In this paper, solutions to the generalized Sylvester matrix equations AX -XF = BY and MXN -X = TY with A, M ∈ R^n×n, B, T ∈ Rn×r, F, N ∈ R^p×p and the matrices N, F being in companion form, are est...In this paper, solutions to the generalized Sylvester matrix equations AX -XF = BY and MXN -X = TY with A, M ∈ R^n×n, B, T ∈ Rn×r, F, N ∈ R^p×p and the matrices N, F being in companion form, are established by a singular value decomposition of a matrix with dimensions n × （n ＋ pr）. The algorithm proposed in this paper for the euqation AX - XF = BY does not require the controllability of matrix pair （A, B） and the restriction that A, F do not have common eigenvalues. Since singular value decomposition is adopted, the algorithm is numerically stable and may provide great convenience to the computation of the solution to these equations, and can perform important functions in many design problems in control systems theory.展开更多
The echo of the material level is non-stationary and contains many singularities.The echo contains false echoes and noise,which affects the detection of the material level signals,resulting in low accuracy of material...The echo of the material level is non-stationary and contains many singularities.The echo contains false echoes and noise,which affects the detection of the material level signals,resulting in low accuracy of material level measurement.A new method for detecting and correcting the material level signal is proposed,which is based on the generalized S-transform and singular value decomposition(GST-SVD).In this project,the change of material level is regarded as the low speed moving target.First,the generalized S-transform is performed on the echo signals.During the transformation process,the variation trend of window of the generalized S-transform is adjusted according to the frequency distribution characteristics of the material level echo signal,achieving the purpose of detecting the signal.Secondly,the SVD is used to reconstruct the time-frequency coefficient matrix.At last,the reconstructed time-frequency matrix performs an inverse transform.The experimental results show that the method can accurately detect the material level echo signal,and it can reserve the detailed characteristics of the signal while suppressing the noise,and reduce the false echo interference.Compared with other methods,the material level measurement error does not exceed 4.01%,and the material level measurement accuracy can reach 0.40%F.S.展开更多
The generalized singular value decomposition(GSVD)of two matrices with the same number of columns is a very useful tool in many practical applications.However,the GSVD may suffer from heavy computational time and memo...The generalized singular value decomposition(GSVD)of two matrices with the same number of columns is a very useful tool in many practical applications.However,the GSVD may suffer from heavy computational time and memory requirement when the scale of the matrices is quite large.In this paper,we use random projections to capture the most of the action of the matrices and propose randomized algorithms for computing a low-rank approximation of the GSVD.Serval error bounds of the approximation are also presented for the proposed randomized algorithms.Finally,some experimental results show that the proposed randomized algorithms can achieve a good accuracy with less computational cost and storage requirement.展开更多
Source-generated noise, such as air, refracted, guided waves, near-surface multiples, and radial ground roll, is one of the most challenging problems in the land seismic method. The interference of the noise with refl...Source-generated noise, such as air, refracted, guided waves, near-surface multiples, and radial ground roll, is one of the most challenging problems in the land seismic method. The interference of the noise with reflection events often results in a distorted representation of the subsurface and gives rise to interpretation uncertainties. To suppress the noise, geophysicists have devised various techniques in both acquisition and processing stages. Conventional processing methods, such as high-pass, f - k and hyperbolic velocity filters, however, have certain disadvantages when handling actual seismic data. In this study, we present a new hybrid method combining singular value decomposition (SVD) with a special linear transformation of the common-shot gather. The method is aimed at effectively removing the noise while minimizing harm to the signal. As compared with other methods, the SVD-based one gives a denser approximation to source-generated noise before its subtraction from the seismic data, due to the use of more appropriate basis functions. The special transformation applied in advance to the data is intended to align the source-generated noise events horizontally and thus to benefit the subsequent SVD. The effectiveness of the method in suppressing source-generated noise is demonstrated with a synthetic data set. Emphasis is put on the comparison of the performance of the method with that of conventional f - k filtering.展开更多
In this paper,the generalized inverse eigenvalue problem for the（P,Q）-conjugate matrices and the associated approximation problem are discussed by using generalized singular value decomposition（GSVD）.Moreover,the ...In this paper,the generalized inverse eigenvalue problem for the（P,Q）-conjugate matrices and the associated approximation problem are discussed by using generalized singular value decomposition（GSVD）.Moreover,the least residual problem of the above generalized inverse eigenvalue problem is studied by using the canonical correlation decomposition（CCD）.The solutions to these problems are derived.Some numerical examples are given to illustrate the main results.展开更多
Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of use...Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.展开更多
针对辅汽轮发电机组振动传递路径不清晰问题,以某高速辅汽轮发电机组为研究对象,建立转子-轴承-定子整机模型,基于工况传递路径分析(operational transfer path analysis,OTPA)方法构建机组振动传递模型,分析机组振动传递规律。首先应...针对辅汽轮发电机组振动传递路径不清晰问题,以某高速辅汽轮发电机组为研究对象,建立转子-轴承-定子整机模型,基于工况传递路径分析(operational transfer path analysis,OTPA)方法构建机组振动传递模型,分析机组振动传递规律。首先应用有限元方法获取机组各传递路径频率响应函数,综合考虑转子不平衡、脉动汽流力及不平衡磁拉力等激励源,采用奇异值分解技术对各轴承座处输入信号进行降噪合成,构建各路径输入与输出关系的传递函数矩阵,分析不同路径对定子外壳的振动贡献,明确其主要振动传递路径。结果表明:定子外壳各测点振动主要振动路径来源于辅汽轮机的2个轴承座,特别是发电机侧轴承座的振动贡献量最大。通过优化调整该轴承座结构固有特性与转子不平衡力相位差可以有效降低定子外壳振动,最大振幅由364μm降至51μm。研究结果可以为辅汽轮发电机组振动溯源及故障诊断的优化提供参考。展开更多
因高度向分辨能力缺失,地基干涉雷达应用于建筑成像时会发生严重的高度向叠掩现象。层析合成孔径雷达(Tomographic Synthetic Aperture Radar,TomoSAR)技术具备高度向分辨能力,能够实现建筑三维成像。地基层析圆弧扫描合成孔径雷达(Grou...因高度向分辨能力缺失,地基干涉雷达应用于建筑成像时会发生严重的高度向叠掩现象。层析合成孔径雷达(Tomographic Synthetic Aperture Radar,TomoSAR)技术具备高度向分辨能力,能够实现建筑三维成像。地基层析圆弧扫描合成孔径雷达(Ground-based Tomographic Arc-scanning Synthetic Aperture Radar,GB-TomoArcSAR)通过双轴转台控制天线在不同俯仰角度的水平面内进行圆周扫描来获取高度向合成孔径,实现三维层析成像。本文提出了GB-TomoArcSAR的三维点云生成方法,首先构建了适用于高度向弧形采样条件的层析成像几何模型。其次利用基于巴特沃斯滤波器的奇异值分解(Singular Value Decomposition,SVD)方法进行谱估计,找出层析谱中的峰值及其对应的峰值位置,构成层析向目标候选集。随后利用自对消顺序广义似然比(Sequential Generalized Likelihood Ratio Test with Cancellation,SGLRTC)检测器估计散射体的数目与位置,通过设置检测门限将真实目标的峰值及对应的峰值位置从候选集中筛选出来。最后采用基于空间几何分布的点云优化方法剔除误差点,生成点云图像。文章通过点目标和面目标的仿真实验,验证了所提方法适用于GB-TomoArcSAR,能够有效解决高度向多散射体目标的叠掩问题;进一步开展了实测数据验证,基于所提方法获取了北京市一处建筑基坑的层析点云,其与实际场景几何特征一致。展开更多
提出一种新的通用旁瓣消除器结构,它利用广义奇异值分解(Generalized singular value decomposition,GSVD)技术,通过广义奇异向量的变换间接估计声源到麦克风之间的传递函数。不同噪声环境下的实验结果表明,与现有的各种GSC算法相比,该...提出一种新的通用旁瓣消除器结构,它利用广义奇异值分解(Generalized singular value decomposition,GSVD)技术,通过广义奇异向量的变换间接估计声源到麦克风之间的传递函数。不同噪声环境下的实验结果表明,与现有的各种GSC算法相比,该算法能够更有效地抑制混响和噪声,并且增强后的语音失真最小。展开更多
基金This work was supported by the Chinese Outstanding Youth Foundation(No.69925308)Program for Changjiang Scholars and Innovative ResearchTeam in University.
文摘In this paper, solutions to the generalized Sylvester matrix equations AX -XF = BY and MXN -X = TY with A, M ∈ R^n×n, B, T ∈ Rn×r, F, N ∈ R^p×p and the matrices N, F being in companion form, are established by a singular value decomposition of a matrix with dimensions n × （n ＋ pr）. The algorithm proposed in this paper for the euqation AX - XF = BY does not require the controllability of matrix pair （A, B） and the restriction that A, F do not have common eigenvalues. Since singular value decomposition is adopted, the algorithm is numerically stable and may provide great convenience to the computation of the solution to these equations, and can perform important functions in many design problems in control systems theory.
基金National Natural Science Foundation of China(No.61761027)。
文摘The echo of the material level is non-stationary and contains many singularities.The echo contains false echoes and noise,which affects the detection of the material level signals,resulting in low accuracy of material level measurement.A new method for detecting and correcting the material level signal is proposed,which is based on the generalized S-transform and singular value decomposition(GST-SVD).In this project,the change of material level is regarded as the low speed moving target.First,the generalized S-transform is performed on the echo signals.During the transformation process,the variation trend of window of the generalized S-transform is adjusted according to the frequency distribution characteristics of the material level echo signal,achieving the purpose of detecting the signal.Secondly,the SVD is used to reconstruct the time-frequency coefficient matrix.At last,the reconstructed time-frequency matrix performs an inverse transform.The experimental results show that the method can accurately detect the material level echo signal,and it can reserve the detailed characteristics of the signal while suppressing the noise,and reduce the false echo interference.Compared with other methods,the material level measurement error does not exceed 4.01%,and the material level measurement accuracy can reach 0.40%F.S.
基金supported by the National Natural Science Foundation of China under Grant nos.11701409 and 11571171the Natural Science Foundation of Jiangsu Province of China under Grant BK20170591the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant 17KJB110018.
文摘The generalized singular value decomposition(GSVD)of two matrices with the same number of columns is a very useful tool in many practical applications.However,the GSVD may suffer from heavy computational time and memory requirement when the scale of the matrices is quite large.In this paper,we use random projections to capture the most of the action of the matrices and propose randomized algorithms for computing a low-rank approximation of the GSVD.Serval error bounds of the approximation are also presented for the proposed randomized algorithms.Finally,some experimental results show that the proposed randomized algorithms can achieve a good accuracy with less computational cost and storage requirement.
文摘Source-generated noise, such as air, refracted, guided waves, near-surface multiples, and radial ground roll, is one of the most challenging problems in the land seismic method. The interference of the noise with reflection events often results in a distorted representation of the subsurface and gives rise to interpretation uncertainties. To suppress the noise, geophysicists have devised various techniques in both acquisition and processing stages. Conventional processing methods, such as high-pass, f - k and hyperbolic velocity filters, however, have certain disadvantages when handling actual seismic data. In this study, we present a new hybrid method combining singular value decomposition (SVD) with a special linear transformation of the common-shot gather. The method is aimed at effectively removing the noise while minimizing harm to the signal. As compared with other methods, the SVD-based one gives a denser approximation to source-generated noise before its subtraction from the seismic data, due to the use of more appropriate basis functions. The special transformation applied in advance to the data is intended to align the source-generated noise events horizontally and thus to benefit the subsequent SVD. The effectiveness of the method in suppressing source-generated noise is demonstrated with a synthetic data set. Emphasis is put on the comparison of the performance of the method with that of conventional f - k filtering.
基金Supported by the Key Discipline Construction Project of Tianshui Normal University
文摘In this paper,the generalized inverse eigenvalue problem for the（P,Q）-conjugate matrices and the associated approximation problem are discussed by using generalized singular value decomposition（GSVD）.Moreover,the least residual problem of the above generalized inverse eigenvalue problem is studied by using the canonical correlation decomposition（CCD）.The solutions to these problems are derived.Some numerical examples are given to illustrate the main results.
基金This research was sponsored by the National Natural Science Foundation of China （Grant Nos. 51275052 and 51105041）, and the Key Project Supported by Beijing Natural Science Foundation （Grant No. 3131002）.
文摘Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.
文摘针对辅汽轮发电机组振动传递路径不清晰问题,以某高速辅汽轮发电机组为研究对象,建立转子-轴承-定子整机模型,基于工况传递路径分析(operational transfer path analysis,OTPA)方法构建机组振动传递模型,分析机组振动传递规律。首先应用有限元方法获取机组各传递路径频率响应函数,综合考虑转子不平衡、脉动汽流力及不平衡磁拉力等激励源,采用奇异值分解技术对各轴承座处输入信号进行降噪合成,构建各路径输入与输出关系的传递函数矩阵,分析不同路径对定子外壳的振动贡献,明确其主要振动传递路径。结果表明:定子外壳各测点振动主要振动路径来源于辅汽轮机的2个轴承座,特别是发电机侧轴承座的振动贡献量最大。通过优化调整该轴承座结构固有特性与转子不平衡力相位差可以有效降低定子外壳振动,最大振幅由364μm降至51μm。研究结果可以为辅汽轮发电机组振动溯源及故障诊断的优化提供参考。
文摘因高度向分辨能力缺失,地基干涉雷达应用于建筑成像时会发生严重的高度向叠掩现象。层析合成孔径雷达(Tomographic Synthetic Aperture Radar,TomoSAR)技术具备高度向分辨能力,能够实现建筑三维成像。地基层析圆弧扫描合成孔径雷达(Ground-based Tomographic Arc-scanning Synthetic Aperture Radar,GB-TomoArcSAR)通过双轴转台控制天线在不同俯仰角度的水平面内进行圆周扫描来获取高度向合成孔径,实现三维层析成像。本文提出了GB-TomoArcSAR的三维点云生成方法,首先构建了适用于高度向弧形采样条件的层析成像几何模型。其次利用基于巴特沃斯滤波器的奇异值分解(Singular Value Decomposition,SVD)方法进行谱估计,找出层析谱中的峰值及其对应的峰值位置,构成层析向目标候选集。随后利用自对消顺序广义似然比(Sequential Generalized Likelihood Ratio Test with Cancellation,SGLRTC)检测器估计散射体的数目与位置,通过设置检测门限将真实目标的峰值及对应的峰值位置从候选集中筛选出来。最后采用基于空间几何分布的点云优化方法剔除误差点,生成点云图像。文章通过点目标和面目标的仿真实验,验证了所提方法适用于GB-TomoArcSAR,能够有效解决高度向多散射体目标的叠掩问题;进一步开展了实测数据验证,基于所提方法获取了北京市一处建筑基坑的层析点云,其与实际场景几何特征一致。
文摘提出一种新的通用旁瓣消除器结构,它利用广义奇异值分解(Generalized singular value decomposition,GSVD)技术,通过广义奇异向量的变换间接估计声源到麦克风之间的传递函数。不同噪声环境下的实验结果表明,与现有的各种GSC算法相比,该算法能够更有效地抑制混响和噪声,并且增强后的语音失真最小。
