AN ACCELERATION FOR THE EIGENSYSTEM REALIZATION ALGORITHM WITH PARTIAL SINGULAR VALUES DECOMPOSITION

The real-time identification of dynamic parameters is importantfor the control system of spacecraft. The eigensystme realizationalgorithm (ERA) is currently the typical method for such applica-tion. In order to identify the dynamic parameter of spacecraftrapidly and accurately, an accelerated ERA with a partial singularvalues decomposition (PSVD) algorithm is presented. In the PSVD, theHankel matrix is reduced to dual diagonal form first, and thentransformed into a tridiagonal matrix.

Analysis of heart rate variability based on singular value decomposition entropy

Assessing the dynamics of heart rate fluctuations can provide valuable information about heart status. In this study, regularity of heart rate variability （HRV） of heart failure patients and healthy persons using the concept of singular value decomposition entropy （SvdEn） is analyzed. SvdEn is calculated from the time series using normalized singular values. The advantage of this method is its simplicity and fast computation. It enables analysis of very short and non-stationary data sets. The results show that SvdEn of patients with congestive heart failure （CHF） shows a low value （SvdEn： 0.056±0.006, p 〈 0.01） which can be completely separated from healthy subjects. In addition, differences of SvdEn values between day and night are found for the healthy groups. SvdEn decreases with age. The lower the SvdEn values, the higher the risk of heart disease. Moreover, SvdEn is associated with the energy of heart rhythm. The results show that using SvdEn for discriminating HRV in different physiological states for clinical applications is feasible and simple.

Randomized Generalized Singular Value Decomposition

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.

The Singular Value Decomposition as a Tool of Investigating Central MHD Instabilities in the HL-1M Tokamak

A variety of strong MHD instabilities are always resulted from MHD activity of Tokamak plasmas. Central MHD instabilities can be observed with pinhole cameras to record soft x-ray (SXR) emission from the plasma along many chords with a high temporal resolution. The investigation of MHD instabilities often necessitates an analysis on spatial-temporal signals. The method of Singular Value Decomposition (SVD) can split such signals into orthogonal spatial and temporal vectors. By this means, the repetition time and the characteristic radius of various MHD phenomena such as sawteeth and snake-like perturbation can be obtained. Moreover, the (1,1) MHD mode is analyzed in great detail by SVD and used to determine the radius of the q = 1 surface.

基于SVD-K-means算法的软扩频信号伪码序列盲估计

针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。

二级减速器故障系统建模及SVD-MMSE劣化评估

为检测故障齿轮劣化程度并进行有效的程度评估,通过有限元法建立含有正常、裂纹和断齿等3种齿轮状态的二级直齿轮减速器系统模型。首先,分别计算3种状态的齿轮时变啮合刚度,并综合考虑轴承支撑刚度,得到了3种不同状态下的轴承振动响应;其次,引入多元多尺度样本熵(multivariate multiscale sample entropy,简称MMSE)对故障齿轮的劣化程度进行分析;最后,引进奇异值分解(singular value decomposition,简称SVD)算法进行预处理,以达到更好的诊断效果来综合评定故障齿轮生命周期的劣化程度。结果表明:齿轮发生故障时,主要导致时频域信号发生转频调制,时域存在有规律的冲击,频域出现边频带,且分布在输入轴的转频及其倍频和啮频及其倍频处;随着故障程度的增加,劣化越发明显,频率成分也发生改变,致使MMSE值也随之变化,且整体呈单调递减趋势;SVD-MMSE算法能有效地对齿轮故障程度进行判别,降低了噪声对于劣化程度检测准确性的影响。

一种基于TSVDT的微波关联前视成像方法

目前,传统雷达成像方法的发展日渐完善,但在前视成像场景下,雷达难以获取方位向上的多普勒信息,从而限制了其方位向分辨率。为了解决这一问题,国内提出了微波关联成像方法。微波关联成像方法利用关联成像原理进行雷达成像,无需利用目标的多普勒信息即可实现高分辨率成像。这一新型雷达成像方法突破了传统雷达成像方法中受限于雷达孔径的分辨率,具有极高的前视成像发展潜力。目前,国内外对微波关联成像的研究主要集中在产生随机波前、解决模型失配问题和研制超材料孔径等方面,但对关键的关联过程的优化主要集中在压缩感知和深度学习方面,而在伪逆算法方面的研究相对较少。因此,为了进一步完善微波关联成像体系,本文提出了一种新的针对伪逆算法优化的微波关联前视成像方法。本文结合截断奇异值分解(Truncated Singular Value Decomposition,TSVD)处理和吉洪诺夫正则化(Tikhonov)提出了奇异值分解和吉洪诺夫正则化的联合处理方法(TSVD-Tikhonov,TSVDT),通过TSVDT方法对时空随机辐射阵进行处理,然后进行压缩关联成像。同时,本文比较了广义交叉验证(Generalized Cross-Validation,GCV)和L曲线法,并证明了在微波关联成像方法中,利用GCV法选择截断参数的运算耗时更短且更稳定。最后,利用微波暗室实验验证了该方法在低信噪比条件下提高了成像的抗干扰能力,并且仍能保持较快的运算速度。

基于增广SVD-MWKF的激励识别与结构响应重构

针对传统卡尔曼滤波算法在结构响应重构应用中需要外部激励及测量噪声方差先验已知的问题,提出一种基于增广SVD-MWKF(Singular Value Decomposition-Moving Window Kalman Filter)的激励识别与结构响应重构方法。首先,引入奇异值分解降噪技术以优化移动窗口法对测量噪声方差的实时估计。随后使用基于增广状态空间方程的卡尔曼滤波算法并结合部分测点的加速度测量数据,实现对结构外部激励的识别及各位置的速度、加速度响应的重构。最后,对起重机桁架和简支梁分别进行数值模拟和试验分析,结果表明,相较于移动窗口法,所提方法对测量噪声方差估计更加准确,且对外部激励能进行有效识别。

DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

基于SVD-SUKF的水下机器人电池SOC估计

荷电状态(SOC)的准确估计关系到水下机器人的电池使用效率与任务规划。针对传统SOC估计算法存在的准确性、稳定性和鲁棒性不足等问题,提出一种奇异值分解增强的球型无迹卡尔曼滤波(SVD-SUKF)SOC估计算法。建立2阶Thevenin电路模型,并使用遗忘因子递推最小二乘法对模型参数进行在线辨识;在无迹卡尔曼滤波算法的基础上引入球型无迹变换和奇异值分解,避免繁琐的调参过程、减少算法计算量以及解决算法的协方差矩阵非正定问题;采用城市道路循环工况对SVD-SUKF算法进行验证。结果表明:SVD-SUKF算法收敛速度较快,平均绝对值误差为0.006 8、均方根误差为0.005 6,算法相较于扩展卡尔曼滤波和无迹卡尔曼滤波有更高的估计精度、更好的稳定性和更强的鲁棒性。

基于DT-CWT和SVD的变电站直流系统接地故障检测技术研究

变电站直流系统的状态直接关系到变电站的正常运行,为了对变电站直流系统出现的接地故障快速、准确定位,提出了一种双树复小波变换(DT-CWT)和奇异值分解(SVD)相结合的变电站直流系统接地故障检测新方法。该方法首先利用DT-CWT对支路电流信号进行分解来构建Hankel矩阵;然后对Hankel矩阵进行SVD分解,得到一系列奇异特征值;再次,利用相邻奇异值差值构建奇异值差分谱,通过奇异值差分谱最大峰值来保留有效的奇异值个数;最后,利用保留的奇异值来重构低频信号。算例分析结果表明,该方法能够准确地从支路电流信号中提取出低频交流信号,可以对变电站直流系统接地故障进行准确定位,很大程度上减小对地电容对检测精度的影响。

Hand-eye calibration with a new linear decomposition algorithm

To solve the homogeneous transformation equation of the form AX=XB in hand-eye calibration, where X represents an unknown transformation from the camera to the robot hand, and A and B denote the known movement transformations associated with the robot hand and the camera, respectively, this paper introduces a new linear decomposition algorithm which consists of singular value decomposition followed by the estimation of the optimal rotation matrix and the least squares equation to solve the rotation matrix of X. Without the requirements of traditional methods that A and B be rigid transformations with the same rotation angle, it enables the extension to non-rigid transformations for A and B. The details of our method are given, together with a short discussion of experimental results, showing that more precision and robustness can be achieved.

时域与频域自适应SVD融合去噪算法

时域方法在地震同相轴倾斜或弯曲时,难以保证去噪的有效性;频域方法在信号频带较宽时,会衰减过多信号。基于此,提出一种时域与频域自适应奇异值分解(singular value decomposition,SVD)融合去噪算法。该算法包含分解与融合技术:在分解技术中,根据奇异值二阶差分谱,在时域与频域中分别进行自适应去噪,得到两个分解矩阵;在融合技术中,提出了用于评估分解矩阵的一致度,利用融合策略得到融合矩阵,最后根据局部相似性调整得到去噪矩阵。在合成与野外数据集上与一些算法进行了对比实验,结果表明,所提算法能够更有效地压制噪声。

基于峭度原则的VMD-SVD微型电机声音信号降噪方法

微型电机运转时的声音信号包含丰富的状态信息,可用于生产线上电机的快速检测,但由于待测电机体积小、声音能量低,采集过程中声音信号易与环境噪声耦合,导致声音信号提取和检测不准确。该文通过研究电机组成结构,分析声音信号频率成分与成因,得到该文研究电机的声音信号3倍频谐波特点,提出一种基于峭度原则的VMDSVD算法对电机声音信号进行提纯降噪,该算法采用VMD分段原理,对各分段信号进行SVD分解,提取谐波特征,利用峭度原则优化VMD参数选取。首先通过仿真信号对比实验,验证了该文算法具有更好的降噪效果和降噪性能指标。而后,将该方法应用于微型电机实测声音信号,测试结果表明提出的基于峭度原则VMD-SVD算法具有良好降噪效果,能够显著提高原始信号信噪比,更利于后续特征提取和故障检测工作。

SVD-TLS extending Prony algorithm for extracting UWB radar target feature

A new method, SVD-TLS extending Prony algorithm, is introduced for extracting UWB radar target features. The method is a modified classical Prony method based on singular value decomposition and total least squares that can improve robust for spectrum estimation. Simulation results show that poles and residuum of target echo can be extracted effectively using this method, and at the same time, random noises can be restrained to some degree. It is applicable for target feature extraction such as UWB radar or other high resolution range radars.

Application of Singular Value Decomposition(SVD)to the Extraction of Gravity Anomalies Associated with Ag-Pb-Zn-W Polymetallic Mineralization in the Bozhushan Ore Field,Southwestern China

The Bozhushan Ore Field,located at the western margin of the South China Block,is an important area for Ag-Pb-Zn-W polymetallic mineralization which may be associated with the Late Cretaceous granitic magmaism.In this paper,the singular value decomposition(SVD)was effectively applied to decompose gravity data at scale of 1:50000 within the Bozhushan Ore Field to extract deep ore-finding information.Two gravity anomaly images displaying different scales of the ore-controlling factors were obtained.(1)The low-pass filtered image may reflect the deeply buried geological structures,hidden intrusions and concealed ore bodies.The negative gravity anomaly may reflect the overall distribution of granite bodies in the Bozhushan Ore Field.One negative gravity anomaly area may correspond to the exposed part of the Baozhushan granitic intrusion and the other corresponds to the concealed part of the granitic intrusion.The granitic intrusions are the main ore-controlling factors in this ore district.(2)The band-pass filtered image depicts the shallow concealed geological structures and geological bodies within this study area.There are two obvious negative gravity anomalies,which may be created by the hidden granites at different depths at both northwestern and southeastern sides of the exposed granitic intrusion.Thus the two negative gravity anomalies are favorable prospecting areas for various type of polymetallic ore deposits at depth.The gravity anomalies extracted by using the SVD exactly reflect the distribution of the ore deposits,structures and intrusions,which will give new insights for further mineral exploration in the study area.

Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix

This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet(PST)of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion(NIC),in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations(ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable(which is also the desired solution), and all others are(unstable)saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method.

基于MRSVD-SVD与VPMCD的交叉滚子轴承故障诊断研究

针对奇异值分解(SVD)提取工业机器人交叉滚子轴承振动信号微弱故障特征分量时,出现奇异值分辨率不足的问题,提出了一种基于最大分辨率奇异值分解(MRSVD)-奇异值分解(SVD)与变量预测模型模式识别(VPMCD)的工业机器人交叉滚子轴承的故障诊断方法。首先,以最大奇异值分辨率原则将一维振动信号构造成了Hankel矩阵,采用奇异值分解方法对Hankel矩阵进行了分解,得到了其奇异值序列,根据奇异值曲率谱理论选择有效奇异值,并进行了重构,得到了经降噪后的高信噪比信号,以重构信号构建了相空间矩阵,进行了二次奇异值分解,得到了其故障特征分量;然后,计算了故障特征分量的特征参数,构建了其特征向量;最后,采用了VPMCD分析了特征向量,完成了对交叉滚子轴承故障类型的识别,并与其它方法进行了识别准确率对比。研究结果表明:采用该方法对工业机器人交叉滚子轴承进行故障诊断,得到的故障类型识别准确率为98.66%,比SVD与共振解调相结合方法提高了9%;该方法通过构建最大奇异值分辨率矩阵提高了奇异值分辨率,可完整提取出工业机器人交叉滚子轴承振动信号的微弱故障特征分量,获得了更高的故障类型识别准确率。

A blind watermarking algorithm based on DWT and SVD

本文提出了一种新的基于离散小波变换(DWT)与奇异值分解(SVD)相结合的数字图像盲水印算法。该算法首先将原始图像作小波分解并将小波分解得到的低频子带进行分块,再对每一块进行奇异值分解,然后选取每块中最大的奇异值通过量化的方法嵌入水印信息,而且水印的提取不需要原始图像。实验结果表明,该算法具有一定的不可感知性及鲁棒性。

Development of the Decoupled Discreet-Time Jacobian Eigenvalue Approximation for Situational Awareness Utilizing Open PDC

With the increased number of PMUs in the power grid, effective high speed, realtime methods to ascertain relevant data for situational awareness are needed. Several techniques have used data from PMUs in conjunction with state estimation to assess system stability and event detection. However, these techniques require system topology and a large computational time. This paper presents a novel approach that uses real-time PMU data streams without the need of system connectivity or additional state estimation. The new development is based on the approximation of the eigenvalues related to the decoupled discreet-time power flow Jacobian matrix using direct openPDC data in real-time. Results are compared with other methods, such as Prony’s method, which can be too slow to handle big data. The newly developed Discreet-Time Jacobian Eigenvalue Approximation (DDJEA) method not only proves its accuracy, but also shows its effectiveness with minimal computational time: an essential element when considering situational awareness.