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Institution

Beijing University of Technology

EducationBeijing, 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 & Computer science. 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
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Journal ArticleDOI
TL;DR: In this paper, an estimated empirical log-likelihood approach to construct the confidence region of the regression parameter is developed, and an adjusted empirical loglikelihood ratio is proved to be asymptotically standard chi-square.

95 citations

Journal ArticleDOI
TL;DR: In this paper, the loss of strength of MPC in water, Na 2 SO 4 solution, and NaCl solution was studied, and the results showed that, for the same type of the samples soaked in different solutions for a given duration, the effects of solution on strength are in decreasing order: water −>−NaCl solution −> Na 2SO 4 solution − NaCl Solution.

95 citations

Journal ArticleDOI
TL;DR: In a continuous anaerobic-anoxic-aerobic (A(2)O) process treating domestic wastewater at ambient temperatures, nitritation was achieved through a combination of short aerobic actual hydraulic retention time (AHRT) and low dissolved oxygen (DO) levels.

95 citations

Book ChapterDOI
23 Aug 2020
TL;DR: This work proposes a novel network named Gait Lateral Network (GLN) which can learn both discriminative and compact representations from the silhouettes for gait recognition and leverages the inherent feature pyramid in deep convolutional neural networks to enhance the gait representations.
Abstract: Gait recognition aims at identifying different people by the walking patterns, which can be conducted at a long distance without the cooperation of subjects. A key challenge for gait recognition is to learn representations from the silhouettes that are invariant to the factors such as clothing, carrying conditions and camera viewpoints. Besides being discriminative for identification, the gait representations should also be compact for storage to keep millions of subjects registered in the gallery. In this work, we propose a novel network named Gait Lateral Network (GLN) which can learn both discriminative and compact representations from the silhouettes for gait recognition. Specifically, GLN leverages the inherent feature pyramid in deep convolutional neural networks to enhance the gait representations. The silhouette-level and set-level features extracted by different stages are merged with the lateral connections in a top-down manner. Besides, GLN is equipped with a Compact Block which can significantly reduce the dimension of the gait representations without hindering the accuracy. Extensive experiments on CASIA-B and OUMVLP show that GLN can achieve state-of-the-art performance using the 256-dimensional representations. Under the most challenging condition of walking in different clothes on CASIA-B, our method improves the rank-1 accuracy by \(6.45\%\).

95 citations

Journal ArticleDOI
TL;DR: The application of a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes provides insights for developing effective countermeasures to alleviate rear- end crash injury severities and improve traffic system safety performance.

95 citations


Authors

Showing all 32228 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Pulickel M. Ajayan1761223136241
James M. Tour14385991364
Dacheng Tao133136268263
Lei Zhang130231286950
Hong-Cai Zhou11448966320
Xiaodong Li104130049024
Lin Li104202761709
Ming Li103166962672
Wenjun Zhang9697638530
Lianzhou Wang9559631438
Miroslav Krstic9595542886
Zhiguo Yuan9363328645
Xiang Gao92135942047
Xiao-yan Li8552831861
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023124
2022611
20213,573
20203,341
20193,075
20182,523