Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovas...Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovascular information,and their data acquisition devices are quiteconvenient.In this study,a novel hybrid approach of fractional Fourier transform(FRFT)com-bined with linear and discrete wavelet transform(DWT)features extracted from PCG is proposedfor PCG multi-class classification.The proposed system enhances the fatigue detection performanceby combining optimized FRFT features with an effective aggregation of linear features and DWTfeatures.The FRFT technique is employed to convert the 1-D PCG signal into 2-D image which issent to a pre-trained convolutional neural network structure,called VGG-16.The features from theVGG-16 were concatenated with the linear and DWT features to form fused features.The fusedfeatures are sent to support vector machine(SVM)to distinguish six distinct fatigue levels.Experi-mental results demonstrate that the proposed fused features outperform other feature combinationssignificantly.展开更多
基于EBE-PCG(element by element-preconditioned conjugate gradient)策略的并行算法不用形成总体刚度矩阵,而且无需进行三维模型的区域分解,从而提高了并行计算的速度和效率,是实现协同优化设计的性能函数快速分析技术的有效途径。文...基于EBE-PCG(element by element-preconditioned conjugate gradient)策略的并行算法不用形成总体刚度矩阵,而且无需进行三维模型的区域分解,从而提高了并行计算的速度和效率,是实现协同优化设计的性能函数快速分析技术的有效途径。文中详细介绍有限元EBE(element by element)的运算方法,给出EBE-PCG并行算法的实现步骤,最后在网络集群环境下,综合运用多种编程语言和分析工具,实现基于EBE-PCG策略的三维有限元并行计算。计算结果表明,该并行算法的计算误差小,并行效率高,适合于性能函数的快速求解。展开更多
基金the National Natural Sci-ence Foundation of China(No.62301056)the Fundamental Research Funds for Central Universities(No.2022QN005).
文摘Accurate detection of exercise fatigue based on physiological signals is vital for reason-able physical activity.As a non-invasive technology,phonocardiogram(PCG)signals possess arobust capability to reflect cardiovascular information,and their data acquisition devices are quiteconvenient.In this study,a novel hybrid approach of fractional Fourier transform(FRFT)com-bined with linear and discrete wavelet transform(DWT)features extracted from PCG is proposedfor PCG multi-class classification.The proposed system enhances the fatigue detection performanceby combining optimized FRFT features with an effective aggregation of linear features and DWTfeatures.The FRFT technique is employed to convert the 1-D PCG signal into 2-D image which issent to a pre-trained convolutional neural network structure,called VGG-16.The features from theVGG-16 were concatenated with the linear and DWT features to form fused features.The fusedfeatures are sent to support vector machine(SVM)to distinguish six distinct fatigue levels.Experi-mental results demonstrate that the proposed fused features outperform other feature combinationssignificantly.
文摘基于EBE-PCG(element by element-preconditioned conjugate gradient)策略的并行算法不用形成总体刚度矩阵,而且无需进行三维模型的区域分解,从而提高了并行计算的速度和效率,是实现协同优化设计的性能函数快速分析技术的有效途径。文中详细介绍有限元EBE(element by element)的运算方法,给出EBE-PCG并行算法的实现步骤,最后在网络集群环境下,综合运用多种编程语言和分析工具,实现基于EBE-PCG策略的三维有限元并行计算。计算结果表明,该并行算法的计算误差小,并行效率高,适合于性能函数的快速求解。
