A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. F...A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.展开更多
Because of the complication of geological procedures,the recorded data have the feature of nonlinear.The multi-fractal singularity value decomposition (MSVD) was used to decomposed the gravity data.In this paper,the M...Because of the complication of geological procedures,the recorded data have the feature of nonlinear.The multi-fractal singularity value decomposition (MSVD) was used to decomposed the gravity data.In this paper,the MSVD was utilized to extract the gravity anomaly associated with the gold mineralization in Tongshi gold field in the southwest of Shandong province.The results showed that the Tongshi complex with negative circular gravity anomaly is an important ore-controlling factor.And the positive ring gravity anomaly distributed展开更多
针对滚动轴承因长期处于强噪声工作环境而故障频发,且早期故障信息微弱难以提取等问题,提出了一种基于奇异值分解(singular value decomposition,SVD)与参数优化变分模态分解(variational mode decomposition,VMD)的联合降噪方法。首先...针对滚动轴承因长期处于强噪声工作环境而故障频发,且早期故障信息微弱难以提取等问题,提出了一种基于奇异值分解(singular value decomposition,SVD)与参数优化变分模态分解(variational mode decomposition,VMD)的联合降噪方法。首先,对轴承振动信号进行了SVD,依据奇异值差分谱理论确定了有效奇异值的阶数并进行了叠加重构,经过矩阵逆变换得到了初步降噪信号;然后,运用灰狼优化算法对VMD的模态个数K和惩罚因子α两参数寻优后进一步分解了初步降噪信号,同时基于峭度和相关系数复合指标选取模态分量;最后,对筛选信号进行了重构,并包络解调分析了降噪前后的故障特征频率。仿真数据和实验数据分析表明:所提方法在强噪声背景下或故障特征信息极其微弱时,都能够有效抑制噪声并提取有效故障信息。展开更多
动力电池在复杂多变工况下,离线参数辨识无法实时反映电池动态特性导致参数辨识精度低,无迹卡尔曼滤波(untraceable Kalman filter,UKF)在估计电池荷电状态(State of charge,SOC)时对噪声处理十分有限,同时在处理协方差矩阵时出现非正...动力电池在复杂多变工况下,离线参数辨识无法实时反映电池动态特性导致参数辨识精度低,无迹卡尔曼滤波(untraceable Kalman filter,UKF)在估计电池荷电状态(State of charge,SOC)时对噪声处理十分有限,同时在处理协方差矩阵时出现非正定问题会导致算法波动和估计失效。基于双极化(dual polarization,DP)电路模型提出了遗忘因子递推最小二乘法(forgotten factor recursive least squares,FFRLS)和奇异值分解-自适应无迹卡尔曼滤波法(singular value decomposition-adaptive untraceable Kalman filter,SVD-AUKF)对电池SOC进行在线估计。仿真结果表明,在复杂工况下(美国联邦城市运行工况),与真实SOC值进行比较,SVD-AUKF进行模拟验证时平均绝对误差和均方根误差分别为0.5286%和0.5447%,在传统UKF算法基础上分别提高了57.96%和63.3%,进一步表明SVD-AUKF准确性和稳定性更高。展开更多
基金Project(2008041001) supported by the Academician Foundation of China Project(N0601-041) supported by the General Armament Department Science Foundation of China
文摘A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.
文摘Because of the complication of geological procedures,the recorded data have the feature of nonlinear.The multi-fractal singularity value decomposition (MSVD) was used to decomposed the gravity data.In this paper,the MSVD was utilized to extract the gravity anomaly associated with the gold mineralization in Tongshi gold field in the southwest of Shandong province.The results showed that the Tongshi complex with negative circular gravity anomaly is an important ore-controlling factor.And the positive ring gravity anomaly distributed
文摘针对滚动轴承因长期处于强噪声工作环境而故障频发,且早期故障信息微弱难以提取等问题,提出了一种基于奇异值分解(singular value decomposition,SVD)与参数优化变分模态分解(variational mode decomposition,VMD)的联合降噪方法。首先,对轴承振动信号进行了SVD,依据奇异值差分谱理论确定了有效奇异值的阶数并进行了叠加重构,经过矩阵逆变换得到了初步降噪信号;然后,运用灰狼优化算法对VMD的模态个数K和惩罚因子α两参数寻优后进一步分解了初步降噪信号,同时基于峭度和相关系数复合指标选取模态分量;最后,对筛选信号进行了重构,并包络解调分析了降噪前后的故障特征频率。仿真数据和实验数据分析表明:所提方法在强噪声背景下或故障特征信息极其微弱时,都能够有效抑制噪声并提取有效故障信息。
文摘动力电池在复杂多变工况下,离线参数辨识无法实时反映电池动态特性导致参数辨识精度低,无迹卡尔曼滤波(untraceable Kalman filter,UKF)在估计电池荷电状态(State of charge,SOC)时对噪声处理十分有限,同时在处理协方差矩阵时出现非正定问题会导致算法波动和估计失效。基于双极化(dual polarization,DP)电路模型提出了遗忘因子递推最小二乘法(forgotten factor recursive least squares,FFRLS)和奇异值分解-自适应无迹卡尔曼滤波法(singular value decomposition-adaptive untraceable Kalman filter,SVD-AUKF)对电池SOC进行在线估计。仿真结果表明,在复杂工况下(美国联邦城市运行工况),与真实SOC值进行比较,SVD-AUKF进行模拟验证时平均绝对误差和均方根误差分别为0.5286%和0.5447%,在传统UKF算法基础上分别提高了57.96%和63.3%,进一步表明SVD-AUKF准确性和稳定性更高。
