M
Minrui Fei
Researcher at Shanghai University
Publications - 316
Citations - 4383
Minrui Fei is an academic researcher from Shanghai University. The author has contributed to research in topics: Control theory & Control system. The author has an hindex of 31, co-authored 298 publications receiving 3167 citations. Previous affiliations of Minrui Fei include Zhejiang Normal University.
Papers
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A Secure Charging Scheme for Electric Vehicles With Smart Communities in Energy Blockchain
TL;DR: In this paper, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts, and a reputation-based delegated Byzantine fault tolerance consensus algorithm is proposed to efficiently achieve the consensus in the permissioned blockchain.
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Network-Based T–S Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles
TL;DR: The stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T–S fuzzy DPS and that of the controller, which can stabilize states of the UMV.
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An Anomaly Detection Approach Based on Isolation Forest Algorithm for Streaming Data using Sliding Window
TL;DR: Using the frame of sliding windows and taking into account the concept drift phenomenon, a novel anomaly detection framework is presented and an adapted streaming data anomaly detection algorithm based on the iForest algorithm, namely iForestASD is proposed.
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Network-based H∞ control for T–S fuzzy systems with an adaptive event-triggered communication scheme
TL;DR: An adaptive event-triggered communication scheme for NCSs is presented, and an asynchronous premise re-construct method for networked T–S fuzzy systems is proposed, which relaxes the assumption of the prior work that the premises of the plant and the PDC control rules are synchronous.
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Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey
TL;DR: The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms and presents several interesting open problems with future research directions for multi- Population optimization methods.