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  • IC1.54, Stephenson Building, Teesside University

20192019
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Personal profile

Academic Biography

Jichun holds a Ph.D. degree in mechanical engineering from King’s College London, University of London, U.K. He Received a B.Eng. (Hons.) degree the M. Eng. degree in mechatronic engineering from China University of Geosciences (Wuhan). His research are focused on medical devices and robots, electric car battery, Not-destructive Testing, operational management & Intelligent transportation, IoT and AI solutions for bespoke robotics in chemical, environmental, life science, energy and agri-food industries. He worked on projects from EU, EPSRC and Innovate UK on biomass sampling handing robotics and Li-ion battery measurement and modeling most recently. He is a member of IEEE, IET and IMeChE.

 

Fingerprint Dive into the research topics where Jichun Li is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Recurrent neural networks Engineering & Materials Science
Neural networks Engineering & Materials Science
Chemical activation Engineering & Materials Science
Evolutionary algorithms Engineering & Materials Science
Animals Engineering & Materials Science
Manipulators Engineering & Materials Science
Computer simulation Engineering & Materials Science
Communication Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2019 2019

25 Downloads (Pure)

An Improved Complex-Valued Recurrent Neural Network Model for Time-Varying Complex-Valued Sylvester Equation

Ding, L., Xiao, L., Zhou, K., Lan, Y., Zhang, Y. & Li, J., 1 Feb 2019, In : IEEE Access. 7, p. 19291-19302 12 p., 8632909.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Recurrent neural networks
Neural networks
Chemical activation
Sampling
Experiments

A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion

Xiao, L., Zhang, Y., Dai, J., Chen, K., Yang, S., Li, W., Liao, B., Ding, L. & Li, J., 15 May 2019, In : Neural Networks. 117, p. 124-134 11 p.

Research output: Contribution to journalArticleResearchpeer-review

Neural Networks (Computer)
Noise
Neural networks
Manipulators
Chemical activation

Design and Analysis of Two FTRNN Models with Application to Time-Varying Sylvester Equation

Jin, J., Xiao, L., Lu, M. & Li, J., 15 Apr 2019, In : IEEE Access.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Recurrent neural networks
Neural networks
Computer simulation
6 Downloads (Pure)

Exploiting Delay Budget Flexibility for Efficient Group Delivery in the Internet of Things

Yao, Y., Sun, Y., Phillips, C., Cao, Y. & Li, J., 2 Apr 2019, In : IEEE Internet of Things Journal.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Evolutionary algorithms
Animals
Communication
Internet of things