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Fan, Lei, Kodamana, Hariprasad, Huang, Biao (2019) Semi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach. AIChE Journal, 65 (3). 964-979 doi:10.1002/aic.16481

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Reference TypeJournal (article/letter/editorial)
TitleSemi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach
JournalAIChE Journal
AuthorsFan, LeiAuthor
Kodamana, HariprasadAuthor
Huang, BiaoAuthor
Year2019 (March)Volume65
Issue3
PublisherWiley
DOIdoi:10.1002/aic.16481Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID6296125Long-form Identifiermindat:1:5:6296125:3
GUID0
Full ReferenceFan, Lei, Kodamana, Hariprasad, Huang, Biao (2019) Semi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach. AIChE Journal, 65 (3). 964-979 doi:10.1002/aic.16481
Plain TextFan, Lei, Kodamana, Hariprasad, Huang, Biao (2019) Semi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach. AIChE Journal, 65 (3). 964-979 doi:10.1002/aic.16481
In(2019, March) AIChE Journal Vol. 65 (3) Wiley

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Shang, Chao, Huang, Biao, Yang, Fan, Huang, Dexian (2015) Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling. AIChE Journal, 61 (12). 4126-4139 doi:10.1002/aic.14937
Zhao, Chunhui, Huang, Biao (2018) A full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis. AIChE Journal, 64 (5). 1662-1681 doi:10.1002/aic.16048
Qin, S. Joe (2022) Latent vector autoregressive modeling and feature analysis of high dimensional and noisy data from dynamic systems. AIChE Journal, 68 (6) doi:10.1002/aic.17703
Liu, Zheyuan, Kodamana, Hariprasad, Afacan, Artin, Huang, Biao (2019) Dynamic prediction of interface level using spatial temporal markov random field. Computers & Chemical Engineering, 128. 301-311 doi:10.1016/j.compchemeng.2019.05.035
Bano, Gabriele, Facco, Pierantonio, Bezzo, Fabrizio, Barolo, Massimiliano (2018) Probabilistic Design space determination in pharmaceutical product development: A Bayesian/latent variable approach. AIChE Journal, 64 (7). 2438-2449 doi:10.1002/aic.16133
Zhai, Jianyuan, Boukouvala, Fani (2019) Nonlinear variable selection algorithms for surrogate modeling. AIChE Journal, 65 (8). doi:10.1002/aic.16601
Pashchenko, Dmitry (2019) Flow dynamic in a packed bed filled with Ni‐Al 2 O 3 porous catalyst: Experimental and numerical approach. AIChE Journal, 65 (5). doi:10.1002/aic.16558
Shang, Chao, Yang, Fan, Gao, Xinqing, Huang, Xiaolin, Suykens, Johan A.K., Huang, Dexian (2015) Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE Journal, 61 (11). 3666-3682 doi:10.1002/aic.14888
Tabora, Jose E., Lora Gonzalez, Federico, Tom, Jean W. (2019) Bayesian probabilistic modeling in pharmaceutical process development. AIChE Journal, 65 (11). doi:10.1002/aic.16744
Yacoub, Francois, MacGregor, John F. (2011) Robust processes through latent variable modeling and optimization. AIChE Journal, 57 (5). 1278-1287 doi:10.1002/aic.12352
Schäfer, Pascal, Caspari, Adrian, Kleinhans, Kerstin, Mhamdi, Adel, Mitsos, Alexander (2019) Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks. AIChE Journal, 65 (5). doi:10.1002/aic.16568
 
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