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Institution

Beihang University

EducationBeijing, China
About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. 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.


Papers
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Journal ArticleDOI
TL;DR: The origin and main issues facing the smart city concept are introduced, and the fundamentals of a smart city by analyzing its definition and application domains are presented.
Abstract: Rapid urbanization creates new challenges and issues, and the smart city concept offers opportunities to rise to these challenges, solve urban problems and provide citizens with a better living environment. This paper presents an exhaustive literature survey of smart cities. First, it introduces the origin and main issues facing the smart city concept, and then presents the fundamentals of a smart city by analyzing its definition and application domains. Second, a data-centric view of smart city architectures and key enabling technologies is provided. Finally, a survey of recent smart city research is presented. This paper provides a reference to researchers who intend to contribute to smart city research and implementation.

536 citations

Journal ArticleDOI
17 Feb 2012-PLOS ONE
TL;DR: It is shown that even a minute initial fraction of cooperators may be sufficient to eventually secure a highly cooperative final state, and it is found that the proposed win-stay-lose-learn rule promotes the evolution of cooperation very robustly and independently of the initial conditions.
Abstract: Holding on to one's strategy is natural and common if the later warrants success and satisfaction. This goes against widespread simulation practices of evolutionary games, where players frequently consider changing their strategy even though their payoffs may be marginally different than those of the other players. Inspired by this observation, we introduce an aspiration-based win-stay-lose-learn strategy updating rule into the spatial prisoner's dilemma game. The rule is simple and intuitive, foreseeing strategy changes only by dissatisfied players, who then attempt to adopt the strategy of one of their nearest neighbors, while the strategies of satisfied players are not subject to change. We find that the proposed win-stay-lose-learn rule promotes the evolution of cooperation, and it does so very robustly and independently of the initial conditions. In fact, we show that even a minute initial fraction of cooperators may be sufficient to eventually secure a highly cooperative final state. In addition to extensive simulation results that support our conclusions, we also present results obtained by means of the pair approximation of the studied game. Our findings continue the success story of related win-stay strategy updating rules, and by doing so reveal new ways of resolving the prisoner's dilemma.

533 citations

Journal ArticleDOI
22 Aug 2011
TL;DR: The concept, architecture, core enabling technologies, and typical characteristics of CMfg are discussed and investigated, as well as the differences and relationship between cloud computing and CMfg.
Abstract: Combining the emerged advanced technologies (such as cloud computing, ‘internet of thing’, virtualization, and service-oriented technologies, advanced computing technologies) with existing advanced manufacturing models and enterprise ‘informationization’ technologies, a new computing- and service-oriented manufacturing model, called cloud manufacturing (CMfg), is proposed. The concept, architecture, core enabling technologies, and typical characteristics of CMfg are discussed and investigated, as well as the differences and relationship between cloud computing and CMfg. Four typical CMfg service platforms, i.e. public, private, community, and hybrid CMfg service platforms, are introduced. The key advantages and challenges for implementing CMfg are analysed, as well as the key technologies and main research findings.

530 citations

Journal ArticleDOI
TL;DR: In this article, the authors discuss the latest advancements in the 2D layered inorganic materials for gas sensors and introduce various types of gas-sensing devices and describe the basic parameters and influence factors of the gas sensors to further enhance their performance.
Abstract: Two-dimensional (2D) layered inorganic nanomaterials have attracted huge attention due to their unique electronic structures, as well as extraordinary physical and chemical properties for use in electronics, optoelectronics, spintronics, catalysts, energy generation and storage, and chemical sensors Graphene and related layered inorganic analogues have shown great potential for gas-sensing applications because of their large specific surface areas and strong surface activities This review aims to discuss the latest advancements in the 2D layered inorganic materials for gas sensors We first elaborate the gas-sensing mechanisms and introduce various types of gas-sensing devices Then, we describe the basic parameters and influence factors of the gas sensors to further enhance their performance Moreover, we systematically present the current gas-sensing applications based on graphene, graphene oxide (GO), reduced graphene oxide (rGO), functionalized GO or rGO, transition metal dichalcogenides, layered II

529 citations

Journal ArticleDOI
TL;DR: This work reports, the encapsulation of Li2S cathodes using two-dimensional layered transition metal disulphides that possess a combination of high conductivity and strong binding withLi2S/Li2Sn species and demonstrates a high specific capacity and high areal capacity.
Abstract: Lithium sulphide is a promising battery cathode material, but it suffers from fast capacity decay. Here, the authors demonstrate the use of two-dimensional transition metal disulphides, in contrast to conventional carbon-based materials, for effective encapsulation of the cathode.

529 citations


Authors

Showing all 67500 results

NameH-indexPapersCitations
Yi Chen2174342293080
H. S. Chen1792401178529
Alan J. Heeger171913147492
Lei Jiang1702244135205
Wei Li1581855124748
Shu-Hong Yu14479970853
Jian Zhou128300791402
Chao Zhang127311984711
Igor Katkov12597271845
Tao Zhang123277283866
Nicholas A. Kotov12357455210
Shi Xue Dou122202874031
Li Yuan12194867074
Robert O. Ritchie12065954692
Haiyan Wang119167486091
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023205
20221,178
20216,767
20206,916
20197,080