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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: It is unveiled that CAN stays the most robust when low-degree nodes or high flight flow links are removed, which is similar to the Worldwide Airline Network (WAN), albeit less redundant.
Abstract: This paper encapsulates the Chinese Airline Network (CAN) into multi-layer infrastructures via the “k-core decomposition” method. The network is divided into three layers: Core layer, containing airports of provincial capital cities, is densely connected and sustains most flight flow; Bridge layer, consisting of airports in Tier 2 and Tier 3 cities, mainly connects two other layers; and Periphery layer, comprising airports of remote areas, sustains little flight flow. Moreover, it is unveiled that CAN stays the most robust when low-degree nodes or high flight flow links are removed, which is similar to the Worldwide Airline Network (WAN), albeit less redundant.

233 citations

Journal ArticleDOI
Xiao Liang1, Xudong Zheng1, Weifeng Lv1, Tongyu Zhu1, Ke Xu1 
TL;DR: This paper builds models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing, indicating the bursty nature of human mobility, similar to many other human activities.
Abstract: As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users’ locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis’ traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities.

233 citations

Journal ArticleDOI
TL;DR: It is found that incorporation of 15% 3TP3T-4F into the PM6:Y6 blend results in facilitating exciton dissociation, increasing charge transport, and reducing trap-assisted recombination, which is responsible for the enlarged PCE.
Abstract: A ternary structure has been demonstrated as being an effective strategy to realize high power conversion efficiency (PCE) in organic solar cells (OSCs); however, general materials selection rules still remain incompletely understood. In this work, two nonfullerene small-molecule acceptors 3TP3T-4F and 3TP3T-IC are synthesized and incorporated as a third component in PM6:Y6 binary blends. The photovoltaic behaviors in the resultant ternary OSCs differ significantly, despite the comparable energy levels. It is found that incorporation of 15% 3TP3T-4F into the PM6:Y6 blend results in facilitating exciton dissociation, increasing charge transport, and reducing trap-assisted recombination. All these features are responsible for the enlarged PCE of 16.7% (certified as 16.2%) in the PM6:Y6:3TP3T-4F ternary OSCs, higher than that (15.6%) in the 3TP3T-IC containing ternary devices. The performance differences are mainly ascribed to the compatibility between the third component and the host materials. The 3TP3T-4F guest acceptor exhibits an excellent compatibility with Y6, tending to form well-mixed phases in the ternary blend without disrupting the favored bicontinuous transport networks, whereas 3TP3T-IC displays a morphological incompatibility with Y6. This work highlights the importance of considering the compatibility for materials selection toward high-efficiency ternary organic OSCs.

233 citations

Journal ArticleDOI
TL;DR: This paper investigates the actuator fault detector design problem for an electric ground vehicle (EGV) that is equipped with an active front-wheel steering system and proposes a gain-scheduling fault detector and an augmented system.
Abstract: In this paper, we investigate the actuator fault detector design problem for an electric ground vehicle (EGV) that is equipped with an active front-wheel steering system. Since the EGV can be steered by a motor automatically, it is desired to design a fault detector for the steering actuator for safety reasons. A two degree of freedom lateral nonlinear vehicle model is established. The nonlinear vehicle model is converted to a linear-parameter-varying (LPV) form and the scheduling vector is related to the vehicle longitudinal velocity. Since it is not easy to measure the longitudinal velocity precisely, the uncertain measurement on the longitudinal velocity is considered and the weighting factors of LPV submodels are subject to uncertainties. Based on the uncertain LPV model, a gain-scheduling fault detector is proposed and an augmented system is obtained. The desired steering angle and the faulty steering angle are both involved in the augmented system. As the steering angle generally has a low-frequency working range, the steering angle amplitude spectrums of three different maneuvers are studied, and the frequency working range is determined. The stability, the $\mathcal {H}{\_}$ performance, and the $\mathcal {H}_{\infty }$ performance of the augmented system are all exploited. Based on the analysis results, the mixed $\mathcal {H}{\_}$ / $\mathcal {H}_{\infty }$ fault detector design method is developed. An experimental test is used to show the performance of the designed fault detector.

233 citations

Journal ArticleDOI
TL;DR: A framework of the cloud healthcare system is proposed based on digital twin healthcare (CloudDTH), a novel, generalized, and extensible framework in the cloud environment for monitoring, diagnosing and predicting aspects of the health of individuals using, for example, wearable medical devices, toward the goal of personal health management.
Abstract: With the development of technologies, such as big data, cloud computing, and the Internet of Things (IoT), digital twin is being applied in industry as a precision simulation technology from concept to practice. Further, simulation plays a very important role in the healthcare field, especially in research on medical pathway planning, medical resource allocation, medical activity prediction, etc. By combining digital twin and healthcare, there will be a new and efficient way to provide more accurate and fast services for elderly healthcare. However, how to achieve personal health management throughout the entire lifecycle of elderly patients, and how to converge the medical physical world and the virtual world to realize real smart healthcare, are still two key challenges in the era of precision medicine. In this paper, a framework of the cloud healthcare system is proposed based on digital twin healthcare (CloudDTH). This is a novel, generalized, and extensible framework in the cloud environment for monitoring, diagnosing and predicting aspects of the health of individuals using, for example, wearable medical devices, toward the goal of personal health management, especially for the elderly. CloudDTH aims to achieve interaction and convergence between medical physical and virtual spaces. Accordingly, a novel concept of digital twin healthcare (DTH) is proposed and discussed, and a DTH model is implemented. Next, a reference framework of CloudDTH based on DTH is constructed, and its key enabling technologies are explored. Finally, the feasibility of some application scenarios and a case study for real-time supervision are demonstrated.

232 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