Higher-order singular value decomposition (HOSVD) is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of the...Higher-order singular value decomposition (HOSVD) is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of them involve incomplete data. To obtain HOSVD of the data with missing values, one can first impute the missing entries through a certain tensor completion method and then perform HOSVD to the reconstructed data. However, the two-step procedure can be inefficient and does not make reliable decomposition. In this paper, we formulate an incomplete HOSVD problem and combine the two steps into solving a single optimization problem, which simultaneously achieves imputation of missing values and also tensor decomposition. We also present one algorithm for solving the problem based on block coordinate update (BCU). Global convergence of the algorithm is shown under mild assumptions and implies that of the popular higher-order orthogonality iteration (HOOI) method, and thus we, for the first time, give global convergence of HOOI. In addition, we compare the proposed method to state-of-the-art ones for solving incom- plete HOSVD and also low-rank tensor completion problems and demonstrate the superior performance of our method over other compared ones. Furthermore, we apply it to face recognition and MRI image reconstruction to show its practical performance.展开更多
The primary goal in the analysis of hierarchical distributed monitoring and control architectures is to study the spatiotemporal patterns of the interactions between areas or subsystems.In this paper,a novel conceptua...The primary goal in the analysis of hierarchical distributed monitoring and control architectures is to study the spatiotemporal patterns of the interactions between areas or subsystems.In this paper,a novel conceptual framework for distributed monitoring of power system oscillations using multiblock principal component analysis(MB-PCA)and higher-order singular value decomposition(HOSVD)is proposed to understand,characterize,and visualize the global behavior of the power system.The proposed framework can be used to evaluate the influence of a given area or utility on the oscillatory behavior,uncover low-dimensional structures from high-dimensional data,and analyze the effects of heterogeneous data on the modal characteristics and interpretation of power system.The metrics are then investigated to examine the relationships between the dynamic patterns and participation of individual data blocks in the global behavior of the system.Practical application of these techniques is demonstrated by case studies of two systems:a 14-machine test system and a 5449-bus 635-generator equivalent model of a large power system.展开更多
This paper focuses on synthesizing a mixed robust H_2/H_∞ linear parameter varying(LPV) controller for the longitudinal motion of an air-breathing hypersonic vehicle via a high order singular value decomposition(H...This paper focuses on synthesizing a mixed robust H_2/H_∞ linear parameter varying(LPV) controller for the longitudinal motion of an air-breathing hypersonic vehicle via a high order singular value decomposition(HOSVD) approach.The design of hypersonic flight control systems is highly challenging due to the enormous complexity of the vehicle dynamics and the presence of significant uncertainties.Motivated by recent results on both LPV control and tensor-product(TP) model transformation approach,the velocity and altitude tracking control problems for the air-breathing hypersonic vehicle is reduced to that of a state feedback stabilizing controller design for a polytopic LPV system with guaranteed performances.The controller implementation is converted into a convex optimization problem with parameterdependent linear matrix inequalities(LMIs) constraints,which is intuitively tractable using LMI control toolbox.Finally,numerical simulation results demonstrate the effectiveness of the proposed approach.展开更多
本文提出了一种基于波束域实值处理的高阶奇异值分解(Higher-Order Singular Value Decomposition)双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达测角算法。该算法与传统波束域测角算法不同,通过凸优化方法对发射和接...本文提出了一种基于波束域实值处理的高阶奇异值分解(Higher-Order Singular Value Decomposition)双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达测角算法。该算法与传统波束域测角算法不同,通过凸优化方法对发射和接收波束域矩阵进行优化设计,可以灵活设置波束主瓣宽度并抑制副瓣电平,发射和接收波束的主副瓣比能够得到很大提高,从而达到提高回波信噪比的目的。相比于传统的矩阵信号模型,通过HOSVD获得的张量信号子空间可以得到更高的测角精度,所提算法对发射和接收波束矩阵的结构进行设计以构造实值张量信号模型。最后,通过建立映射关系的方法对插值误差进行补偿,仿真结果验证所提算法的有效性。展开更多
针对短相干积累时间(coherent integration time,CIT)引起的多普勒分辨率低,无法从强大海杂波中检测出舰船目标的问题,提出了基于高阶奇异值分解(higher order singular value decomposition,HOSVD)的海杂波抑制算法。首先利用相邻单元...针对短相干积累时间(coherent integration time,CIT)引起的多普勒分辨率低,无法从强大海杂波中检测出舰船目标的问题,提出了基于高阶奇异值分解(higher order singular value decomposition,HOSVD)的海杂波抑制算法。首先利用相邻单元内海杂波的相干性,将毗邻距离单元和方位单元的多脉冲接收数据应用三阶张量表示,然后采用HOSVD方法求解三阶张量的海杂波子空间和目标子空间的投影矩阵,最后利用投影矩阵将三阶张量映射到目标子空间以抑制海杂波。该方法与现有子空间类海杂波抑制方法相比,提高了信干噪比(signal to clutter plus noise ratio,SCNR)和峰值旁瓣电平比(peak sidelobe level ratio,PSLR),解决了目标谱峰偏移问题。展开更多
针对极化敏感阵列信号波达方向(direction of arrival,DOA)估计问题,提出了一种基于塔克张量域序贯截断高阶奇异值分解的正则极化旋转不变参数估计(Tucker tensor based regularized polarimetric estimation of signal parameters via ...针对极化敏感阵列信号波达方向(direction of arrival,DOA)估计问题,提出了一种基于塔克张量域序贯截断高阶奇异值分解的正则极化旋转不变参数估计(Tucker tensor based regularized polarimetric estimation of signal parameters via rotational invariance technique,trpESPRIT)方法。首先对阵列接收信号进行塔克张量建模,之后通过序贯截断高阶奇异值分解获得塔克张量域信号子空间,最后利用多旋转不变子空间幅相关系获得信号DOA估计。相比于传统矩阵建模方法,塔克张量建模更便于组织多维数据结构,实现高维的数据匹配操作,而序贯截断高阶奇异值分解则可以获得更高的信号子空间估计精度以及后续的DOA估计。仿真结果表明,trpESPRIT方法较之常规矩阵方法和矢量方法可以更好地抑制噪声,具有更高的信号DOA估计精度,在低信噪比和低快拍条件下仍然具有良好的分辨能力。展开更多
A new method to extract person-independent expression feature based on higher-order singular value decomposition (HOSVD) is proposed for facial expression recognition. Based on the assumption that similar persons ha...A new method to extract person-independent expression feature based on higher-order singular value decomposition (HOSVD) is proposed for facial expression recognition. Based on the assumption that similar persons have similar facial expression appearance and shape, the person-similarity weighted expression feature is proposed to estimate the expression feature of test persons. As a result, the estimated expression feature can reduce the influence of individuals caused by insufficient training data, and hence become less person-dependent. The proposed method is tested on Cohn-Kanade facial expression database and Japanese female facial expression (JAFFE) database. Person-independent experimental results show the superiority of the proposed method over the existing methods.展开更多
文摘Higher-order singular value decomposition (HOSVD) is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of them involve incomplete data. To obtain HOSVD of the data with missing values, one can first impute the missing entries through a certain tensor completion method and then perform HOSVD to the reconstructed data. However, the two-step procedure can be inefficient and does not make reliable decomposition. In this paper, we formulate an incomplete HOSVD problem and combine the two steps into solving a single optimization problem, which simultaneously achieves imputation of missing values and also tensor decomposition. We also present one algorithm for solving the problem based on block coordinate update (BCU). Global convergence of the algorithm is shown under mild assumptions and implies that of the popular higher-order orthogonality iteration (HOOI) method, and thus we, for the first time, give global convergence of HOOI. In addition, we compare the proposed method to state-of-the-art ones for solving incom- plete HOSVD and also low-rank tensor completion problems and demonstrate the superior performance of our method over other compared ones. Furthermore, we apply it to face recognition and MRI image reconstruction to show its practical performance.
文摘The primary goal in the analysis of hierarchical distributed monitoring and control architectures is to study the spatiotemporal patterns of the interactions between areas or subsystems.In this paper,a novel conceptual framework for distributed monitoring of power system oscillations using multiblock principal component analysis(MB-PCA)and higher-order singular value decomposition(HOSVD)is proposed to understand,characterize,and visualize the global behavior of the power system.The proposed framework can be used to evaluate the influence of a given area or utility on the oscillatory behavior,uncover low-dimensional structures from high-dimensional data,and analyze the effects of heterogeneous data on the modal characteristics and interpretation of power system.The metrics are then investigated to examine the relationships between the dynamic patterns and participation of individual data blocks in the global behavior of the system.Practical application of these techniques is demonstrated by case studies of two systems:a 14-machine test system and a 5449-bus 635-generator equivalent model of a large power system.
基金supported by the National Natural Science Foundation of China(6120300761304239+1 种基金61503392)the Natural Science Foundation of Shaanxi Province(2015JQ6213)
文摘This paper focuses on synthesizing a mixed robust H_2/H_∞ linear parameter varying(LPV) controller for the longitudinal motion of an air-breathing hypersonic vehicle via a high order singular value decomposition(HOSVD) approach.The design of hypersonic flight control systems is highly challenging due to the enormous complexity of the vehicle dynamics and the presence of significant uncertainties.Motivated by recent results on both LPV control and tensor-product(TP) model transformation approach,the velocity and altitude tracking control problems for the air-breathing hypersonic vehicle is reduced to that of a state feedback stabilizing controller design for a polytopic LPV system with guaranteed performances.The controller implementation is converted into a convex optimization problem with parameterdependent linear matrix inequalities(LMIs) constraints,which is intuitively tractable using LMI control toolbox.Finally,numerical simulation results demonstrate the effectiveness of the proposed approach.
文摘本文提出了一种基于波束域实值处理的高阶奇异值分解(Higher-Order Singular Value Decomposition)双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达测角算法。该算法与传统波束域测角算法不同,通过凸优化方法对发射和接收波束域矩阵进行优化设计,可以灵活设置波束主瓣宽度并抑制副瓣电平,发射和接收波束的主副瓣比能够得到很大提高,从而达到提高回波信噪比的目的。相比于传统的矩阵信号模型,通过HOSVD获得的张量信号子空间可以得到更高的测角精度,所提算法对发射和接收波束矩阵的结构进行设计以构造实值张量信号模型。最后,通过建立映射关系的方法对插值误差进行补偿,仿真结果验证所提算法的有效性。
文摘针对短相干积累时间(coherent integration time,CIT)引起的多普勒分辨率低,无法从强大海杂波中检测出舰船目标的问题,提出了基于高阶奇异值分解(higher order singular value decomposition,HOSVD)的海杂波抑制算法。首先利用相邻单元内海杂波的相干性,将毗邻距离单元和方位单元的多脉冲接收数据应用三阶张量表示,然后采用HOSVD方法求解三阶张量的海杂波子空间和目标子空间的投影矩阵,最后利用投影矩阵将三阶张量映射到目标子空间以抑制海杂波。该方法与现有子空间类海杂波抑制方法相比,提高了信干噪比(signal to clutter plus noise ratio,SCNR)和峰值旁瓣电平比(peak sidelobe level ratio,PSLR),解决了目标谱峰偏移问题。
文摘针对极化敏感阵列信号波达方向(direction of arrival,DOA)估计问题,提出了一种基于塔克张量域序贯截断高阶奇异值分解的正则极化旋转不变参数估计(Tucker tensor based regularized polarimetric estimation of signal parameters via rotational invariance technique,trpESPRIT)方法。首先对阵列接收信号进行塔克张量建模,之后通过序贯截断高阶奇异值分解获得塔克张量域信号子空间,最后利用多旋转不变子空间幅相关系获得信号DOA估计。相比于传统矩阵建模方法,塔克张量建模更便于组织多维数据结构,实现高维的数据匹配操作,而序贯截断高阶奇异值分解则可以获得更高的信号子空间估计精度以及后续的DOA估计。仿真结果表明,trpESPRIT方法较之常规矩阵方法和矢量方法可以更好地抑制噪声,具有更高的信号DOA估计精度,在低信噪比和低快拍条件下仍然具有良好的分辨能力。
基金supported by National Natural Science Foundation of China (6087208460940008)+2 种基金Beijing Training Programming Foundation for the Talents (20081D1600300343)Excellent Young Scholar Research Fund of Beijing Institute of Technology (2007Y0305)Fundamental Research Foundation of Beijing Institute of Technology (20080342005)
文摘A new method to extract person-independent expression feature based on higher-order singular value decomposition (HOSVD) is proposed for facial expression recognition. Based on the assumption that similar persons have similar facial expression appearance and shape, the person-similarity weighted expression feature is proposed to estimate the expression feature of test persons. As a result, the estimated expression feature can reduce the influence of individuals caused by insufficient training data, and hence become less person-dependent. The proposed method is tested on Cohn-Kanade facial expression database and Japanese female facial expression (JAFFE) database. Person-independent experimental results show the superiority of the proposed method over the existing methods.