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S. Joe Qin
S. Joe Qin
Lingnan University, Hong Kong, President, Member of EASA, Fellow of HKAES, NAI, IEEE, IFAC, AIChE
Bestätigte E-Mail-Adresse bei ln.edu.hk - Startseite
Titel
Zitiert von
Zitiert von
Jahr
A survey of industrial model predictive control technology
SJ Qin, TA Badgwell
Control engineering practice 11 (7), 733-764, 2003
66572003
Statistical process monitoring: basics and beyond
S Joe Qin
Journal of Chemometrics: A Journal of the Chemometrics Society 17 (8‐9), 480-502, 2003
18552003
An overview of industrial model predictive control technology
SJ Qin, TA Badgwell
AIche symposium series 93 (316), 232-256, 1997
15671997
Survey on data-driven industrial process monitoring and diagnosis
SJ Qin
Annual reviews in control 36 (2), 220-234, 2012
15422012
Recursive PCA for adaptive process monitoring
W Li, HH Yue, S Valle-Cervantes, SJ Qin
Journal of process control 10 (5), 471-486, 2000
10692000
Nonlinear Model Predictive Control, chap. An overview of nonlinear model predictive control applications
S Qin, T Badgwell
Birkhäuser Verlag, Boston, MA, 2000
844*2000
An overview of nonlinear model predictive control applications
SJ Qin, TA Badgwell
Nonlinear model predictive control, 369-392, 2000
8282000
An overview of subspace identification
SJ Qin
Computers & chemical engineering 30 (10-12), 1502-1513, 2006
8122006
Identification of faulty sensors using principal component analysis
R Dunia, SJ Qin, TF Edgar, TJ McAvoy
AIChE Journal 42 (10), 2797-2812, 1996
7941996
Recursive PLS algorithms for adaptive data modeling
SJ Qin
Computers & Chemical Engineering 22 (4-5), 503-514, 1998
7911998
Nonlinear predictive control and moving horizon estimation—an introductory overview
F Allgöwer, TA Badgwell, JS Qin, JB Rawlings, SJ Wright
Advances in control, 391-449, 1999
7551999
Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods
S Valle, W Li, SJ Qin
Industrial & Engineering Chemistry Research 38 (11), 4389-4401, 1999
7061999
Nonlinear PLS modeling using neural networks
SJ Qin, TJ McAvoy
Computers & Chemical Engineering 16 (4), 379-391, 1992
6801992
Reconstruction-based fault identification using a combined index
HH Yue, SJ Qin
Industrial & engineering chemistry research 40 (20), 4403-4414, 2001
6292001
Reconstruction-based contribution for process monitoring
CF Alcala, SJ Qin
Automatica 45 (7), 1593-1600, 2009
6192009
Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models
J Yu, SJ Qin
AIChE Journal 54 (7), 1811-1829, 2008
5742008
Subspace approach to multidimensional fault identification and reconstruction
R Dunia, SJ Qin
AIChE Journal 44 (8), 1813-1831, 1998
5591998
Total projection to latent structures for process monitoring
D Zhou, G Li, SJ Qin
AIChE Journal 56 (1), 168-178, 2010
5302010
Control performance monitoring—a review and assessment
SJ Qin
Computers & Chemical Engineering 23 (2), 173-186, 1998
5131998
Fault detection and diagnosis based on modified independent component analysis
JM Lee, SJ Qin, IB Lee
AIChE journal 52 (10), 3501-3514, 2006
4952006
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