Z
Zhiwei Gao
Researcher at Northumbria University
Publications - 190
Citations - 7971
Zhiwei Gao is an academic researcher from Northumbria University. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has an hindex of 33, co-authored 160 publications receiving 6182 citations. Previous affiliations of Zhiwei Gao include Nankai University & University of Manchester.
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Observer-based fault estimation and tolerant control for stochastic Takagi–Sugeno fuzzy systems with Brownian parameter perturbations
TL;DR: Rugby fault estimation and fault tolerant control for stochastic Takagi–Sugeno fuzzy systems, subjected to Brownian parameter perturbations, unknown process uncertainties and unexpected faults, are investigated and sufficient conditions are proposed to ensure the robust stability of the overall closed-loop system.
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Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation
Jinyu Hu,Zhiwei Gao,Weisen Pan +2 more
TL;DR: The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels.
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Synthesis on PI-based pitch controller of large wind turbines generator
TL;DR: In this article, a proportional and integral (PI) pitch controller is designed for a large wind turbine generator by using graphical approach, and the influence of the time delay in hydraulic pressure driven of pitch unit on the stability region of PI controller is analyzed.
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Guest Editorial: Special section on data-driven approaches for complex industrial systems
TL;DR: This Special Issue on "Data-Driven Approaches for Complex Industrial Systems" of the IEEE TRANSACTIONS on industrial informatics provides a forum for researchers and practitioners to report recent results on data-driven methods with applications to complex industrial systems, and to identify critical issues and challenges for future investigations in this field.
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Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine
TL;DR: A novel identification technique, that is high-gain observer-based identification approach, is proposed for systems with bounded process and measurement noises and an adaptive change detection and parameter identification algorithm is presented.