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Computer Science

D-Index
39
Citations
5454
World Ranking
9866
National Ranking
619

Overview

Hua-Liang Wei is affiliated with the University of Sheffield in the United Kingdom. Their research spans multiple areas within engineering and computer science, with significant contributions in artificial intelligence, control and systems engineering, and electrical and electronic engineering.

The scientist's recent publications include a variety of topics related to machine fault diagnosis, brain-computer interfaces, and control systems. Notable papers include:

  • Brain functional and effective connectivity based on electroencephalography recordings: A review (2021, Human Brain Mapping)
  • A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines (2020, IEEE Transactions on Industrial Informatics)
  • Switched PI Control Based MRAS for Sensorless Control of PMSM Drives Using Fuzzy-Logic-Controller (2022, IEEE Open Journal of Power Electronics)
  • A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings (2021, International Journal of Automation and Computing)
  • A Deep Adversarial Learning Prognostics Model for Remaining Useful Life Prediction of Rolling Bearing (2021, IEEE Transactions on Artificial Intelligence)

Frequent coauthors in Hua-Liang Wei's research include:

  • Zhaohua Liu
  • Lei Chen
  • Xiaohua Li
  • Mingyang Lv
  • Yan Ding

Common publication venues where their work appears are:

  • arXiv (Cornell University)
  • 2022 27th International Conference on Automation and Computing (ICAC)
  • Sensors
  • IEEE Transactions on Instrumentation and Measurement
  • Complex System Modeling and Simulation

Their main fields of study concentrate on:

  • Engineering
  • Computer Science

Within these fields, specific subfields of study are:

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Cognitive Neuroscience

Research topics frequently addressed by Hua-Liang Wei include:

  • Machine Fault Diagnosis Techniques
  • EEG and Brain-Computer Interfaces
  • Sensorless Control of Electric Motors
  • Gear and Bearing Dynamics Analysis
  • Video Surveillance and Tracking Methods
  • Fault Detection and Control Systems
  • Network Security and Intrusion Detection

Best Publications

  • A new class of wavelet networks for nonlinear system identification

    S.A. Billings;Hua-Liang Wei

  • Feature Subset Selection and Ranking for Data Dimensionality Reduction

    Hua-Liang Wei;S.A. Billings

  • Brain functional and effective connectivity based on electroencephalography recordings: A review

    Jun Cao;Yifan Zhao;Xiaocai Shan;Xiaocai Shan;Hua-liang Wei

  • Term and variable selection for non-linear system identification

    H. L. Wei;S. A. Billings;J. Liu

  • Global Identification of Electrical and Mechanical Parameters in PMSM Drive Based on Dynamic Self-Learning PSO

    Zhao-Hua Liu;Hua-Liang Wei;Xiao-Hua Li;Kan Liu

  • Generalized multiscale radial basis function networks

    Stephen A. Billings;Hua-Liang Wei;Michael A. Balikhin

  • Using the NARMAX approach to model the evolution of energetic electrons fluxes at geostationary orbit

    M. A. Balikhin;R. J. Boynton;S. N. Walker;J. E. Borovsky

  • Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies

    Zhao-Hua Liu;Hua-Liang Wei;Qing-Chang Zhong;Kan Liu

  • Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis

    Zhao-Hua Liu;Bi-Liang Lu;Hua-Liang Wei;Lei Chen

  • Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information

    Hua Liang Wei;Stephen A. Billings

  • An adaptive orthogonal search algorithm for model subset selection and non-linear system identification

    Stephen A. Billings;Hua-Liang Wei

  • The wavelet-NARMAX representation: a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

    S. A. Billings;H. L. Wei

  • Identification of Time-Varying Systems Using Multi-Wavelet Basis Functions

    Yang Li;Hua-liang Wei;S A Billings

  • A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking

    Azlyna Senawi;Hua-Liang Wei;Stephen A. Billings

  • Using the NARMAX OLS-ERR algorithm to obtain the most influential coupling functions that affect the evolution of the magnetosphere

    R. J. Boynton;M. A. Balikhin;S. A. Billings;H. L. Wei

  • Handling missing data in multivariate time series using a vector autoregressive model-imputation (VAR-IM) algorithm

    Faraj Bashir;Hua-Liang Wei

  • A Stacked Auto-Encoder Based Partial Adversarial Domain Adaptation Model for Intelligent Fault Diagnosis of Rotating Machines

    Zhao-Hua Liu;Bi-Liang Lu;Hua-Liang Wei;Lei Chen

  • Identification of Time-Varying Systems Using Multiresolution Wavelet Models

    Hua-Liang Wei;S. A. Billings

  • Sparse Model Identification Using a Forward Orthogonal Regression Algorithm Aided by Mutual Information

    S.A. Billings;Hua-Liang Wei

  • Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks

    H.L. Wei;D.Q. Zhu;S.A. Billings;M.A. Balikhin

Frequent Co-Authors

Stephen A. Billings
Stephen A. Billings University of Sheffield
Grant R. Bigg
Grant R. Bigg University of Sheffield
Visakan Kadirkamanathan
Visakan Kadirkamanathan University of Sheffield
Qing-Chang Zhong
Qing-Chang Zhong Illinois Institute of Technology
Zi-Qiang Lang
Zi-Qiang Lang University of Sheffield
Edward Hanna
Edward Hanna University of Lincoln
Annalena Venneri
Annalena Venneri Brunel University London
Xiaofeng Liao
Xiaofeng Liao Chongqing University
Tingwen Huang
Tingwen Huang Shenzhen Institutes of Advanced Technology
Yu-Lun Chueh
Yu-Lun Chueh National Tsing Hua University

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