2023 - Research.com Electronics and Electrical Engineering in Canada Leader Award
2018 - IEEE Fellow For contributions to and application of Bayesian soft-sensing for control performance monitoring
Biao Huang spends much of his time researching Control theory, Mathematical optimization, Data mining, Control system and Nonlinear system. His work carried out in the field of Control theory brings together such families of science as Matrix, Model predictive control and Benchmark. His study looks at the relationship between Model predictive control and fields such as Control theory, as well as how they intersect with chemical problems.
His research in Mathematical optimization tackles topics such as Robust control which are related to areas like Adaptive control and Exponential stability. The various areas that Biao Huang examines in his Data mining study include Probabilistic logic, Bayesian probability and Soft sensor. Biao Huang combines subjects such as Stability, Particle filter and Expectation–maximization algorithm with his study of Nonlinear system.
His primary areas of study are Control theory, Mathematical optimization, Algorithm, Nonlinear system and Control engineering. His Control theory research includes themes of Model predictive control and Minimum-variance unbiased estimator. His research in Mathematical optimization intersects with topics in Bayesian probability, Particle filter, Estimation theory and Constraint.
His Bayesian probability research is multidisciplinary, incorporating perspectives in Machine learning and Probabilistic logic. His work investigates the relationship between Algorithm and topics such as Expectation–maximization algorithm that intersect with problems in Identification. His work deals with themes such as Control and Benchmark, which intersect with Control engineering.
Algorithm, Nonlinear system, Control theory, Artificial intelligence and Iterative learning control are his primary areas of study. Biao Huang interconnects Fault detection and isolation, Probability density function, Bayesian inference and Expectation–maximization algorithm in the investigation of issues within Algorithm. His Nonlinear system study combines topics from a wide range of disciplines, such as Mathematical optimization, Data model and Robustness.
The concepts of his Control theory study are interwoven with issues in Estimation theory and Control. His biological study spans a wide range of topics, including Machine learning and Pattern recognition. He has researched Iterative learning control in several fields, including Data-driven, Multi-agent system, Lyapunov function and Trajectory.
His primary areas of investigation include Algorithm, Nonlinear system, Artificial intelligence, Data-driven and Feature extraction. The Algorithm study combines topics in areas such as Distribution, Probability density function, Outlier and Expectation–maximization algorithm. Nonlinear system is a subfield of Control theory that Biao Huang explores.
His Control theory research is mostly focused on the topic State observer. His research investigates the connection with Artificial intelligence and areas like Pattern recognition which intersect with concerns in Autoencoder and Probabilistic logic. The study incorporates disciplines such as Principal component analysis and Data mining in addition to Feature extraction.
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A new method for stabilization of networked control systems with random delays
Liqian Zhang;Yang Shi;Tongwen Chen;Biao Huang.
IEEE Transactions on Automatic Control (2005)
Performance Assessment of Control Loops: Theory and Applications
Biao Huang;S. L. Shah;M. A. Johnson;M. J. Grimble.
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Zhiqiang Ge;Zhihuan Song;Steven X. Ding;Biao Huang.
IEEE Access (2017)
Performance Assessment of Control Loops
Biao Huang;Sirish L. Shah.
Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE
Xiaofeng Yuan;Biao Huang;Yalin Wang;Chunhua Yang.
IEEE Transactions on Industrial Informatics (2018)
Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach
Biao Huang;Ramesh Kadali.
Good, bad or optimal? Performance assessment of multivariable processes
B. Huang;S. L. Shah;E. K. Kwok.
Detection of multiple oscillations in control loops
N.F Thornhill;B Huang;H Zhang.
Journal of Process Control (2003)
Subspace method aided data-driven design of fault detection and isolation systems
S.X. Ding;P. Zhang;A. Naik;E.L. Ding.
Journal of Process Control (2009)
Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference
Qingchao Jiang;Xuefeng Yan;Biao Huang.
IEEE Transactions on Industrial Electronics (2016)
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