为解决危大工程中吊装作业安全管理的问题,基于深度学习构建目标检测算法(You Only Look Once version 5,YOLOv5)网络模型,针对进入吊装作业区域内人员的防护装备进行多目标融合检测,并对吊钩在施工过程中的状态进行检测。在原始的检测...为解决危大工程中吊装作业安全管理的问题,基于深度学习构建目标检测算法(You Only Look Once version 5,YOLOv5)网络模型,针对进入吊装作业区域内人员的防护装备进行多目标融合检测,并对吊钩在施工过程中的状态进行检测。在原始的检测网络模型中引入4种注意力机制,并通过5种训练模型的结果对比分析,进而选择卷积块注意力模块(Convolutional Block Attention Module,CBAM)最优模型。优化后的检测模型对安全帽的平均识别精度达86.5%,对反光衣的平均识别精度达83.0%,对吊钩的状态识别精度达92.0%。将训练好的人员检测模型和吊钩检测模型打包成exe执行文件,应用到施工安全管理人员的中控平台,可帮助管理人员更好地判断吊装作业的工作情况,进而及时进行风险管控。展开更多
We performed a multifractal analysis using wavelet transform to detect the changes in the fractality of the USD/JPY and EUR/JPY exchange rates, and predicted their extreme values using extreme value theory. After the ...We performed a multifractal analysis using wavelet transform to detect the changes in the fractality of the USD/JPY and EUR/JPY exchange rates, and predicted their extreme values using extreme value theory. After the 1997 Asian financial crisis, the USD/JPY and EUR/JPY became multifractal, then the USD/JPY became monofractal and stable, and yen depreciation was observed. However, the EUR/JPY became multifractal and unstable, and a strong yen depreciation was observed. The coherence between the USD/JPY and EUR/JPY was strong between 1995 and 2000. After the 2007-2008 financial crisis, the USD/JPY became monofractal and stable, and yen appreciation was observed. However, the EUR/JPY became multifractal and unstable, and strong yen appreciation was observed. Various diagnostic plots for assessing the accuracy of the GP model fitted to USD/JPY and EUR/JPY are shown, and all the diagnostic plots support the fitted GP model. The shape parameters of USD/JPY and EUR/JPY were close to zero, therefore the USD/JPY and EUR/JPY did not have finite upper limits. We predicted the maximum return level for the return periods of 10, 20, 50, 100, 350, and 500 years and their respective 95% confidence intervals (CI). As a result, the 10-year and 100-year return levels for USD/JPY were estimated to be 149.6 and 164.8, with 95% CI [143.2, 156.0] and [149.4, 180.1], respectively.展开更多
文摘为解决危大工程中吊装作业安全管理的问题,基于深度学习构建目标检测算法(You Only Look Once version 5,YOLOv5)网络模型,针对进入吊装作业区域内人员的防护装备进行多目标融合检测,并对吊钩在施工过程中的状态进行检测。在原始的检测网络模型中引入4种注意力机制,并通过5种训练模型的结果对比分析,进而选择卷积块注意力模块(Convolutional Block Attention Module,CBAM)最优模型。优化后的检测模型对安全帽的平均识别精度达86.5%,对反光衣的平均识别精度达83.0%,对吊钩的状态识别精度达92.0%。将训练好的人员检测模型和吊钩检测模型打包成exe执行文件,应用到施工安全管理人员的中控平台,可帮助管理人员更好地判断吊装作业的工作情况,进而及时进行风险管控。
文摘We performed a multifractal analysis using wavelet transform to detect the changes in the fractality of the USD/JPY and EUR/JPY exchange rates, and predicted their extreme values using extreme value theory. After the 1997 Asian financial crisis, the USD/JPY and EUR/JPY became multifractal, then the USD/JPY became monofractal and stable, and yen depreciation was observed. However, the EUR/JPY became multifractal and unstable, and a strong yen depreciation was observed. The coherence between the USD/JPY and EUR/JPY was strong between 1995 and 2000. After the 2007-2008 financial crisis, the USD/JPY became monofractal and stable, and yen appreciation was observed. However, the EUR/JPY became multifractal and unstable, and strong yen appreciation was observed. Various diagnostic plots for assessing the accuracy of the GP model fitted to USD/JPY and EUR/JPY are shown, and all the diagnostic plots support the fitted GP model. The shape parameters of USD/JPY and EUR/JPY were close to zero, therefore the USD/JPY and EUR/JPY did not have finite upper limits. We predicted the maximum return level for the return periods of 10, 20, 50, 100, 350, and 500 years and their respective 95% confidence intervals (CI). As a result, the 10-year and 100-year return levels for USD/JPY were estimated to be 149.6 and 164.8, with 95% CI [143.2, 156.0] and [149.4, 180.1], respectively.
