P
Peng Shi
Researcher at University of Adelaide
Publications - 1601
Citations - 80441
Peng Shi is an academic researcher from University of Adelaide. The author has contributed to research in topics: Control theory & Nonlinear system. The author has an hindex of 137, co-authored 1371 publications receiving 65195 citations. Previous affiliations of Peng Shi include Harbin Engineering University & Harbin University of Science and Technology.
Papers
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Proceedings ArticleDOI
Genetic algorithm optimised fuzzy control of DSTATCOM for improving power quality
TL;DR: In this paper, the authors proposed a fuzzy logic controller for the distribution static compensator (DSTATCOM) for improving the power quality and dynamic performance of a distribution power system.
Journal ArticleDOI
Low-dose salinomycin inhibits breast cancer metastasis by repolarizing tumor hijacked macrophages toward the M1 phenotype.
Huan Shen,Changquan Calvin Sun,Lichun Kang,Xiaoyue Tan,Peng Shi,Lingyu Wang,Ergang Liu,Junbo Gong +7 more
TL;DR: Results showed that low dose salinomycin in the range of 10-50 nM could efficiently induce M1 macrophage polarization in a dose- and time- dependent manner in vitro, with 30 nM SAL being optimal to generate M1-type macrophages from RAW246.7 cells.
Journal ArticleDOI
Stochastic synchronization of complex networks via aperiodically intermittent noise
TL;DR: The comparison principle plays a significant role in synchronizing complex networks in this paper and the sufficient criterion for stochastic synchronization of the networks is also obtained.
Journal ArticleDOI
H∞ fault detection for a class of T-S fuzzy model-based nonlinear networked control systems
TL;DR: In this paper, a robust H ∞ fault detection filter is designed for a class of discrete-time nonlinear networked control systems via T-S fuzzy model with multiple bounded state delay and random packet dropout induced by the limited bandwidth.
Journal ArticleDOI
Deep Collaborative Intelligence-Driven Traffic Forecasting in Green Internet of Vehicles
Zhiwei Guo,Kai Yu,Kostromitin Konstantin,Shahid Mumtaz,Wei Wei,Peng Shi,Joel J. P. C. Rodrigues +6 more
TL;DR: Wang et al. as discussed by the authors proposed a deep collaborative intelligence-driven traffic forecasting model in green Internet of Vehicles (GIoV), which combines both deep embedding and graph embedding together.