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Institution

University of Electronic Science and Technology of China

EducationChengdu, China
About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.


Papers
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Journal ArticleDOI
TL;DR: This paper addresses the vibration control and the input constraint for an Euler–Bernoulli beam system under a periodic distributed disturbance and aperiodic boundary disturbance and proposes a restrained adaptive boundary iterative learning control law based on a time-weighted Lyapunov–Krasovskii-like composite energy function.
Abstract: This paper addresses the vibration control and the input constraint for an Euler–Bernoulli beam system under aperiodic distributed disturbance and aperiodic boundary disturbance. Hyperbolic tangent functions and saturation functions are adopted to tackle the input constraint. A restrained adaptive boundary iterative learning control (ABILC) law is proposed based on a time-weighted Lyapunov–Krasovskii-like composite energy function. In order to deal with the uncertainty of a system parameter and reject the external disturbances, three adaptive laws are designed and learned in the iteration domain. All the system states of the closed-loop system are proved to be bounded in each iteration. Along the iteration axis, the displacements asymptotically converge toward zero. Simulation results are provided to illustrate the effectiveness of the proposed ABILC scheme.

178 citations

Journal ArticleDOI
TL;DR: This paper investigates the global asymptotic stability and stabilization of memristive neural networks (MNNs) with communication delays via event-triggered sampling control through a newly augmented Lyapunov-Krasovskii functional.

178 citations

Journal ArticleDOI
TL;DR: The results show that surface polariton Cherenkov light radiation source can generate radiation, from visible light to the ultraviolet frequency regime and the radiation power density can reach or even exceed 10(8) W/cm(2) depending on the beam energy and current density.
Abstract: A physical phenomenon has been found: in a structure of nanometal film with dielectric-medium loading, the surface polaritons excited by a uniformly moving electron bunch can be transformed into Cherenkov radiation with intensity enhancement in the medium. Based on this mechanism, the surface polariton Cherenkov light radiation source is presented and explored in the Letter. The results show that surface polariton Cherenkov light radiation source can generate radiation, from visible light to the ultraviolet frequency regime and the radiation power density can reach or even exceed 10(8) W/cm(2) depending on the beam energy and current density. It is a tunable and miniature light radiation source promising to be integrated on a chip and built into a light radiation source array.

178 citations

Journal ArticleDOI
TL;DR: This work proposes a novel approach to jointly exploit feature adaptation with distribution matching and sample adaptation with landmark selection, suitable for both homogeneous- and heterogeneous-domain adaptations by learning domain-specific projections.
Abstract: Domain adaptation aims to leverage knowledge from a well-labeled source domain to a poorly labeled target domain. A majority of existing works transfer the knowledge at either feature level or sample level. Recent studies reveal that both of the paradigms are essentially important, and optimizing one of them can reinforce the other. Inspired by this, we propose a novel approach to jointly exploit feature adaptation with distribution matching and sample adaptation with landmark selection. During the knowledge transfer, we also take the local consistency between the samples into consideration so that the manifold structures of samples can be preserved. At last, we deploy label propagation to predict the categories of new instances. Notably, our approach is suitable for both homogeneous- and heterogeneous-domain adaptations by learning domain-specific projections. Extensive experiments on five open benchmarks, which consist of both standard and large-scale datasets, verify that our approach can significantly outperform not only conventional approaches but also end-to-end deep models. The experiments also demonstrate that we can leverage handcrafted features to promote the accuracy on deep features by heterogeneous adaptation.

178 citations

Journal ArticleDOI
TL;DR: Real-time measurements are performed in an optical fibre system of the unstable breakup of a continuous wave field, simultaneously characterizing emergent modulation instability breather pulses and their associated statistics, which allow quantitative comparison between experiment, modelling and theory.
Abstract: Modulation instability is a fundamental process of nonlinear science, leading to the unstable breakup of a constant amplitude solution of a physical system. There has been particular interest in studying modulation instability in the cubic nonlinear Schrodinger equation, a generic model for a host of nonlinear systems including superfluids, fibre optics, plasmas and Bose-Einstein condensates. Modulation instability is also a significant area of study in the context of understanding the emergence of high amplitude events that satisfy rogue wave statistical criteria. Here, exploiting advances in ultrafast optical metrology, we perform real-time measurements in an optical fibre system of the unstable breakup of a continuous wave field, simultaneously characterizing emergent modulation instability breather pulses and their associated statistics. Our results allow quantitative comparison between experiment, modelling and theory, and are expected to open new perspectives on studies of instability dynamics in physics.

178 citations


Authors

Showing all 51090 results

NameH-indexPapersCitations
Gang Chen1673372149819
Frede Blaabjerg1472161112017
Kuo-Chen Chou14348757711
Yi Yang143245692268
Guanrong Chen141165292218
Shuit-Tong Lee138112177112
Lei Zhang135224099365
Rajkumar Buyya133106695164
Lei Zhang130231286950
Bin Wang126222674364
Haiyan Wang119167486091
Bo Wang119290584863
Yi Zhang11643673227
Qiang Yang112111771540
Chun-Sing Lee10997747957
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023159
2022980
20217,384
20207,220
20196,976