The derivation of a diagonally loaded sample-matrix inversion （LSMI） algorithm on the busis of inverse matrix recursion （i.e.LSMI-IMR algorithm） is conducted by reconstructing the recursive formulation of covarian...The derivation of a diagonally loaded sample-matrix inversion （LSMI） algorithm on the busis of inverse matrix recursion （i.e.LSMI-IMR algorithm） is conducted by reconstructing the recursive formulation of covariance matrix. For the new algorithm, diagonal loading is by setting initial inverse matrix without any addition of computation. In addition, a corresponding improved recursive algorithm is presented, which is low computational complexity. This eliminates the complex multiplications of the scalar coefficient and updating matrix, resulting in significant computational savings. Simulations show that the LSMI-IMR algorithm is valid.展开更多
In order to enhance the robustness and contrast in the minimum variance(MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamfo...In order to enhance the robustness and contrast in the minimum variance(MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.展开更多
精准的电力负荷预测有利于保障电力系统的安全、经济运行。针对现行预测算法存在的预测准确度低、模型耗时长等问题,提出一种基于随机森林(random forest,RF)算法和粗糙集理论(rough set theory,RST)的改进型深度学习(deeplearning, DL...精准的电力负荷预测有利于保障电力系统的安全、经济运行。针对现行预测算法存在的预测准确度低、模型耗时长等问题,提出一种基于随机森林(random forest,RF)算法和粗糙集理论(rough set theory,RST)的改进型深度学习(deeplearning, DL)短期负荷预测模型(RF-DL-RST)。该模型首先基于历史数据,利用随机森林算法提取影响负荷预测的关键特征量;然后将关键特征量和历史负荷值作为深度神经网络的输入、输出项进行训练,并通过粗糙集理论修正预测结果。最后,通过算例进行仿真验证,结果表明,该模型的预测准确度比单一的深度学习模型及不进行预测修正的模型更高。展开更多
A source enumeration method based on diagonal loading of eigenvalues and constructing second-order statistics is proposed,for the case that the antenna array observed signals are overlapped with spatial colored noise,...A source enumeration method based on diagonal loading of eigenvalues and constructing second-order statistics is proposed,for the case that the antenna array observed signals are overlapped with spatial colored noise,and the number of antennas compared with the number of snapshots meet the requirement of general asymptotic regime.Firstly,the sample covariance matrix of the observed signals is obtained,the eigenvalues of the sample covariance matrix can be acquired by eigenvalue decomposition,and the eigenvalues are diagonally loaded,and a new formula for calculating the diagonal loading is presented.Based on the diagonal loaded eigenvalues,the difference values are calculated for the adjacent eigenvalues after diagonal loading,and the statistical variance of the difference values is calculated.On this basis,the second-order statistics of the difference values are constructed,and when the second-order statistics are minimized,the corresponding number of sources is estimated.The proposed method has wide applicability,which is suitable for both general asymptotic regime and classical asymptotic system,and is suitable for both white Gaussian noise environment and colored noise environment.The method makes up for the lack of source enumeration methods in the case of general asymptotic system and colored noise.展开更多
文摘The derivation of a diagonally loaded sample-matrix inversion （LSMI） algorithm on the busis of inverse matrix recursion （i.e.LSMI-IMR algorithm） is conducted by reconstructing the recursive formulation of covariance matrix. For the new algorithm, diagonal loading is by setting initial inverse matrix without any addition of computation. In addition, a corresponding improved recursive algorithm is presented, which is low computational complexity. This eliminates the complex multiplications of the scalar coefficient and updating matrix, resulting in significant computational savings. Simulations show that the LSMI-IMR algorithm is valid.
基金Project(2013GZX0147-3)supported by the Science and Technology Pillar Program of Sichuan Province,China
文摘In order to enhance the robustness and contrast in the minimum variance(MV) beamformer, adaptive diagonal loading method was proposed. The conventional diagonal loading technique has already been used in the MV beamformer, but has the drawback that its level is specified by predefined parameter and without consideration of input-data. To alleviate this problem, the level of diagonal loading was computed appropriately and automatically from the given data by shrinkage method in the proposed adaptive diagonal loaded beamformer. The performance of the proposed beamformer was tested on the simulated point target and cyst phantom was obtained using Field II. In the point target simulation, it is shown that the proposed method has higher lateral resolution than the conventional delay-and-sum beamformer and could be more robust in estimating the amplitude peak than the MV beamformer when acoustic velocity error exists. In the cyst phantom simulation, the proposed beamformer has shown that it achieves an improvement in contrast ratio and without distorting the edges of cyst.
文摘精准的电力负荷预测有利于保障电力系统的安全、经济运行。针对现行预测算法存在的预测准确度低、模型耗时长等问题,提出一种基于随机森林(random forest,RF)算法和粗糙集理论(rough set theory,RST)的改进型深度学习(deeplearning, DL)短期负荷预测模型(RF-DL-RST)。该模型首先基于历史数据,利用随机森林算法提取影响负荷预测的关键特征量;然后将关键特征量和历史负荷值作为深度神经网络的输入、输出项进行训练,并通过粗糙集理论修正预测结果。最后,通过算例进行仿真验证,结果表明,该模型的预测准确度比单一的深度学习模型及不进行预测修正的模型更高。
基金supported by the Natural Science Foundation of China(61801480)。
文摘A source enumeration method based on diagonal loading of eigenvalues and constructing second-order statistics is proposed,for the case that the antenna array observed signals are overlapped with spatial colored noise,and the number of antennas compared with the number of snapshots meet the requirement of general asymptotic regime.Firstly,the sample covariance matrix of the observed signals is obtained,the eigenvalues of the sample covariance matrix can be acquired by eigenvalue decomposition,and the eigenvalues are diagonally loaded,and a new formula for calculating the diagonal loading is presented.Based on the diagonal loaded eigenvalues,the difference values are calculated for the adjacent eigenvalues after diagonal loading,and the statistical variance of the difference values is calculated.On this basis,the second-order statistics of the difference values are constructed,and when the second-order statistics are minimized,the corresponding number of sources is estimated.The proposed method has wide applicability,which is suitable for both general asymptotic regime and classical asymptotic system,and is suitable for both white Gaussian noise environment and colored noise environment.The method makes up for the lack of source enumeration methods in the case of general asymptotic system and colored noise.
