World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
85
Citations
28292
World Ranking
384
National Ranking
17

Research.com Recognitions

  • 2018 - IEEE Fellow For contributions to and application of Bayesian soft-sensing for control performance monitoring

Overview

Biao Huang is affiliated with the University of Alberta in Canada. Their research is primarily situated within the field of Engineering, with a particular focus on Control and Systems Engineering, which accounts for the majority of their publications. Additional subfields of study include Artificial Intelligence, Mechanical Engineering, Statistics, Probability and Uncertainty, and Electrical and Electronic Engineering.

The scientist's work spans a variety of main research topics, including:

  • Fault Detection and Control Systems
  • Advanced Control Systems Optimization
  • Control Systems and Identification
  • Mineral Processing and Grinding
  • Advanced Statistical Process Monitoring
  • Spectroscopy and Chemometric Analyses
  • Iterative Learning Control Systems

Biao Huang has published numerous papers in several key venues, reflecting consistent engagement with journals focused on chemical engineering and control systems. Frequent publication venues include:

  • Computers & Chemical Engineering
  • Journal of Process Control
  • SSRN Electronic Journal
  • IEEE Transactions on Cybernetics
  • arXiv (Cornell University)

Some of the recent papers authored or co-authored by Biao Huang are as follows:

  • "A review On reinforcement learning: Introduction and applications in industrial process control," 2020, Computers & Chemical Engineering
  • "Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives," 2020, IEEE Transactions on Intelligent Transportation Systems
  • "Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning," 2022, IEEE Transactions on Neural Networks and Learning Systems
  • "Transfer Learning-Motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives," 2023, IEEE Transactions on Neural Networks and Learning Systems
  • "Reinforcement learning approach to autonomous PID tuning," 2022, Computers & Chemical Engineering

The scientist has collaborated extensively with several frequent co-authors, notably:

  • Ronghu Chi
  • Chunhui Zhao
  • Hongtian Chen
  • Zhongsheng Hou
  • Ranjith Chiplunkar

Biao Huang was awarded the IEEE Fellow distinction in 2018 for contributions to and applications of Bayesian soft-sensing for control performance monitoring.

Best Publications

  • A new method for stabilization of networked control systems with random delays

    Liqian Zhang;Yang Shi;Tongwen Chen;Biao Huang

  • Data Mining and Analytics in the Process Industry: The Role of Machine Learning

    Zhiqiang Ge;Zhihuan Song;Steven X. Ding;Biao Huang

  • Performance Assessment of Control Loops: Theory and Applications

    Biao Huang;S. L. Shah;M. A. Johnson;M. J. Grimble

  • A review On reinforcement learning: Introduction and applications in industrial process control

    Rui Nian;Jinfeng Liu;Biao Huang

  • 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

  • Performance Assessment of Control Loops

    Biao Huang;Sirish L. Shah

  • Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

    Hongtian Chen;Bin Jiang;Steven X. Ding;Biao Huang

  • Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach

    Biao Huang;Ramesh Kadali

  • Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference

    Qingchao Jiang;Xuefeng Yan;Biao Huang

  • Subspace method aided data-driven design of fault detection and isolation systems

    S.X. Ding;P. Zhang;A. Naik;E.L. Ding

  • 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

  • Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

    Qingchao Jiang;Xuefeng Yan;Biao Huang

  • Design of inferential sensors in the process industry: A review of Bayesian methods

    Shima Khatibisepehr;Biao Huang;Swanand Khare

  • A full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis

    Chunhui Zhao;Biao Huang

  • Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

    Xiaofeng Yuan;Jiao Zhou;Biao Huang;Yalin Wang

  • Closed-loop subspace identification: an orthogonal projection approach

    Biao Huang;Steven X. Ding;S.Joe Qin

  • A data driven subspace approach to predictive controller design

    Ramesh Kadali;Biao Huang;Anthony Rossiter

  • Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

    Youqing Wang;Yabin Si;Biao Huang;Zhijiang Lou

  • H∞ model reduction of Markovian jump linear systems☆

    Liqian Zhang;Biao Huang;James Lam

Frequent Co-Authors

Sirish L. Shah
Sirish L. Shah University of Alberta
Fei Liu
Fei Liu Jiangnan University
Tongwen Chen
Tongwen Chen University of Alberta
Zhongsheng Hou
Zhongsheng Hou Qingdao University
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Zidong Wang
Zidong Wang Brunel University London
Jinfeng Liu
Jinfeng Liu University of Alberta
Nina F. Thornhill
Nina F. Thornhill Imperial College London
Xiaofeng Yuan
Xiaofeng Yuan Central South University
Jing-Li Luo
Jing-Li Luo Shenzhen University

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