风向作为风荷载变异性的关键因素之一,在极端风速预测中不容忽视。基于安徽省67个气象站点1980—2020年间的历史日极值风速和风向数据,分别对风速和风向进行了概率分布建模分析。结合最优边际分布,采用3种常用的阿基米德Copula函数建立...风向作为风荷载变异性的关键因素之一,在极端风速预测中不容忽视。基于安徽省67个气象站点1980—2020年间的历史日极值风速和风向数据,分别对风速和风向进行了概率分布建模分析。结合最优边际分布,采用3种常用的阿基米德Copula函数建立了风速和风向的二维联合分布模型。最后结合Copula函数的条件概率理论,给出了全省范围内16个风向上50年重现期下的设计风速区划。研究结果表明,Gumbel分布和三阶von Mises分布适用于全省绝大多数站点风速和风向的边际分布。根据赤池信息准则(Akaike information criterion,AIC)可以发现,对于大多数气象站点的二维联合分布,Frank-Copula函数拟合误差最小。各风向下的极值风速大小存在显著差异,忽略风向的极值风速预测,将会导致计算结果不准确。展开更多
Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were col...Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.展开更多
文摘风向作为风荷载变异性的关键因素之一,在极端风速预测中不容忽视。基于安徽省67个气象站点1980—2020年间的历史日极值风速和风向数据,分别对风速和风向进行了概率分布建模分析。结合最优边际分布,采用3种常用的阿基米德Copula函数建立了风速和风向的二维联合分布模型。最后结合Copula函数的条件概率理论,给出了全省范围内16个风向上50年重现期下的设计风速区划。研究结果表明,Gumbel分布和三阶von Mises分布适用于全省绝大多数站点风速和风向的边际分布。根据赤池信息准则(Akaike information criterion,AIC)可以发现,对于大多数气象站点的二维联合分布,Frank-Copula函数拟合误差最小。各风向下的极值风速大小存在显著差异,忽略风向的极值风速预测,将会导致计算结果不准确。
文摘Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.
