Institution
Beijing University of Technology
Education•Beijing, Beijing, China•
About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Laser. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.
Papers published on a yearly basis
Papers
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TL;DR: A review of the available technologies for dam deformation monitoring is provided, including those sensors that are already applied in routinary operations and some experimental solutions, to support people who are working in this field to have a complete view of existing solutions, as well as to understand future directions and trends.
Abstract: In recent years, the measurement of dam displacements has benefited from a great improvement of existing technology, which has allowed a higher degree of automation. This has led to data collection with an improved temporal and spatial resolution. Robotic total stations and GNSS (Global Navigation Satellite System) techniques, often in an integrated manner, may provide efficient solutions for measuring 3D displacements on precise locations on the outer surfaces of dams. On the other hand, remote-sensing techniques, such as terrestrial laser scanning, ground-based SAR (synthetic aperture radar) and satellite differential interferometric SAR offer the chance to extend the observed region to a large portion of a structure and its surrounding areas, integrating the information that is usually provided in a limited number of in-situ control points. The design and implementation of integrated monitoring systems have been revealed as a strategic solution to analyze different situations in a spatial and temporal context. Research devoted to the optimization of data processing tools has evolved with the aim of improving the accuracy and reliability of the measured deformations. The analysis of the observed data for the interpretation and prediction of dam deformations under external loads has been largely investigated on the basis of purely statistical or deterministic methods. The latter may integrate observation from geodetic, remote-sensing and geotechnical/structural sensors with mechanical models of the dam structure. In this paper, a review of the available technologies for dam deformation monitoring is provided, including those sensors that are already applied in routinary operations and some experimental solutions. The aim was to support people who are working in this field to have a complete view of existing solutions, as well as to understand future directions and trends.
101 citations
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TL;DR: In this article, the authors analyzed the influence of shear processes on nonlinear flow behavior through 3D rough-walled rock fractures and found that the relationship between the volumetric flow rate and hydraulic gradient can be well fit using Forchheimer's law.
101 citations
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TL;DR: In this paper, the occurrence of denitrifying phosphate accumulating organisms (DNPAOs) and the contribution of DNPAOs to biological nutrient removal performance were investigated in a bench-scale A2O system.
101 citations
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TL;DR: In this article, a sensitive flexible piezoelectric energy harvester (FPEH) was constructed by filling a poly(vinylidene fluoride) (PVDF) polymer matrix with oriented BaTi2O5 nanorods (BT2).
101 citations
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29 Oct 2007TL;DR: This paper proposes to locate and track a drivers' mouth movement using a CCD camera to study on monitoring and recognizing a driver's yawning and shows that Gabor coefficients are more powerful than geometric features to detect yawning.
Abstract: Fatigue driving is an important reason of traffic accidents. Yawning is an evidence of driver fatigue. This paper proposes to locate and track a driver's mouth movement using a CCD camera to study on monitoring and recognizing a driver's yawning. Firstly detecting drivers' faces uses Gravity-Center template, then detecting drivers' left and right mouth corners by grey projection, and extracting texture features of drivers' mouth corners (left and right) using Gabor wavelets. Finally LDA is applied to classify feature vectors to detect yawning. The method is tested on 400 images from twenty videos. In contrast, yawning is also detected by the ratio of mouth height and width. The experiment results show that Gabor coefficients are more powerful than geometric features to detect yawning and the average recognition rate is 95% which has more than 20% improvement.
101 citations
Authors
Showing all 32228 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Pulickel M. Ajayan | 176 | 1223 | 136241 |
James M. Tour | 143 | 859 | 91364 |
Dacheng Tao | 133 | 1362 | 68263 |
Lei Zhang | 130 | 2312 | 86950 |
Hong-Cai Zhou | 114 | 489 | 66320 |
Xiaodong Li | 104 | 1300 | 49024 |
Lin Li | 104 | 2027 | 61709 |
Ming Li | 103 | 1669 | 62672 |
Wenjun Zhang | 96 | 976 | 38530 |
Lianzhou Wang | 95 | 596 | 31438 |
Miroslav Krstic | 95 | 955 | 42886 |
Zhiguo Yuan | 93 | 633 | 28645 |
Xiang Gao | 92 | 1359 | 42047 |
Xiao-yan Li | 85 | 528 | 31861 |