| 1 |
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 |
| 2 |
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 |
| 3 |
Using multivariate pattern segmentation to assess process performance and mine good operation conditions for dynamic chemical industry
Wang K, Chen JH, Song ZH
Chemical Engineering Science, 201, 339, 2019 |
| 4 |
Advances and opportunities in machine learning for process data analytics
Qin SJ, Chiang LH
Computers & Chemical Engineering, 126, 465, 2019 |
| 5 |
Challenges and opportunities in biopharmaceutical manufacturing control
Hong MS, Severson KA, Jiang M, Lu AE, Love JC, Braatz RD
Computers & Chemical Engineering, 110, 106, 2018 |
| 6 |
Fault detection and diagnosis using empirical mode decomposition based principal component analysis
Du YC, Du DP
Computers & Chemical Engineering, 115, 1, 2018 |
| 7 |
Framework for a smart data analytics platform towards process monitoring and alarm management
Hu WK, Shah SL, Chen TW
Computers & Chemical Engineering, 114, 225, 2018 |
| 8 |
Mixed-effects Gaussian process modeling approach with application in injection molding processes
Luo LK, Yao Y, Gao FR, Zhao CH
Journal of Process Control, 62, 37, 2018 |
| 9 |
Study on missing data imputation and modeling for the leaching process
He DK, Wang ZS, Yang L, Dai WW
Chemical Engineering Research & Design, 124, 1, 2017 |
| 10 |
Process control of time-varying systems using parameter-less self-organizing maps
Choung YJ, Kang J, Kim SB
Journal of Process Control, 52, 45, 2017 |
