Institution
Nanjing University of Information Science and Technology
Education•Nanjing, China•
About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.
Papers published on a yearly basis
Papers
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TL;DR: Using O3 exposure-response functions, the costs of O3-induced losses in rice, wheat, forests and forest production, and SOMO35-based morbidity for respiratory diseases and non-accidental mortality are evaluated, representing 7% of the China Gross Domestic Product in 2015.
196 citations
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TL;DR: In this paper, the authors performed first-principle calculations to investigate the mechanical properties of the monolayer borophene, including ideal tensile strength and critical strain.
Abstract: Very recently, two-dimensional (2D) boron sheets (borophene) with rectangular structures were grown successfully on single crystal Ag(111) substrates (Mannix et al 2015 Science 350 1513). The fabricated boroprene is predicted to have unusual mechanical properties. We performed first-principle calculations to investigate the mechanical properties of the monolayer borophene, including ideal tensile strength and critical strain. It was found that monolayer borophene can withstand stress up to 20.26 N m−1 and 12.98 N m−1 in a and b directions, respectively. However, its critical strain was found to be small. In the a direction, the critical value is only 8%, which, to the best of our knowledge, is the lowest among all studied 2D materials. Our numerical results show that the tensile strain applied in the b direction enhances the bucking height of borophene resulting in an out-of-plane negative Poisson's ratio, which makes the boron sheet show superior mechanical flexibility along the b direction. The failure mechanism and phonon instability of monolayer borophene were also explored.
194 citations
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TL;DR: The multiresolution structure and sparsity of wavelets are employed by nonlocal dictionary learning in each decomposition level of the wavelets in a manner that outperforms two state-of-the-art image denoising algorithms on higher noise levels.
Abstract: Exploiting the sparsity within representation models for images is critical for image denoising. The best currently available denoising methods take advantage of the sparsity from image self-similarity, pre-learned, and fixed representations. Most of these methods, however, still have difficulties in tackling high noise levels or noise models other than Gaussian. In this paper, the multiresolution structure and sparsity of wavelets are employed by nonlocal dictionary learning in each decomposition level of the wavelets. Experimental results show that our proposed method outperforms two state-of-the-art image denoising algorithms on higher noise levels. Furthermore, our approach is more adaptive to the less extensively researched uniform noise.
194 citations
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TL;DR: A privacy preservation method, named ECO, with privacy preservation for IoV is proposed in this paper and NSGA-II (non-dominated sorting genetic algorithm II) is adopted to realize multi-objective optimization to reduce the execution time and energy consumption of ECDs and prevent privacy conflicts of the computing tasks.
194 citations
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TL;DR: The evaluation results demonstrate the desirable efficiency of PD-LBP from both the shorter problem solving time and smaller communication requirement of task allocation in dynamic environments.
Abstract: We propose a decentralized belief propagation-based method, PD-LBP, for multi-agent task allocation in open and dynamic grid and cloud environments where both the sets of agents and tasks constantly change. PD-LBP aims at accelerating the online response to, improving the resilience from the unpredicted changing in the environments, and reducing the message passing for task allocation. To do this, PD-LBP devises two phases, pruning and decomposition. The pruning phase focuses on reducing the search space through pruning the resource providers, and the decomposition addresses decomposing the network into multiple independent parts where belief propagation can be operated in parallel. Comparison between PD-LBP and two other state-of-the-art methods, Loopy Belief Propagation-based method and Reduced Binary Loopy Belief Propagation based method, is performed. The evaluation results demonstrate the desirable efficiency of PD-LBP from both the shorter problem solving time and smaller communication requirement of task allocation in dynamic environments.
193 citations
Authors
Showing all 14448 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ashok Kumar | 151 | 5654 | 164086 |
Lei Zhang | 135 | 2240 | 99365 |
Bin Wang | 126 | 2226 | 74364 |
Shuicheng Yan | 123 | 810 | 66192 |
Zeshui Xu | 113 | 752 | 48543 |
Xiaoming Li | 113 | 1932 | 72445 |
Qiang Yang | 112 | 1117 | 71540 |
Yan Zhang | 107 | 2410 | 57758 |
Fei Wang | 107 | 1824 | 53587 |
Yongfa Zhu | 105 | 355 | 33765 |
James C. McWilliams | 104 | 535 | 47577 |
Zhi-Hua Zhou | 102 | 626 | 52850 |
Tao Li | 102 | 2483 | 60947 |
Lei Liu | 98 | 2041 | 51163 |
Jian Feng Ma | 97 | 305 | 32310 |