Institution
Beihang University
Education•Beijing, China•
About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.
Topics: Computer science, Control theory, Nonlinear system, Microstructure, Artificial neural network
Papers published on a yearly basis
Papers
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TL;DR: The nth-order LDP is proposed to encode the (n-1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP).
Abstract: This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The nth-order LDP is proposed to encode the (n-1)th -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.
996 citations
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TL;DR: Li et al. as discussed by the authors showed that branched alkyl chains in non-fullerene acceptors allow favorable morphology in the active layer, enabling a certified device efficiency of 17.32% with a fill factor of 81.5% for single-junction organic solar cells.
Abstract: Molecular design of non-fullerene acceptors is of vital importance for high-efficiency organic solar cells. The branched alkyl chain modification is often regarded as a counter-intuitive approach, as it may introduce an undesirable steric hindrance that reduces charge transport in non-fullerene acceptors. Here we show the design and synthesis of a highly efficient non-fullerene acceptor family by substituting the beta position of the thiophene unit on a Y6-based dithienothiophen[3,2-b]-pyrrolobenzothiadiazole core with branched alkyl chains. It was found that such a modification to a different alkyl chain length could completely change the molecular packing behaviour of non-fullerene acceptors, leading to improved structural order and charge transport in thin films. An unprecedented efficiency of 18.32% (certified value of 17.9%) with a fill factor of 81.5% is achieved for single-junction organic solar cells. This work reveals the importance of the branched alkyl chain topology in tuning the molecular packing and blend morphology, which leads to improved organic photovoltaic performance. Molecular design of acceptor and donor molecules has enabled major progress in organic photovoltaics. Li et al. show that branched alkyl chains in non-fullerene acceptors allow favourable morphology in the active layer, enabling a certified device efficiency of 17.9%.
966 citations
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TL;DR: The role of big data in supporting smart manufacturing is discussed, a historical perspective to data lifecycle in manufacturing is overviewed, and a conceptual framework proposed in the paper is proposed.
937 citations
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TL;DR: It is demonstrated that the Li2S decomposition energy barrier is associated with the binding between isolated Li ions and the sulfur in sulfides; this is the main reason that sulfide materials can induce lower overpotential compared with commonly used carbon materials.
Abstract: Polysulfide binding and trapping to prevent dissolution into the electrolyte by a variety of materials has been well studied in Li−S batteries. Here we discover that some of those materials can play an important role as an activation catalyst to facilitate oxidation of the discharge product, Li2S, back to the charge product, sulfur. Combining theoretical calculations and experimental design, we select a series of metal sulfides as a model system to identify the key parameters in determining the energy barrier for Li2S oxidation and polysulfide adsorption. We demonstrate that the Li2S decomposition energy barrier is associated with the binding between isolated Li ions and the sulfur in sulfides; this is the main reason that sulfide materials can induce lower overpotential compared with commonly used carbon materials. Fundamental understanding of this reaction process is a crucial step toward rational design and screening of materials to achieve high reversible capacity and long cycle life in Li−S batteries.
933 citations
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TL;DR: Using tetrahexahedral gold nanorods as a heterogeneous electrocatalyst, an electrocatalytic N2 reduction reaction was shown to be possible at room temperature and atmospheric pressure, with a high Faradic efficiency up to 4.02% at -0.2 V vs reversible hydrogen electrode.
Abstract: Using tetrahexahedral gold nanorods as a heterogeneous electrocatalyst, an electrocatalytic N2 reduction reaction is shown to be possible at room temperature and atmospheric pressure, with a high Faradic efficiency up to 4.02% at -0.2 V vs reversible hydrogen electrode (1.648 µg h-1 cm-2 and 0.102 µg h-1 cm-2 for NH3 and N2 H4 ·H2 O, respectively).
923 citations
Authors
Showing all 67500 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
H. S. Chen | 179 | 2401 | 178529 |
Alan J. Heeger | 171 | 913 | 147492 |
Lei Jiang | 170 | 2244 | 135205 |
Wei Li | 158 | 1855 | 124748 |
Shu-Hong Yu | 144 | 799 | 70853 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Igor Katkov | 125 | 972 | 71845 |
Tao Zhang | 123 | 2772 | 83866 |
Nicholas A. Kotov | 123 | 574 | 55210 |
Shi Xue Dou | 122 | 2028 | 74031 |
Li Yuan | 121 | 948 | 67074 |
Robert O. Ritchie | 120 | 659 | 54692 |
Haiyan Wang | 119 | 1674 | 86091 |