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Institution

Hefei University of Technology

EducationHefei, China
About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.


Papers
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Journal ArticleDOI
TL;DR: The syneresis of starch gel during freeze-thaw process was reduced by the addition of pectin, and a significant increase was observed in both the proportion of slowly digestible starch and resistant starch for retrograded starch-pectin mixtures.

151 citations

Journal ArticleDOI
TL;DR: Two conjugated microporous polymers containing thiophene-moieties obtained by the polymerization of 3,3',5,5'-tetrabromo-2,2'-bithiophene and ethynylbenzene monomers through the palladium-catalyzed Sonogashira-Hagihara crosscoupling reaction show high thermal stability and ultrahigh absorption performance for iodine vapour.

151 citations

Journal ArticleDOI
TL;DR: In this paper, the authors reported both experimental and theoretical assessment of layered MoSe 2 nanoplates as the anode materials for Na-ion batteries and showed that they can achieve the initial discharge and charge capacities of 513 and 440 µmg −1 at the current of 0.1C in a voltage of 1.1-3 µmV, respectively.

151 citations

Journal ArticleDOI
TL;DR: Self-supervised video hashing (SSVH) as discussed by the authors proposes a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with less computations than the stacked architecture.
Abstract: Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss. In this paper, we propose a novel unsupervised video hashing framework dubbed self-supervised video hashing (SSVH), which is able to capture the temporal nature of videos in an end-to-end learning to hash fashion. We specifically address two central problems: 1) how to design an encoder–decoder architecture to generate binary codes for videos and 2) how to equip the binary codes with the ability of accurate video retrieval. We design a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with less computations than the stacked architecture. Then, we encourage the binary codes to simultaneously reconstruct the visual content and neighborhood structure of the videos. Experiments on two real-world data sets show that our SSVH method can significantly outperform the state-of-the-art methods and achieve the current best performance on the task of unsupervised video retrieval.

151 citations

Journal ArticleDOI
TL;DR: Using cascaded systems' theory and graph theory, it is shown that the attitude synchronization is achieved asymptotically and the induced vibrations by flexible appendages are simultaneously suppressed under the proposed control law.

151 citations


Authors

Showing all 28292 results

NameH-indexPapersCitations
Yi Chen2174342293080
Xiang Zhang1541733117576
Jun Chen136185677368
Shuicheng Yan12381066192
Yang Li117131963111
Jian Liu117209073156
Han-Qing Yu10571839735
Jianqiao Ye10196242647
Wei Liu96153842459
Wei Zhou93164039772
Panos M. Pardalos87120739512
Zhong Chen80100028171
Yong Zhang7866536388
Rong Cao7656821747
Qian Zhang7689125517
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023106
2022490
20213,120
20202,931
20192,666
20182,151