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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A two-stage scheduling method for hot rolling and its application
TL;DR: In this paper, a two-stage hot rolling scheduling method is proposed for steel processing, which involves many objectives and constraints in both technical and practical respects, and the proposed technique can improve production efficiency and offer significant economic benefits.
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Decentralized asymptotic fault tolerant control of near space vehicle with high order actuator dynamics
TL;DR: A decentralized asymptotic fault tolerant control system is proposed for near space vehicle (NSV) attitude dynamics, and the actuator failure model is developed whose behavior is described by high-order dynamics.
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Central Suboptimal H ∞ Filter Design for Linear Time-Varying Systems with State or Measurement Delay
TL;DR: This paper presents central finite-dimensional H∞ filters for linear systems with state or measurement delay that are suboptimal for a given threshold γ with respect to a modified Bolza–Meyer quadratic criterion including an attenuation control term with opposite sign.
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Crosstalk shielding of transition metal ions for long cycling lithium–metal batteries
Xue-Qiang Zhang,Xin-Meng Wang,Xin-Meng Wang,Bo-Quan Li,Peng Shi,Jia-Qi Huang,Aibing Chen,Qiang Zhang +7 more
TL;DR: In this article, the effect of transition metal ions, e.g., Mn ions, dissolved from the cathode on the failure of lithium anodes was investigated in a working cell, and a graphene-coated separator was proposed to obstruct Mn ions by adsorption for lithium protection.
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Learning very fast decision tree from uncertain data streams with positive and unlabeled samples
TL;DR: Experimental results demonstrate the strong ability and efficiency of puuCVFDT to handle concept drift with uncertainty under positive and unlabeled learning scenario, and the classification performance of the proposed algorithm is still compared to that of CVFDT, which is learned from fully labeled data without uncertainty.