Dr Hua-Liang Wei
BSc, MSc, PhD
School of Electrical and Electronic Engineering
Senior Lecturer
Full contact details
School of Electrical and Electronic Engineering
Amy Johnson Building
Portobello Street
Sheffield
S1 3JD
- Profile
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Dr Wei is head of the following two Labs:
- Dynamical Modelling, Data Mining and Decision Making (3DM)
- Digital Medicine & Computational Neuroscience (DMCN)
- Research interests
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- Identification and modelling for complex nonlinear systems
- NARMAX methodology and applications.
- Artificial neural networks (ANN), radial basis function networks (RBFN), wavelet neural networks and multiresolution wavelet models, computational statistics, machine learning, intelligent computation and data mining.
- Regression analysis, parameter estimation and optimization, sparse representation.
- Nonlinear and nonstationary (time-varying) signal processing, system identification and data modelling.
- Spatio-temporal system identification and modelling.
- Bioscience signal processing and data modelling
- Neurophysiology and neuro-imaging data modelling and analysis.
- EEG, fMRI and ECG data processing, modelling and analysis.
- Data based classification, pattern recognition, anomaly detection, with applications in clinical and medical diagnosis and prognosis.
- Forecasting and analysis of complex stochastic dynamical processes with applications in
- Space weather systems.
- Environmental systems.
- Computational economics and finance.
- New concepts and methodologies developments for the identification and analysis of nonlinear complex systems.
- Applications and developments of signal processing, system identification and data modelling to control engineering, bioengineering, neuroscience, systems/synthetic biology, environments, space weather and other emerging areas.
The Dynamical Modelling, Data Mining and Decision Making (3DM) Lab
As part of the Complex Systems and Signal Processing Research Group, led by Professor Billings, this Lab conducts a wide scope of research in developing methods and algorithms for system identification, dynamical modelling, machine learning, data mining, signal processing, nonlinear system analysis, forecasting and decision making, with applications in general engineering, environment, medicine, neuroscience and computational biology, bioinformatics, cheminformatics, space, social dynamics, model and data based decision making, and other emerging areas.
Collaborations:
- Department of Geography, University of Sheffield
- Department of Chemistry, University of Sheffield
- Department of Oncology, University of Sheffield
- Department of Psychology, University of Sheffield
- Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital
- School of Aerospace Engineering, Beijing Institute of Technology, China
- School of Automation Science and Electrical Engineering, Beihang University, China
The Digital Medicine & Computational Neuroscience (DMCN) Lab
As part of the Complex Systems and Signal Processing Research Group, led by Professor Billings, this Lab conducts a wide scope of research in developing methods and algorithms for system identification, biomedical and neurophysiological signal processing, causality analysis, time-varying modelling, medical image processing, healthcare, digital medicine, decision making based on digital data.
Collaborations:
● Department of Chemistry, University of Sheffield
● Department of Oncology, University of Sheffield
● Department of Psychology, University of Sheffield
● Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital - Identification and modelling for complex nonlinear systems
- Publications
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Journal articles
- Inaugural editorial of Transactions on Artificial Intelligence in Space (TAIS). ICCK Transactions on Artificial Intelligence in Space, 1(1), 1-2. View this article in WRRO
- Periodic-enhanced informer model for short-term wind power forecasting using SCADA data. IEEE Transactions on Sustainable Energy, 16(4), 2573-2585. View this article in WRRO
- Small Data for Big Tasks in Seasonal Weather Forecasting: A Balanced Perspective on Interpretability and Predictability of NARMAX and Machine Learning Methods.
- A zero-sequence current analysis approach for rotating machinery fault diagnosis of induction motor drivetrain based on sparse learning. IEEE Transactions on Power Electronics, 40(7), 9800-9810. View this article in WRRO
- New class detection in network traffic classification using confidence information embedded cascade structure. IEEE Transactions on Network Science and Engineering, 12(3), 1692-1706. View this article in WRRO
- EEG signal processing techniques and applications—2nd edition. Sensors, 25(3), 805-805. View this article in WRRO
- An intelligent state evaluation and maintenance arrangement system for wind turbines based on digital twin. Academia Engineering, 1(4). View this article in WRRO
- Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models. Meteorological Applications, 31(1). View this article in WRRO
- A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data. Expert Systems with Applications, 247. View this article in WRRO
- North Atlantic atmospheric circulation indices: links with summer and winter temperature and precipitation in north-west Europe, including persistence and variability. International Journal of Climatology.
- Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting. International Journal of Electrical and Computer Engineering (IJECE), 14(1), 960-970. View this article in WRRO
- Editorial: New theories, models, and AI methods of brain dynamics, brain decoding and neuromodulation. Frontiers in Neuroscience, 17. View this article in WRRO
- A variational auto-encoder based multi-source deep domain adaptation model using optimal transport for cross-machine fault diagnosis of rotating machinery. IEEE Transactions on Instrumentation and Measurement. View this article in WRRO
- A novel sample selection approach based universal unsupervised domain adaptation for fault diagnosis of rotating machinery. Reliability Engineering & System Safety, 240. View this article in WRRO
- An adaptive interval construction based GRU model for short-term wind speed interval prediction using two phase search strategy. IEEE Open Journal of Signal Processing, 4, 375-389. View this article in WRRO
- Modelling short-term appliance energy use with interpretable machine learning: a system identification approach. Arabian Journal for Science and Engineering, 48(11), 15667-15678. View this article in WRRO
- Channel-spatial attention convolutional neural networks trained with adaptive learning rates for surface damage detection of wind turbine blades. Measurement, 217. View this article in WRRO
- Complex systems modelling of UK winter wheat yield. Computers and Electronics in Agriculture, 209. View this article in WRRO
- Online classification of network traffic based on granular computing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(8), 5199-5211. View this article in WRRO
- Highly Efficient Cellulose Nanofiber/Halloysite Nanotube Separators for Sodium-Ion Batteries. Nanomaterials, 15(22), 1745-1745.
- A Novel Interpretable Lightweight Ensemble Learning Method for Static and Dynamic Medical and Healthcare Data Classification. ICCK Transactions on Emerging Topics in Artificial Intelligence, 2(3), 131-131.
Book chapters
- Blockchain-based secure digital twin framework for the smart healthy city, Digital Twin, Blockchain, and Sensor Networks in the Healthy and Mobile City (pp. 199-226). Elsevier
Conference proceedings
- NARX-MLP: a hybrid model for accurate interpretable medical data classification. 2025 10th International Conference on Machine Learning Technologies (ICMLT) (pp 287-291). Helsinki, Finland, 23 May 2025 - 23 May 2025. View this article in WRRO
- SAFE-IML: Sparsity-aware feature extraction for interpretable machine learning with two-stage neural network modelling. 2025 10th International Conference on Machine Learning Technologies (ICMLT) (pp 188-194). Helsinki, Finland, 23 May 2025 - 23 May 2025. View this article in WRRO
- System identification and interpretable modelling of dynamical systems with small data using sparse Bayesian learning. 2025 32nd International Conference on Systems, Signals and Image Processing (IWSSIP). Skopje, North Macedonia, 24 June 2025 - 24 June 2025. View this article in WRRO
- SinGS: Animatable Single-Image Human Gaussian Splats with Kinematic Priors. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp 5571-5580), 10 June 2025 - 17 June 2025.
- On sparse nonlinear system identification using orthogonal matching pursuit, orthogonal least squares and LASSO. 2024 32nd Mediterranean Conference on Control and Automation (MED) (pp 935-940). Chania, Crete, Greece, 11 June 2024 - 11 June 2024. View this article in WRRO
- Uncertainty-informed model selection method for nonlinear system identification and interpretable machine learning. 2024 32nd Mediterranean Conference on Control and Automation (MED) (pp 909-914). Chania, Crete, Greece, 11 June 2024 - 11 June 2024. View this article in WRRO
- Predicting the Atlantic meridional overturning circulation using nonlinear system identification methods and the NARMAX models. Inżynieria Mineralna – Journal of the Polish Mineral Engineering Society, Vol. 1(1) (pp 521-528). Prague, Czech Republic, 28 August 2023 - 28 August 2023. View this article in WRRO
- Assessing uncertainty in space weather forecasting using quantile regression and complex nonlinear systems identification techniques. 2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE). Swansea, UK, 14 August 2023 - 14 August 2023. View this article in WRRO
- System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting. Proc. of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023)
- Inaugural editorial of Transactions on Artificial Intelligence in Space (TAIS). ICCK Transactions on Artificial Intelligence in Space, 1(1), 1-2. View this article in WRRO
- Grants
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Modelling uncertainties in radiation belt forecasts, STFC, 04/2024 - 03/2027, £538,897, as PI
PICANTE - Processes, Impacts, and Changes of ANTarctic Extreme weather, NERC, 02/2024 - 10/2027, £2,060,245, as Co-PI.
EU Horizon 2020, Co-PI, `Prediction of Grespace Radiation Environment and Solar Wind Parameters (PROGRESS), 2 January 2015 to 1 January 2018, €2,5M (Sheffield €700,000).
EPSRC Platform Grant, S A Billings, V Kadirkamanathan, Z Q Lang, D Coca, M Balikhin, H L Wei, `System Identification and Information Processing for Complex Systems´, 4 January 2010 - 3 January 2015, £1,21M
Royal Society Collaboration Grant, V. Kadirkamanathan and H. L. Wei, `Investigation of the control mechanism on co-culture bioethanol producing system´, 2010 -2012, £11,950
EPSRC, H L Wei, `System Identification and Data Modelling of Complex Nonlinear and Nonstationary Processes´, 4 January 2011 to 31 March 2012, £101,004
The Ryder Briggs Charity, P.G. Sarrigiannis, Y. Zhao, H .L. Wei, F. He, S. A. Billings, `The thalamic clock and the cortical focus theory in childhood absence epilepsy—an insight with a new approach of quantitative EEG analysis in the time domain: the error reduction ratio method´, 1 April 2013 to 3 March 2014, £8,378
- Teaching activities
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- Data Modelling and Analysis (UG)
- Optimisation and Search (MEng)
- Optimisation and Search (MSc)
- Nonlinear Systems (MEng)
- Advanced Controller Design (MEng)
- State-Space, Optimal Control and Nonlinear Systems (MSc)
- Professional activities and memberships
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- Chair, Programme Committee - ICNC’10 (the 6th International Conference on Natural Computation 2010), Yantai, China, August 10-12, 2010.
- Programme Committee member for a number of international conferences.
- Reviewer for many international journals.
- Reviewer for many international conferences.