Beihang University

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.

Mechanical properties of nanomaterials: A review

Abstract: Abstract As an emerging material, nanomaterials have attracted extensive attention due to their small size, surface effect and quantum tunneling effect, as well as potential applications in traditional materials, medical devices, electronic devices, coatings and other industries. Herein, the influence of nanoparticle selection, production process, grain size, and grain boundary structures on the mechanical properties of nanomaterials is introduced. The current research progress and application range of nano-materials are presented. The unique properties of nano-materials make them superior over traditional materials. Therefore, nanomaterials will have a broader application prospect in the future. Research on nanomaterials is significant for the development and application of materials science.

Development of a Wearable-Sensor-Based fall detection system

Abstract: Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls.This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient's location.

Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s} =$ 13 TeV using the CMS detector

Abstract: The performance of missing transverse momentum (Tmiss) reconstruction algorithms for the CMS experiment is presented, using proton-proton collisions at a center-of-mass energy of 13 TeV, collected at the CERN LHC in 2016. The data sample corresponds to an integrated luminosity of 35.9 fb-1. The results include measurements of the scale and resolution of Tmiss, and detailed studies of events identified with anomalous Tmiss. The performance is presented of a Tmiss reconstruction algorithm that mitigates the effects of multiple proton-proton interactions, using the "pileup per particle identification" method. The performance is shown of an algorithm used to estimate the compatibility of the reconstructed Tmiss with the hypothesis that it originates from resolution effects.

BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera

Abstract: We propose BodyFusion, a novel real-time geometry fusion method that can track and reconstruct non-rigid surface motion of a human performance using a single consumer-grade depth camera. To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal articulated motion prior for human performance and contribute a skeleton-embedded surface fusion (SSF) method. The key feature of our method is that it jointly solves for both the skeleton and graph-node deformations based on information of the attachments between the skeleton and the graph nodes. The attachments are also updated frame by frame based on the fused surface geometry and the computed deformations. Overall, our method enables increasingly denoised, detailed, and complete surface reconstruction as well as the updating of the skeleton and attachments as the temporal depth frames are fused. Experimental results show that our method exhibits substantially improved nonrigid motion fusion performance and tracking robustness compared with previous state-of-the-art fusion methods. We also contribute a dataset for the quantitative evaluation of fusion-based dynamic scene reconstruction algorithms using a single depth camera.