Institution
University of Science and Technology Beijing
Education•Beijing, China•
About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.
Topics: Microstructure, Alloy, Corrosion, Ultimate tensile strength, Austenite
Papers published on a yearly basis
Papers
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TL;DR: The goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.
Abstract: Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.
958 citations
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TL;DR: A series of six-component (FeCoNiCrMn)100−xAlx (x = 0−20 ǫ) high-entropy alloys was synthesized to investigate the alloying effect of Al on the structure and tensile properties as mentioned in this paper.
954 citations
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953 citations
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TL;DR: In this article, composites consisting of polyethylene-oxide/garnet electrolytes were fabricated for a safe solid-state Li-metal rechargeable battery, which achieved high discharge capacity (139.1% after 100 cycles) and high capacity retention (103.6% with coulombic efficiency of 100% after 50 cycles).
916 citations
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TL;DR: In this paper, stable aqueous TiO2 nanofluids with different particle sizes and concentrations were formulated and measured for their static thermal conductivity and rheological behaviour.
889 citations
Authors
Showing all 41904 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Yang Yang | 171 | 2644 | 153049 |
Jun Chen | 136 | 1856 | 77368 |
Jun Lu | 135 | 1526 | 99767 |
Jie Liu | 131 | 1531 | 68891 |
Shuai Liu | 129 | 1095 | 80823 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Shaobin Wang | 126 | 872 | 52463 |
Tao Zhang | 123 | 2772 | 83866 |
Jian Liu | 117 | 2090 | 73156 |
Xin Li | 114 | 2778 | 71389 |
Jianhui Hou | 110 | 429 | 53265 |
Hong Wang | 110 | 1633 | 51811 |
Baoshan Xing | 109 | 823 | 48944 |