-25, 2017 | 608 | 2017 |
| A survey on deep learning for data-driven soft sensors Q Sun, Z Ge IEEE Transactions on Industrial Informatics 17 (9), 5853-5866, 2021 | 392 | 2021 |
| Process monitoring based on independent component analysis− principal component analysis (ICA− PCA) and similarity factors Z Ge, Z Song Industrial & Engineering Chemistry Research 46 (7), 2054-2063, 2007 | 355 | 2007 |
| 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, 2018 | 291 | 2018 |
| Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data J Zhu, Z Ge, Z Song, F Gao Annual Reviews in Control 46, 107-133, 2018 | 291 | 2018 |
| Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data J Zhu, Z Ge, Z Song IEEE Transactions on Industrial Informatics 13 (4), 1877-1885, 2017 | 278 | 2017 |
| Distributed PCA model for plant-wide process monitoring Z Ge, Z Song Industrial & engineering chemistry research 52 (5), 1947-1957, 2013 | 258 | 2013 |
| Improved kernel PCA-based monitoring approach for nonlinear processes Z Ge, C Yang, Z Song Chemical Engineering Science 64 (9), 2245-2255, 2009 | 234 | 2009 |
| A comparative study of just-in-time-learning based methods for online soft sensor modeling Z Ge, Z Song Chemometrics and Intelligent Laboratory Systems 104 (2), 306-317, 2010 | 224 | 2010 |
| Process data analytics via probabilistic latent variable models: A tutorial review Z Ge Industrial & Engineering Chemistry Research 57 (38), 12646-12661, 2018 | 214 | 2018 |
| Online monitoring of nonlinear multiple mode processes based on adaptive local model approach Z Ge, Z Song Control Engineering Practice 16 (12), 1427-1437, 2008 | 196 | 2008 |
| Global–local structure analysis model and its application for fault detection and identification M Zhang, Z Ge, Z Song, R Fu Industrial & Engineering Chemistry Research 50 (11), 6837-6848, 2011 | 183 | 2011 |
| Nonlinear process monitoring based on linear subspace and Bayesian inference Z Ge, M Zhang, Z Song Journal of Process Control 20 (5), 676-688, 2010 | 183 | 2010 |
| Mixture Bayesian regularization method of PPCA for multimode process monitoring Z Ge, Z Song AIChE journal 56 (11), 2838-2849, 2010 | 182 | 2010 |
| Multimode process monitoring based on Bayesian method Z Ge, Z Song Journal of Chemometrics: A Journal of the Chemometrics Society 23 (12), 636-650, 2009 | 171 | 2009 |
| 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 | 169 | 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 | 168 | 2014 |
| 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 | 167 | 2017 |
Song ZhihuanProfessor of Control Science & Engineering, Zhejiang UniversityVerified email at zju.edu.cn
