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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: In this article, a novel ultrafast kinetics net electrode assembled via MoSe2/MXene heterojunction is synthesized by a simple hydrothermal method followed by thermal annealing.

123 citations

Proceedings ArticleDOI
20 Jun 2021
TL;DR: Wang et al. as mentioned in this paper explored the sparsity in image SR to improve inference efficiency of SR networks and developed a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation.
Abstract: Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-resolution (LR) images mainly exist in regions of edges and textures, less computational resources are required for those flat regions. Therefore, existing CNN-based methods involve redundant computation in flat regions, which increases their computational cost and limits their applications on mobile devices. In this paper, we explore the sparsity in image SR to improve inference efficiency of SR networks. Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation. Within our SMSR, spatial masks learn to identify "important" regions while channel masks learn to mark redundant channels in those "unimportant" regions. Consequently, redundant computation can be accurately localized and skipped while maintaining comparable performance. It is demonstrated that our SMSR achieves state-of-the-art performance with 41%/33%/27% FLOPs being reduced for ×2/3/4 SR. Code is available at: https://github.com/LongguangWang/SMSR.

123 citations

Journal ArticleDOI
TL;DR: Carbon fiber-reinforced carbon aerogel composites for thermal insulators were prepared by copyrolysis of resorcinol-formaldehyde (RF) aerogels reinforced by oxidized polyacrylonitrile (PAN) fiber felts without any obvious cracks.
Abstract: Carbon fiber-reinforced carbon aerogel composites (C/CAs) for thermal insulators were prepared by copyrolysis of resorcinol-formaldehyde (RF) aerogels reinforced by oxidized polyacrylonitrile (PAN) fiber felts. The RF aerogel composites were obtained by impregnating PAN fiber felts with RF sols, then aging, ethanol exchanging, and drying at ambient pressure. Upon carbonization, the PAN fibers shrink with the RF aerogels, thus reducing the difference of shrinkage rates between the fiber reinforcements and the aerogel matrices, and resulting in C/CAs without any obvious cracks. The three point bend strength of the C/CAs is 7.1 ± 1.7 MPa, and the thermal conductivity is 0.328 W m–1 K–1 at 300 °C in air. These composites can be used as high-temperature thermal insulators (in inert atmospheres or vacuum) or supports for phase change materials in thermal protection system.

123 citations

Journal ArticleDOI
TL;DR: In this article, the authors summarized the existing linear and nonlinear uncertainty propagators and their associated applications in the field of orbital mechanics and discussed the advantages and drawbacks of different methods, as well as potential directions for future efforts.

123 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed an effective approach to gain the dispersion properties of the periodically shunted plate in any directions from the solutions of transcendental eigenvalue problems.

123 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