供水管网存在大量分支接头,流体在分支接头的分流作用下产生支管流致噪声,并通过管道与泄漏声信号进行耦合。针对支管流致噪声存在下的供水管道泄漏定位问题。提出一种基于高效快速独立主成分分析(efficient fast independent component...供水管网存在大量分支接头,流体在分支接头的分流作用下产生支管流致噪声,并通过管道与泄漏声信号进行耦合。针对支管流致噪声存在下的供水管道泄漏定位问题。提出一种基于高效快速独立主成分分析(efficient fast independent component analysis,EFastICA)技术的复值域高效快速独立主成分分析(complex efficient fast independent component analysis,C-EFastICA)技术,该算法将时域瞬时线性EFastICA技术的代价函数、约束函数、迭代规则等有效地扩展到复数域,实现对含支管流致噪声的泄漏声信号分解处理。与其他主成分分析(independent component analysis,ICA)类算法固定选择非线性函数不同的是,C-EFastICA根据声信号的广义高斯性特征,自适应地选择非线性函数建立代价函数和迭代学习规则,使得算法对混合信号的分离程度更高。试验结果表明,泄漏信号和支管流致噪声均是超高斯信号,经C-EFastICA分解得到的源泄漏信号对漏点的定位相对误差低于12%,低于传统同类的C-FastICA技术。展开更多
In order to facilitate the extraction of the default mode network（DMN）, reduce the data complexity of the functional magnetic resonance imaging （fMRI）and overcome the restriction of the linearity of the mixing pro...In order to facilitate the extraction of the default mode network（DMN）, reduce the data complexity of the functional magnetic resonance imaging （fMRI）and overcome the restriction of the linearity of the mixing process encountered with the independent component analysis（ICA）, a framework of dimensionality reduction and nonlinear transformation is proposed. First, the principal component analysis（PCA）is applied to reduce the time dimension 153 594×128 of the fMRI data to 153 594×5 for simplifying complexity computation and obtaining 95% of the information. Secondly, a new kernel-based nonlinear ICA method referred as the kernel ICA（KICA）based on the Gaussian kernel is introduced to analyze the resting-state fMRI data and extract the DMN. Experimental results show that the KICA provides a better performance for the resting-state fMRI data analysis compared with the classical ICA. Furthermore, the DMN is accurately extracted and the noise is reduced.展开更多
Independent component analysis （ICA） is a widely used method for blind source separation （BSS）. The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to colle...Independent component analysis （ICA） is a widely used method for blind source separation （BSS）. The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis （PCA）, the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches.展开更多
实际工业过程中往往包含不同运行工况,且每种工况数据一般不服从同一种分布.数据的多分布性和分布的不确定性使得传统的故障诊断方法难以获得满意的效果,因此提出一种基于局部邻域和贝叶斯推断的多工况故障诊断方法.首先,通过局部邻域...实际工业过程中往往包含不同运行工况,且每种工况数据一般不服从同一种分布.数据的多分布性和分布的不确定性使得传统的故障诊断方法难以获得满意的效果,因此提出一种基于局部邻域和贝叶斯推断的多工况故障诊断方法.首先,通过局部邻域标准化算法对多工况数据进行预处理;再利用ICA-PCA(independent component analysis and principal component analysis)方法分别对该数据集的高斯特性和非高斯特性进行分析处理,获得全局模型;然后结合贝叶斯推断将多个统计量组合成一个监测统计量,实现多工况过程的在线监测;最后通过数值例子和TE过程的仿真研究,验证了提出方法的可行性和有效性.展开更多
基金Key Academic Discipline during the11th Five-Year Plan Period of Jiangsu Province
文摘In order to facilitate the extraction of the default mode network（DMN）, reduce the data complexity of the functional magnetic resonance imaging （fMRI）and overcome the restriction of the linearity of the mixing process encountered with the independent component analysis（ICA）, a framework of dimensionality reduction and nonlinear transformation is proposed. First, the principal component analysis（PCA）is applied to reduce the time dimension 153 594×128 of the fMRI data to 153 594×5 for simplifying complexity computation and obtaining 95% of the information. Secondly, a new kernel-based nonlinear ICA method referred as the kernel ICA（KICA）based on the Gaussian kernel is introduced to analyze the resting-state fMRI data and extract the DMN. Experimental results show that the KICA provides a better performance for the resting-state fMRI data analysis compared with the classical ICA. Furthermore, the DMN is accurately extracted and the noise is reduced.
基金supported by National Natural Science Foundation(No.60875061)
文摘Independent component analysis （ICA） is a widely used method for blind source separation （BSS）. The mature ICA model has a restriction that the number of the sources must equal to that of the sensors used to collect data, which is hard to meet in most practical cases. In this paper, an overdetermined ICA method is proposed and successfully used in the analysis of human colonic pressure signals. Using principal component analysis （PCA）, the method estimates the number of the sources firstly and reduces the dimensions of the observed signals to the same with that of the sources; and then, Fast- ICA is used to estimate all the sources. From 26 groups of colonic pressure recordings, several colonic motor patterns are extracted, which riot only prove the effectiveness of this method, but also greatly facilitate further medical researches.
文摘实际工业过程中往往包含不同运行工况,且每种工况数据一般不服从同一种分布.数据的多分布性和分布的不确定性使得传统的故障诊断方法难以获得满意的效果,因此提出一种基于局部邻域和贝叶斯推断的多工况故障诊断方法.首先,通过局部邻域标准化算法对多工况数据进行预处理;再利用ICA-PCA(independent component analysis and principal component analysis)方法分别对该数据集的高斯特性和非高斯特性进行分析处理,获得全局模型;然后结合贝叶斯推断将多个统计量组合成一个监测统计量,实现多工况过程的在线监测;最后通过数值例子和TE过程的仿真研究,验证了提出方法的可行性和有效性.
