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: 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
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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
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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
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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
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05 Mar 2017TL;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
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 |