Biao Huang
Citat de
Citat de
A new method for stabilization of networked control systems with random delays
L Zhang, Y Shi, T Chen, B Huang
IEEE Transactions on automatic control 50 (8), 1177-1181, 2005
Data mining and analytics in the process industry: the role of machine learning
Z Ge, Z Song, SX Ding, B Huang
IEEE Access 5, 20590-20616, 2017
Performance assessment of control loops: theory and applications
B Huang, SL Shah
Springer Science & Business Media, 1999
Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE
X Yuan, B Huang, Y Wang, C Yang, W Gui
IEEE Transactions on Industrial Informatics 14 (7), 3235-3243, 2018
Dynamic modeling, predictive control and performance monitoring: A data-driven subspace approach
B Huang, R Kadali
Springer, 2008
A review on reinforcement learning: Introduction and applications in industrial process control
R Nian, J Liu, B Huang
Computers & Chemical Engineering 139, 106886, 2020
Good, bad or optimal? Performance assessment of multivariable processes
B Huang, SL Shah, EK Kwok
Automatica 33 (6), 1175-1183, 1997
Subspace method aided data-driven design of fault detection and isolation systems
SX Ding, P Zhang, A Naik, EL Ding, B Huang
Journal of process control 19 (9), 1496-1510, 2009
Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference
Q Jiang, X Yan, B Huang
IEEE Transactions on Industrial Electronics 63 (1), 377-386, 2015
Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives
H Chen, B Jiang, SX Ding, B Huang
IEEE Transactions on Intelligent Transportation Systems 23 (3), 1700-1716, 2020
Detection of multiple oscillations in control loops
NF Thornhill, B Huang, H Zhang
Journal of Process Control 13 (1), 91-100, 2003
Design of inferential sensors in the process industry: A review of Bayesian methods
S Khatibisepehr, B Huang, S Khare
Journal of Process Control 23 (10), 1575-1596, 2013
Detection and diagnosis of stiction in control loops: state of the art and advanced methods
M Jelali, B Huang
Springer Science & Business Media, 2009
Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes
Q Jiang, X Yan, B Huang
Industrial & Engineering Chemistry Research 58 (29), 12899-12912, 2019
A data driven subspace approach to predictive controller design
R Kadali, B Huang, A Rossiter
Control engineering practice 11 (3), 261-278, 2003
A full‐condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis
C Zhao, B Huang
AIChE Journal 64 (5), 1662-1681, 2018
Closed-loop subspace identification: an orthogonal projection approach
B Huang, SX Ding, SJ Qin
Journal of process control 15 (1), 53-66, 2005
Hierarchical quality-relevant feature representation for soft sensor modeling: a novel deep learning strategy
X Yuan, J Zhou, B Huang, Y Wang, C Yang, W Gui
IEEE Transactions on Industrial Informatics 16 (6), 3721-3730, 2019
Robust reliable control for a class of uncertain nonlinear state-delayed systems
Z Wang, B Huang, H Unbehauen
Automatica 35 (5), 955-963, 1999
H∞ model reduction of Markovian jump linear systems
L Zhang, B Huang, J Lam
Systems & Control Letters 50 (2), 103-118, 2003
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