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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
M. Aguilar1, G Alberti2, Behcet Alpat, A. Alvino2  +344 moreInstitutions (39)
TL;DR: The very accurate data show that the positron fraction is steadily increasing from 10 to ∼ 250 GeV, but, from 20 to 250 GeV, the slope decreases by an order of magnitude, showing the existence of new physical phenomena.
Abstract: A precision measurement by the Alpha Magnetic Spectrometer on the International Space Station of the positron fraction in primary cosmic rays in the energy range from 0.5 to 350 GeV based on 6.8 × 10(6) positron and electron events is presented. The very accurate data show that the positron fraction is steadily increasing from 10 to ∼ 250 GeV, but, from 20 to 250 GeV, the slope decreases by an order of magnitude. The positron fraction spectrum shows no fine structure, and the positron to electron ratio shows no observable anisotropy. Together, these features show the existence of new physical phenomena.

1,100 citations

Journal ArticleDOI
TL;DR: Recently, the LHCb Collaboration discovered two hidden-charm pentaquark states, which are also beyond the quark model as discussed by the authors, and investigated various theoretical interpretations of these candidates of the multiquark states.

1,083 citations

Journal ArticleDOI
TL;DR: This unique system is composed of well-distributed clusters of conical spines and trichomes on the cactus stem; each spine contains three integrated parts that have different roles in the fog collection process according to their surface structural features.
Abstract: Multiple biological structures have demonstrated fog collection abilities, such as beetle backs with bumps and spider silks with periodic spindle-knots and joints. Many Cactaceae species live in arid environments and are extremely drought-tolerant. Here we report that one of the survival systems of the cactus Opuntia microdasys lies in its efficient fog collection system. This unique system is composed of well-distributed clusters of conical spines and trichomes on the cactus stem; each spine contains three integrated parts that have different roles in the fog collection process according to their surface structural features. The gradient of the Laplace pressure, the gradient of the surface-free energy and multi-function integration endow the cactus with an efficient fog collection system. Investigations of the structure-function relationship in this system may help us to design novel materials and devices to collect water from fog with high efficiencies.

1,051 citations

Book ChapterDOI
08 Sep 2018
TL;DR: Zhang et al. as discussed by the authors proposed a novel Receptive Fields (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness.
Abstract: Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.

1,028 citations

Journal ArticleDOI
16 Jul 2012-Sensors
TL;DR: This paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches.
Abstract: Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced gas sensor calibration. This paper reviews the descriptions, evaluation, comparison and recent developments in existing gas sensing technologies. A classification of sensing technologies is given, based on the variation of electrical and other properties. Detailed introduction to sensing methods based on electrical variation is discussed through further classification according to sensing materials, including metal oxide semiconductors, polymers, carbon nanotubes, and moisture absorbing materials. Methods based on other kinds of variations such as optical, calorimetric, acoustic and gas-chromatographic, are presented in a general way. Several suggestions related to future development are also discussed. Furthermore, this paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches.

1,018 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