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Method to Appraise Dangerous Class of Building Masonry Component Based on DC-YOLO Model
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作者 Hongrui Zhang Wenxue Wei +2 位作者 Xinguang Xiao Song Yang Wanlu Shao 《Computers, Materials & Continua》 SCIE EI 2020年第4期457-468,共12页
This DC-YOLO Model was designed in order to improve the efficiency for appraising dangerous class of buildings and avoid manual intervention,thereby making the appraisal results more objective.It is an automated metho... This DC-YOLO Model was designed in order to improve the efficiency for appraising dangerous class of buildings and avoid manual intervention,thereby making the appraisal results more objective.It is an automated method designed based on deep learning and target detection algorithms to appraise the dangerous class of building masonry component.Specifically,it(1)adopted K-means clustering to obtain the quantity and size of the prior boxes;(2)expanded the grid size to improve identification to small targets;(3)introduced in deformable convolution to adapt to the irregular shape of the masonry component cracks.The experimental results show that,comparing with the conventional method,the DC-YOLO model has better recognition rates for various targets to different extents,and achieves good effects in precision,recall rate and F1 value,which indicates the good performance in classifying dangerous classes of building masonry component. 展开更多
关键词 Deep learning masonry component appraisal of dangerous class deformable convolution
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Fire risk evaluation research on fully mechanized coalface based on the uncertainty measure theory 被引量:1
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作者 JIA Hai-lin YU Ming-gao Chang Xu-hua 《Journal of Coal Science & Engineering(China)》 2010年第2期157-162,共6页
A relatively perfect coalmine fire risk-evaluating and order-arranging model that includes sixteen influential factors was established according to the statistical information of the fully mechanized coalface ground o... A relatively perfect coalmine fire risk-evaluating and order-arranging model that includes sixteen influential factors was established according to the statistical information of the fully mechanized coalface ground on the uncertainty measure theory. Then the single-index measure function of sixteen influential factors and the calculation method of computing the index weight ground on entropy theory were respectively established. The value assignment of sixteen influential factors was carried out by the qualitative analysis and observational data, respectively, in succession. The sequence of fire danger class of four experimental coalfaces could be obtained by the computational aids of Matlab according to the confidence level criterion. Some conclusions that the fire danger class of the No.l, No.2 and No.3 coalface belongs to high criticality can be obtained. But the fire danger class of the No.4 coalface belongs to higher criticality. The fire danger class of the No.4 coalface is more than that of the No.2 coalface. The fire danger class of the No.2 coalface is more than that of the No.1 coalface. Finally, the fire danger class of the No.1 coalface is more than that of the No.3 coalface. 展开更多
关键词 fully mechanized coalface fire risk evaluation uncertainty measure single-index measure function sequence of fire danger class
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