Prof. Dr. Jun Liu
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Academic Career Length
17Years (2010-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 October 2019 in ISA Transactions
Journal Article
1 December 2018 in Neurocomputing
Journal Article
3 August 2018 in IET Control Theory & Applications
Journal Article
The study solves multi‐agent cooperative control problems of covering irregularly‐shaped unknown areas. A sweep coverage motion scheme is proposed: the agents are arranged in a row across the area and sweep through. The formation is flexible thus the agents can be scattered across the widest part and can queue up to pass through the narrowest portion. This allows the agents to cover the region by collectively passing through it only once, which may help to improve the survivability of reconnaissance vehicles. A distributed second‐order control law is designed by modelling the agents as point masses in planar space. The approach is inspired by the motion of a string of balls connected by springs, thus virtual elastic force and potential field are applied to the agents, which introduces non‐linearity to the system. Complete coverage as well as collision avoidance among agents and borders are guaranteed by parameter constraints that provided and validated via theoretical deduction and numerical simulations. Compared with the existing unknown‐area coverage methods by randomly exploring the area, the proposed one yields more regular motion paths. Limitations of the work and future research directions are finally concluded.
8 November 2019 in IEEE Transactions on Neural Networks and Learning Systems
Journal Article
NZNan Zhou
Nan ZhouBCBadong Chen
Badong ChenADAnd Yuanhua Du
And Yuanhua DuTJTao Jiang
Tao JiangJun LiuYXYangyang Xu
Yangyang XuConcept factorization (CF) has shown its great advantage for both clustering and data representation and is particularly useful for image representation. Compared with nonnegative matrix factorization (NMF), CF can be applied to data containing negative values. However, the performance of CF method and its extensions will degenerate a lot due to the negative effects of outliers, and CF is an unsupervised method that cannot incorporate label information. In this article, we propose a novel CF method, with a novel model built based on the maximum correntropy criterion (MCC). In order to capture the local geometry information of data, our method integrates the robust adaptive embedding and CF into a unified framework. The label information is utilized in the adaptive learning process. Furthermore, an iterative strategy based on the accelerated block coordinate update is proposed. The convergence property of the proposed method is analyzed to ensure that the algorithm converges to a reliable solution. The experimental results on four real-world image data sets show that the new method can almost always filter out the negative effects of the outliers and outperform several state-of-the-art image representation methods.
15 February 2019 in International Journal of Robust and Nonlinear Control
Journal Article
1 July 2019 in Journal of the Franklin Institute
Journal Article
About Me
Jun Liu received the Ph.D. degree in control science and engineering from the University of Electronic Science and Technology of China(UESTC), Chengdu, China, in 2018. He is currently a Professor with the School of Automation, Chengdu University of Information Technology (CUIT), Chengdu, China. In 2014, he worked with the University of California at San Diego (UCSD), San Diego, CA, USA, as a Visiting Scholar. From 2020 to 2021, he worked with the Hong Kong Polytechnic University (PolyU), Hong Kong SAR, China, as a Postdoctoral Fellow. He has authored or coauthored more than 60 articles in various journals and conference proceedings, and has won several provincial and ministerial awards. He has served as the Sichuan chairman in China of the 5th-7th Asian International Conference on Artificial Intelligence Technology (2021-2023ACAIT), member of the technical procedure committee and co-chairman of the organization committee of the 14th-16th International Conference on Signal Processing Systems (2022-2025ICSPS), etc. He has served as served as a special reviewer of TII, TNNLS, TCB, ISA Trans, Neurocomputing, JFI, CJE, JIS and other journals, as well as editorial board members of some journals. He has served as a commentator of Mathematic Reviews in the United States. His research interests include coordinated control of multi-agent systems, machine learning, new computing and its applications, artificial intelligence and robots.