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Institution

National University of Defense Technology

EducationChangsha, China
About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Radar & Synthetic aperture radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.


Papers
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Journal ArticleDOI
TL;DR: By using multidimensional particle-in-cell simulations, a new regime of stable proton beam acceleration which takes place when a two-ion-species shaped foil is illuminated by a circularly polarized laser pulse is presented.
Abstract: By using multidimensional particle-in-cell simulations, we present a new regime of stable proton beam acceleration which takes place when a two-ion-species shaped foil is illuminated by a circularly polarized laser pulse. In the simulations, the lighter protons are nearly instantaneously separated from the heavier carbon ions due to the charge-to-mass ratio difference. The heavy ion layer expands in space and acts to buffer the proton layer from the Rayleigh-Taylor-like (RT) instability that would have otherwise degraded the proton beam acceleration. A simple three-interface model is formulated to explain qualitatively the stable acceleration of the light ions. In the absence of the RT instability, the high quality monoenergetic proton bunch persists even after the laser-foil interaction ends.

157 citations

Proceedings ArticleDOI
16 Jul 2011
TL;DR: This paper introduces a novel unsupervised feature selection approach via Joint Embedding Learning and Sparse Regression (JELSR), which uses the weight via locally linear approximation to construct graph and unify embedding learning and sparse regression to perform feature selection.
Abstract: The problem of feature selection has aroused considerable research interests in the past few years. Traditional learning based feature selection methods separate embedding learning and feature ranking. In this paper, we introduce a novel unsupervised feature selection approach via Joint Embedding Learning and Sparse Regression (JELSR). Instead of simply employing the graph laplacian for embedding learning and then regression, we use the weight via locally linear approximation to construct graph and unify embedding learning and sparse regression to perform feature selection. By adding the l2,1-norm regularization, we can learn a sparse matrix for feature ranking. We also provide an effective method to solve the proposed problem. Compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression simultaneously. Plenty of experimental results are provided to show the validity.

157 citations

Posted Content
TL;DR: In this article, a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation is presented, called Lazily Aggregated Gradient (LAG).
Abstract: This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation. Simple rules are designed to detect slowly-varying gradients and, therefore, trigger the reuse of outdated gradients. The resultant gradient-based algorithms are termed Lazily Aggregated Gradient --- justifying our acronym LAG used henceforth. Theoretically, the merits of this contribution are: i) the convergence rate is the same as batch gradient descent in strongly-convex, convex, and nonconvex smooth cases; and, ii) if the distributed datasets are heterogeneous (quantified by certain measurable constants), the communication rounds needed to achieve a targeted accuracy are reduced thanks to the adaptive reuse of lagged gradients. Numerical experiments on both synthetic and real data corroborate a significant communication reduction compared to alternatives.

157 citations

Journal ArticleDOI
TL;DR: This paper is concerned with reliable fuzzy tracking control for a near-space hypersonic vehicle (NSHV) subject to aperiodic measurement information and stochastic actuator failures.
Abstract: This paper is concerned with reliable fuzzy tracking control for a near-space hypersonic vehicle (NSHV) subject to aperiodic measurement information and stochastic actuator failures. The NSHV dynamics is approximated by the Takagi–Sugeno fuzzy models and the stochastic failures are characterized by a Markov chain. Different with the existing tracking results on NSHV, only the aperiodic sampling measurements are available during system operation. To realize the tracking objective, a reliable fuzzy sampled-data tracking control strategy is presented. An appropriate time-dependent Lyapunov function is constructed to fully capture the real sampling pattern. The sampling-interval-dependent mean square exponential stability criterion with disturbance attenuation is then established. The solution of the tracking controller gains can be obtained by solving an optimization problem. Finally, the simulation studies on NSHV dynamics in the entry phase are performed to verify the validity of the developed fuzzy tracking control strategy.

157 citations

Journal ArticleDOI
TL;DR: The applied cable-driven architectures and the corresponding theoretical studies are reviewed and summarized here, as well as their theories and methods, such as the workspace analysis based on the Jacobian matrix, particle swarm optimization and genetic algorithm, and kinematic designbased on the graph theory.
Abstract: Research on the cable-driven mechanism has greatly developed with the booming of the robots in the past 30 years, and a range of corresponding theoretical studies have been published on them. The large-scale robot or manipulator with the complex cable-driven mechanism can be reconfigured. However, more theoretical studies are required on their topological architecture design and optimization to achieve this. Therefore, the applied cable-driven architectures and the corresponding theoretical studies are reviewed and summarized here. The parallel, serial, and differential architecture are illustrated, as well as their theories and methods, such as the workspace analysis based on the Jacobian matrix, particle swarm optimization and genetic algorithm, and kinematic design based on the graph theory are described. The features of the architecture and the theory studies are concluded. It is hoped that this study will help with design of future studies.

157 citations


Authors

Showing all 39659 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Jian Li133286387131
Chi Lin1251313102710
Wei Xu103149249624
Lei Liu98204151163
Xiang Li97147242301
Chang Liu97109939573
Jian Huang97118940362
Tao Wang97272055280
Wei Liu96153842459
Jian Chen96171852917
Wei Wang95354459660
Peng Li95154845198
Jianhong Wu9372636427
Jianhua Zhang9241528085
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202397
2022468
20212,986
20203,468
20193,695