Dr Zhan Li

Research Highlights

1. 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
2. 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
3. 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
4. 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
5. 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

• 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

• 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.

• 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.