Dr Zhan Li

Senior Lecturer, Computer Science

+44 (0) 1792 987360

zhan.li@swansea.ac.uk

Office - 416
Fourth Floor
Computational Foundry
Bay Campus

Available For Postgraduate Supervision

Research Links

ResearcherID

About

Dr Zhan Li is a Senior Lecturer at the Department of Computer Science, Swansea University, U.K. He received his PhD degree from INRIA/University of Montpellier, France. Dr Zhan Li' main interests include AI, robotics, intelligent control and rehabilitation engineering. He is an Editorial Board Member of PLOS One, the Guest Editor of Frontiers in Neurorobotics, Frontiers in Neuroscience and Journal of Healthcare Engineering, and serves as IEEE RAS Technical Committee Member on Collaborative Automation for Flexible Manufacturing

Areas Of Expertise

Neural Networks
Robotics
Machine Learning
Human Movement Understanding

Publications

Research Highlights

Li, Z., Li, C., Li, S., Zhu, S., & Samani, H. (2022). A sparsity-based method for fault-tolerant manipulation of a redundant robot. Robotica, 40(10), 1-19.
https://doi.org/10.1017/S0263574722000285, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59462
Khan, A., Cao, X., Li, Z., & Li, S. (2022). Evolutionary Computation Based Real-time Robot Arm Path-planning Using Beetle Antennae Search. EAI Endorsed Transactions on AI and Robotics, 1, 1-10.
https://doi.org/10.4108/airo.v1i.6, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59568
Tan, N., Zhong, Z., Yu, P., Li, Z., & Ni, F. (2022). A Discrete Model-Free Scheme for Fault Tolerant Tracking Control of Redundant Manipulators. IEEE Transactions on Industrial Informatics, 18(1), 1-1.
https://doi.org/10.1109/tii.2022.3149919, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59339
Li, Z. & Li, S. (2021). Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network. Neural Processing Letters, 54
https://doi.org/10.1007/s11063-021-10678-5, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58642
Li, Z. & Li, S. (2021). Neural Network Model-Based Control for Manipulator: An Autoencoder Perspective. IEEE Transactions on Neural Networks and Learning Systems, 32(5), 1-15.
https://doi.org/10.1109/tnnls.2021.3109953, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57895

All Research

Journal Articles

Li, J., Qu, L., Li, Z., Liao, B., Li, S., Rong, Y., Liu, Z., Liu, Z., & Lin, K. (2023). A Novel Zeroing Neural Network for Solving Time-Varying Quadratic Matrix Equations against Linear Noises. Mathematics, 11(2), 475
https://doi.org/10.3390/math11020475, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62487
Liu, Z., Chen, Y., Xiang, X., Li, Z., Liao, B., & Li, J. (2022). An End-to-End Real-Time Lightweight Network for the Joint Segmentation of Optic Disc and Optic Cup on Fundus Images. Mathematics, 10(22), 4288
https://doi.org/10.3390/math10224288, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62097
Li, Z. & Li, S. (2022). Model-based recurrent neural network for redundancy resolution of manipulator with remote centre of motion constraints. International Journal of Systems Science, 53(14), 1-14.
https://doi.org/10.1080/00207721.2022.2070790, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa60160
Li, Z., Li, C., Li, S., Zhu, S., & Samani, H. (2022). A sparsity-based method for fault-tolerant manipulation of a redundant robot. Robotica, 40(10), 1-19.
https://doi.org/10.1017/S0263574722000285, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59462
Tan, N., Zhong, Z., Yu, P., Li, Z., & Ni, F. (2022). A Discrete Model-Free Scheme for Fault Tolerant Tracking Control of Redundant Manipulators. IEEE Transactions on Industrial Informatics, 18(1), 1-1.
https://doi.org/10.1109/tii.2022.3149919, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59339
Li, Z. & Li, S. (2022). An L₁-Norm Based Optimization Method for Sparse Redundancy Resolution of Robotic Manipulators. IEEE Transactions on Circuits and Systems II: Express Briefs, 69(2), 469-473.
https://doi.org/10.1109/tcsii.2021.3088942, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57319
Khan, A., Cao, X., Li, Z., & Li, S. (2022). Evolutionary Computation Based Real-time Robot Arm Path-planning Using Beetle Antennae Search. EAI Endorsed Transactions on AI and Robotics, 1, 1-10.
https://doi.org/10.4108/airo.v1i.6, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa59568
Li, Z. & Li, S. (2021). Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network. Neural Processing Letters, 54
https://doi.org/10.1007/s11063-021-10678-5, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58642
Li, Z. & Li, S. (2021). Neural Network Model-Based Control for Manipulator: An Autoencoder Perspective. IEEE Transactions on Neural Networks and Learning Systems, 32(5), 1-15.
https://doi.org/10.1109/tnnls.2021.3109953, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57895

Supervision

Development and Implementation of Intuitive Human-Machine Interaction System. (current)

PhD

Other supervisor: Prof Cinzia Giannetti
Medical Image Analysis for Lung and Heart Condition Diagnosis (current)

PhD

Other supervisor: Prof Xianghua Xie

Taught Modules

CS-099 Computers Unplugged

This module gives an overview of the main principles underlying computers and computer science, without resorting to the use of computers. It is accessible, and relevant, to students of all disciplines who wish to learn about, or reinforce their understanding of, computers and computer science.
CS-S00 Year Abroad (Computer Science)

Study at a partner institution abroad.
CSC345 Big Data and Machine Learning

This module provides a broad introduction to artificial intelligence, machine learning, pattern recognition, and their applications to big data problems. The students will gain understanding and knowledge of the theoretical foundations of learning, learn effective machine learning techniques, and acquire practical know-how in applying some of those theories and techniques to real world problems. Topics include big data concept, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.
CSCM45 Big Data and Machine Learning

This module will discuss in-depth some of the most widely used and state-of-the-art artificical intelligence and machine learning techniques and their applications to big data problems. The students will gain both theoretical understanding of learning and practical know-how in applying those theories to real world problems. Topics include big data concept, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.

Dr Zhan Li

Telephone number

Email address

Research Links

About

Areas Of Expertise

Publications

Research Highlights

All Research

Journal Articles

Supervision

Development and Implementation of Intuitive Human-Machine Interaction System. (current)

Medical Image Analysis for Lung and Heart Condition Diagnosis (current)

Taught Modules

CS-099 Computers Unplugged

CS-S00 Year Abroad (Computer Science)

CSC345 Big Data and Machine Learning

CSCM45 Big Data and Machine Learning