| 1 |
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 |
| 2 |
A review of the Expectation Maximization algorithm in data-driven process identification
Sammaknejad N, Zhao YJ, Huang B
Journal of Process Control, 73, 123, 2019 |
| 3 |
Extracting dynamic features with switching models for process data analytics and application in soft sensing
Ma YJ, Huang B
AIChE Journal, 64(6), 2037, 2018 |
| 4 |
Process monitoring using a generalized probabilistic linear latent variable model
Raveendran R, Kodamana H, Huang B
Automatica, 96, 73, 2018 |
| 5 |
Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis
Shang C, Yang F, Gao XQ, Huang XL, Suykens JAK, Huang DX
AIChE Journal, 61(11), 3666, 2015 |
| 6 |
Multivariate characterization, modeling, and design of ionic liquid molecules
Hada S, Herring RH, Davis SE, Eden MR
Computers & Chemical Engineering, 81, 310, 2015 |
| 7 |
Robust semi-supervised mixture probabilistic principal component regression model development and application to soft sensors
Zhu JL, Ge ZQ, Song ZH
Journal of Process Control, 32, 25, 2015 |
| 8 |
Designing multi-responsive polymers using latent variable methods
Torres JMGT, Nichols E, MacGregor JF, Hoare T
Polymer, 55(2), 505, 2014 |
| 9 |
Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods
MacGregor J, Cinar A
Computers & Chemical Engineering, 47, 111, 2012 |
| 10 |
Latent Variable Model Predictive Control (LV-MPC) for trajectory tracking in batch processes
Golshan M, MacGregor JF, Bruwer MJ, Mhaskar P
Journal of Process Control, 20(4), 538, 2010 |
