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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.

Neural networks Engineering & Materials Science
Recurrent 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

42 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
23 Downloads (Pure)

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. 7, p. 58945-58950 6 p., 8691448.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Recurrent neural networks
Neural networks
Computer simulation
18 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. 6, 4, p. 6593-6605 8680622.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Evolutionary algorithms
Animals
Communication
Internet of things

New Noise-Tolerant ZNN Models With Predefined-Time Convergence for Time-Variant Sylvester Equation Solving

Xiao, L., Zhang, Y., Dai, J., Li, J. & Li, W., 5 Aug 2019, (Accepted/In press) In : IEEE Transactions on Systems, Man, and Cybernetics: Systems. p. 1-12

Research output: Contribution to journalArticleResearchpeer-review

Neural networks
Chemical activation
Control systems