A novel and efficient method for decomposing a signal into a set of intrinsic mode functions （IMFs） and a trend is proposed. Unlike the original empirical mode decomposition （EMD）, which uses spline fits to extrac...A novel and efficient method for decomposing a signal into a set of intrinsic mode functions （IMFs） and a trend is proposed. Unlike the original empirical mode decomposition （EMD）, which uses spline fits to extract variations from the signal by separating the local mean from the fluctuations in the decomposing process, this new method being proposed takes advantage of the theory of variable finite impulse response （FIR） filtering where filter coefficients and breakpoint frequencies can be adjusted to track any peak-to-peak time scale changes. The IMFs are results of a multiple variable frequency response FIR filtering when signals pass through the filters. Numerical examples validate that in contrast with the original EMD, the proposed method can fine-tune the frequency resolution and suppress the aliasing effectively.展开更多
随着西藏地区旅游业的蓬勃发展,人群荷载对藏式古建筑结构安全的影响日益显著。为了量化人群荷载对结构的影响,需要从结构健康监测数据中分离出人群荷载引起的应变响应,提出了一种基于灰狼优化(grey wolf optimizer,GWO)算法优化变分模...随着西藏地区旅游业的蓬勃发展,人群荷载对藏式古建筑结构安全的影响日益显著。为了量化人群荷载对结构的影响,需要从结构健康监测数据中分离出人群荷载引起的应变响应,提出了一种基于灰狼优化(grey wolf optimizer,GWO)算法优化变分模态分解(variational mode decomposition,VMD)算法并结合GG(Gath-Geva,GG)聚类算法的人群荷载效应分离方法,简称GWO-VMD-GG。首先,利用GWO算法以最小包络熵为适应度函数来确定VMD参数模态分解层数K和二次惩罚因子α;其次,采用优化后的VMD算法对实测应变信号进行分解;最后,以相关系数为特征参数,采用GG聚类算法对分解得到的本征模态函数(intrinsic mode function,IMF)分量进行聚类,将快变应变分量重构,得到人群荷载引起的应变响应。简述了某藏式古建筑游客分布特征,并通过对藏式古建筑木结构应变监测数据的分析,成功分离出游客日、周、年分布特征人群荷载效应,验证了所提方法在工程实践中的有效性。结果表明,该方法能够有效避免VMD参数选择和IMF分量划分过程中的人为干预,实现从大规模监测数据中自动分离人群荷载效应,为藏式古建筑的结构安全评估提供了一种有效手段。展开更多
基金supported by the National Natural Science Foundation of China (60472021).
文摘A novel and efficient method for decomposing a signal into a set of intrinsic mode functions （IMFs） and a trend is proposed. Unlike the original empirical mode decomposition （EMD）, which uses spline fits to extract variations from the signal by separating the local mean from the fluctuations in the decomposing process, this new method being proposed takes advantage of the theory of variable finite impulse response （FIR） filtering where filter coefficients and breakpoint frequencies can be adjusted to track any peak-to-peak time scale changes. The IMFs are results of a multiple variable frequency response FIR filtering when signals pass through the filters. Numerical examples validate that in contrast with the original EMD, the proposed method can fine-tune the frequency resolution and suppress the aliasing effectively.
