F
Feng Liu
Researcher at Stevens Institute of Technology
Publications - 47
Citations - 838
Feng Liu is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Computer science & Wind power. The author has an hindex of 13, co-authored 41 publications receiving 464 citations. Previous affiliations of Feng Liu include Picower Institute for Learning and Memory & Huazhong University of Science and Technology.
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Dynamic analysis, circuit realization, control design and image encryption application of an extended Lü system with coexisting attractors
TL;DR: A chaotic image encryption algorithm is proposed according to the extended Lu system with coexisting attractors and the performance of the algorithm is numerically analyzed.
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An Extremely Simple Chaotic System With Infinitely Many Coexisting Attractors
TL;DR: An extremely simple chaotic system with infinitely many coexisting chaotic attractors that consists of five terms with two nonlinearities, and has an infinite number of unstable equilibria owing to its sinusoidal nonlinearity.
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Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation
Xinglong Ju,Feng Liu,Feng Liu +2 more
TL;DR: This paper investigates the implications of crossover and mutation steps of CGA for the wind farm layout problem, which explains why CGA has a higher possibility of convergence to a suboptimal solution, and proposes novel algorithms by incorporating the self-adaptivity capability of individuals, called Adaptive Genetic Algorithm (AGA) and Self-Informed Genetic Al algorithm (SIGA).
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Predefined-time formation tracking control of networked marine surface vehicles
TL;DR: A novel predefined-time sliding mode surface is proposed and two kinds of hierarchical control algorithms are developed to achieve the formation tracking of the virtual leader, whose acceleration in the earth-fixed coordinates can be zero or bounded.
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Multi-AGV path planning with double-path constraints by using an improved genetic algorithm
TL;DR: An improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning is investigated, using three-exchange crossover heuristic operators to produce more optimal offsprings for getting more information.