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Xin Li
Researcher at Shanghai Jiao Tong University
Publications - 89
Citations - 971
Xin Li is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Rogue wave & Computer science. The author has an hindex of 14, co-authored 84 publications receiving 544 citations. Previous affiliations of Xin Li include Bay Institute.
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Journal ArticleDOI
A review on fluid dynamics of flapping foils
Wu Xia,Wu Xia,Xiantao Zhang,Xiantao Zhang,Xinliang Tian,Xinliang Tian,Xin Li,Xin Li,Wenyue Lu,Wenyue Lu +9 more
TL;DR: The fluid dynamics of flapping foils are reviewed in this paper, where a wide range of researches are conducted for the two-dimensional flapping foil which has a relatively simple geometry, however, for a three-dimensional foil, the aspect ratio and shape take effects and completely distinct fluid dynamics are revealed compared with the 2D one.
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Application of an adaptive bistable power capture mechanism to a point absorber wave energy converter
TL;DR: In this article, an adaptive bistable power capture mechanism is proposed for point absorber wave energy converters in regular waves, which is realized by two symmetrically oblique main springs together with two auxiliary springs and can adjust the potential function automatically to lower the potential barrier near the unstable equilibrium position.
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Numerical and experimental study on the maneuverability of an active propeller control based wave glider
Wang Peng,Wang Peng,Wang Daoyong,Wang Daoyong,Xiantao Zhang,Xiantao Zhang,Xin Li,Xin Li,Tao Peng,Huimin Lu,Xinliang Tian,Xinliang Tian +11 more
TL;DR: An 8 degree-of-freedom (DOF) mathematical model of the wave glider based on the active propeller control is developed and the results demonstrate that the propellers control based waveglider has better maneuverability than the conventional rudder control basedwave glider.
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Visual information processing for deep-sea visual monitoring system
TL;DR: This paper proposes the concept of a learning-based deep-sea visual monitoring system, uses the gradient generation adversarial network (GGAN) to recover the heavily destroyed underwater images, and proposes using deep compressed learning for real-time communication.
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Numerical simulation of irregular wave-simulating irregular wave train
TL;DR: Wang et al. as discussed by the authors used a hydrodynamic transfer function to calculate the amplitude of wave-maker motion associated with each wave component, then superposition was carried out on all of the wave-makers motion components to get the final wavemaker motion.