scispace - formally typeset
Search or ask a question
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 & 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
More filters
Journal ArticleDOI
TL;DR: In this article, a new approach combining the environment monitoring, model simulation and source apportionment methods was proposed to investigate the impact of vehicular emissions on the PM 2.5 pollution.

87 citations

Journal ArticleDOI
TL;DR: In this paper, a design-oriented model was developed to define axial stiffness using the jacket stiffness rather than its rupture strain, which is applicable to both conventional and large rupture strain fiber-reinforced polymers (LRS FRP)-confined concrete columns.

87 citations

Journal ArticleDOI
Weiqin Chu1, Xu Zhang1, Jie Wang1, Shu Zhao1, Shiqi Liu1, Haijun Yu1 
TL;DR: In this article, a low-cost deep eutectic solvent, i.e., AlCl3/acetamide, as the electrolyte for reversible room-temperature Al-S battery has been reported.

87 citations

Journal ArticleDOI
TL;DR: In this paper, the microstructure and crystallography of eutectic borides and secondary precipitations in 18.5% C steel have been investigated extensively, and the results show that the as-cast micro-structure of Cr-Ni-Mo-containing Fe-B steel is composed of a dendritic martensite with large interdendritic eutectoric borsides.

86 citations

Proceedings ArticleDOI
05 Mar 2017
TL;DR: A feature extraction method for ultrasound images based on the convolution neural networks (CNNs), try to introduce more meaningful semantic features to the classification.
Abstract: Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound images based on the convolution neural networks (CNNs), try to introduce more meaningful semantic features to the classification. Firstly, a CNN model trained with a massive natural dataset is transferred to the ultrasound image domain, to generate semantic deep features and handle the small sample problem. Then, we combine those deep features with conventional features such as Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP) together, to form a hybrid feature space. Finally, a positive-samplefirst majority voting and a feature-selected based strategy are employed for the hybrid classification. Experimental results on 1037 images show that the accuracy of our proposed method is 0.931, which outperformed other relative methods by over 10%.

86 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
Network Information
Related Institutions (5)
Harbin Institute of Technology
109.2K papers, 1.6M citations

95% related

Tsinghua University
200.5K papers, 4.5M citations

94% related

University of Science and Technology of China
101K papers, 2.4M citations

92% related

Northeastern University
58.1K papers, 1.7M citations

91% related

Zhejiang University
183.2K papers, 3.4M citations

91% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023124
2022611
20213,573
20203,341
20193,075
20182,523