| 1 |
Bayesian network for dynamic variable structure learning and transfer modeling of probabilistic soft sensor
Zeng L, Ge ZQ
Journal of Process Control, 100, 20, 2021 |
| 2 |
Approximate Controllability and Approximate Observability of Singular Distributed Parameter Systems
Ge ZQ, Ge XC, Zhang JF
IEEE Transactions on Automatic Control, 65(5), 2294, 2020 |
| 3 |
Refining data-driven soft sensor modeling framework with variable time reconstruction
Yao L, Ge ZQ
Journal of Process Control, 87, 91, 2020 |
| 4 |
Streaming parallel variational Bayesian supervised factor analysis for adaptive soft sensor modeling with big process data
Yang ZY, Yao L, Ge ZQ
Journal of Process Control, 85, 52, 2020 |
| 5 |
Monitoring and prediction of big process data with deep latent variable models and parallel computing
Yang ZY, Ge ZQ
Journal of Process Control, 92, 19, 2020 |
| 6 |
Gaussian Discriminative Analysis aided GAN for imbalanced big data augmentation and fault classification
Zhuo Y, Ge ZQ
Journal of Process Control, 92, 271, 2020 |
| 7 |
Quality variable prediction for chemical processes based on semisupervised Dirichlet process mixture of Gaussians
Shao WM, Ge ZQ, Song ZH
Chemical Engineering Science, 193, 394, 2019 |
| 8 |
Scalable learning and probabilistic analytics of industrial big data based on parameter server: Framework, methods and applications
Yao L, Ge ZQ
Journal of Process Control, 78, 13, 2019 |
| 9 |
K-means Bayes algorithm for imbalanced fault classification and big data application
Chen GC, Liu Y, Ge ZQ
Journal of Process Control, 81, 54, 2019 |
| 10 |
Dynamic mutual information similarity based transient process identification and fault detection
He YC, Zhou L, Ge ZQ, Song ZH
Canadian Journal of Chemical Engineering, 96(7), 1541, 2018 |
