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- 저자 : Zi-xue Yin (검색결과 2 건)
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Guo-dong Cai,Liang Chen,Yao-zong Teng,Zi-xue Yin,Zhe Zhang 대한토목학회 2023 KSCE JOURNAL OF CIVIL ENGINEERING Vol.27 No.2
Internal erosion is one of the important causes of soil damage. In this paper, an erosion-stress coupling apparatus was developed to investigate the mechanical property of soil after internal erosion. Main efforts were devoted to rebuilding pressure cell so that soils can be eroded under confining pressure and deviator pressure. The hydraulic gradient is controlled by using the water-head control method which can provide up to 40 m water-head. The volume change of the sample can also be accurately measured by the volume controller. The eroded particles were collected by the eroded soil collection system. Triaxial consolidation drained shear tests after internal erosion were performed on gap graded soils. It was found that the drainage failure strength and drainage internal friction angle of soil after internal erosion under confining pressure decreased with the increase of fine particle loss. Based on the experimental data, amodified Duncan-Chang model considering fine particle loss was introduced. Numerical modelling of vertical displacement of dam was carried out by using the modified Duncan-Chang model, and it was found that when the fine particle loss exceeds 10%, the displacement increases significantly.
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Zhou Qing-Qing,Wang Jiashuo,Tang Wen,Hu Zhang-Chun,Xia Zi-Yi,Xue-Song Li,Zhang Rongguo,Yin Xindao,Zhang Bing,Zhang Hong 대한영상의학회 2020 Korean Journal of Radiology Vol.21 No.7
Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists’ workload.
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