Abstract
Original language | English |
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Article number | e17703 |
Number of pages | 14 |
Journal | AICHE Journal |
Volume | 68 |
Issue number | 6 |
Early online date | 30 Mar 2022 |
DOIs | |
Publication status | Published - Jun 2022 |
Externally published | Yes |
Funding
Financial support for this work from a General Research Fund by RGC of Hong Kong (No. 11303421), Dimension reduction modeling methods for high dimensional dynamic data in smart manufacturing and operations, the Natural Science Foundation of China grant (U20A20189), Big data-driven abnormal situation intelligent diagnosis and self-healing control for process industries, and the City University of Hong Kong Project (9380123), SGP: Bridging between systems theory and dynamic data learning toward industrial intelligence and industry 4.0 is gratefully acknowledged. The author appreciates the helpful discussions with Prof. Alain Bensoussan at the City University of Hong Kong and Prof. Yingying Fan at the University of Southern California.
Keywords
- dynamic latent variable learning
- latent system modeling
- plant-wide feature analysis
- predictable latent time series
- profile likelihood