Contourlet watermarking algorithm based on Arnold scrambling and singular value decomposition

A new digital watermarking algorithm based on the contourlet transform is proposed to improve the robustness and anti-attack performances of digital watermarking. The algorithm uses the Arnold scrambling technique and the singular value decomposition （SVD） scheme. The Arnold scrambling technique is used to preprocess the watermark, and the SVD scheme is used to find the best suitable hiding points. After the contourlet transform of the carrier image, intermediate frequency sub-bands are decomposed to obtain the singularity values. Then the watermark bits scrambled in the Arnold rules are dispersedly embedded into the selected SVD points. Finally, the inverse contourlet transform is applied to obtain the carrier image with the watermark. In the extraction part, the watermark can be extracted by the semi-blind watermark extracting algorithm. Simulation results show that the proposed algorithm has better hiding and robustness performances than the traditional contourlet watermarking algorithm and the contourlet watermarking algorithm with SVD. Meanwhile, it has good robustness performances when the embedded watermark is attacked by Gaussian noise, salt- and-pepper noise, multiplicative noise, image scaling and image cutting attacks, etc. while security is ensured.

The Singular Value Decomposition Analysis between Summer Precipitation in the Dongting Lake Region and the Global Sea Surface Temperature

By dint of the summer precipitation data from 21 stations in the Dongting Lake region during 1960-2008 and the sea surface temperature(SST) data from NOAA,the spatial and temporal distributions of summer precipitation and their correlations with SST are analyzed.The coupling relationship between the anomalous distribution in summer precipitation and the variation of SST has between studied with the Singular Value Decomposition(SVD) analysis.The increase or decrease of summer precipitation in the Dongting Lake region is closely associated with the SST anomalies in three key regions.The variation of SST in the three key regions has been proved to be a significant previous signal to anomaly of summer rainfall in Dongting region.

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.

Detection and correction of level echo based on generalized S-transform and singular value decomposition

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.

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APSO-SSD-SVD的特高压换流站OLTC振动信号降噪方法

随着中国特高压交直流换流站的大规模投运,有载分接开关(on-load tap changer, OLTC)已成为特高压换流站中发生故障较多的设备之一。针对强背景噪声环境下特高压换流站OLTC故障特征难以提取的问题,提出一种基于自适应粒子群算法优化奇异谱分解和奇异值分解的方法。首先,利用自适应粒子群优化(adaptive particle swarm optimization, APSO)算法对奇异谱分解算法中的模态参数进行优化,选取最优分解模态数。其次,基于最大峭度准则选取最佳奇异谱分量。然后,确定最佳重构阶数,通过奇异值分解重构信号,从而达到信号降噪的目的。将所提方法应用于仿真信号和实验信号,结果表明所提方法的信噪比达到23.302,均方根误差仅为0.004,并且波形相似参数高达0.998,优于其他降噪方法。所提方法能够更有效地实现对特高压换流站OLTC振动信号的降噪,为辅助运维人员诊断OLTC状态提供参考。

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。

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.

基于改进TVF-EMD与SVD的轴承故障特征提取

滚动轴承早期故障信号微弱,故障特征难以提取。针对此问题,提出一种基于时变滤波经验模态分解(TVF-EMD)模态分量自适应融合与奇异值分解(SVD)降噪的轴承早期故障特征提取方法。为了降低故障信号的非线性和非平稳性,通过TVF-EMD将轴承信号分解为一系列本征模态函数(IMF)。为了克服TVF-EMD分解后IMF分量过多的不足,构造包络故障信息能量占比(EREFI)指标,通过EREFI对IMF分量进行降序排列,并依据包络故障信息能量占比递增原则对IMF分量依次进行融合,直至找到最优融合分量。最后,通过SVD对最优融合分量降噪,并提取故障特征。通过仿真信号以及2个实测轴承故障信号对所提方法性能进行了实验验证。实验结果表明:所提方法具有良好的敏感特征筛选融合能力和降噪能力,能更准确提取出轴承早期故障特征,实现故障类型的准确识别。

基于SVD-IACMD的GIS振动信号去噪算法

振动测量对发现气体绝缘开关设备(gas insulated switchgear,GIS)潜在性缺陷具有重要意义,但GIS本体振动信号易受基础振动、测量噪声以及环境噪声的影响,使得现场GIS振动带电检测和机械缺陷诊断的效果较差。针对此问题,提出一种基于奇异值分解(singular value decomposition,SVD)-改进自适应啁啾模态分解(improve adaptive chirp mode decomposition,IACMD)的现场振动信号降噪算法。该方法首先利用SVD对原始振动信号进行预处理,滤除低频基础振动和测量噪声,其次利用鱼鹰优化算法(osprey optimization algorithm,OOA)对处理后的信号进行自适应模态分解,得到分解后的固有模态(intrinsic mode functions,IMF)分量,再利用互相关系数筛选有效分量重构振动信号。模拟信号与现场信号测试结果表明:与OOA-自适应啁啾模态分解(adaptive chirp mode decomposition,ACMD)和SVD-变分模态分解(variational mode decomposition,VMD)相比,所提出的SVD-IACMD算法可以去除基础振动、测量噪声和环境噪声,保留GIS本体振动的基频和谐波分量,为GIS现场抗干扰振动检测和机械缺陷诊断提供技术支持。

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。

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。

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.

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。

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.

基于STA/LTA改进的CEEMD-SVD微震信号降噪算法

针对煤矿井下工作环境复杂,采集到的微震信号包含大量噪声信号,严重影响对微震信号的拾取、定位和反演。采用互补集合经验模态分解(complementary set empirical mode decomposition, CEEMD)联合奇异值分解(singular value decomposition, SVD)与长短时窗法(STA/LTA)相结合的降噪算法。利用CEEMD分解微震信号,得到固有模态分量(inherent modal component, IMF),依据相关系数确定噪声主导的IMF和信号主导的IMF,通过STA/LTA去除CEEMD产生的伪分量。对噪声主导的分量进行SVD分解降噪后与信号主导的分量及剩余分量重构得到降噪后信号。加入模拟噪声信号与实际采集的微震信号进行仿真实验,结果表明本文算法在保证小剩余噪声干扰的情况下,可以节省计算时间。通过与经验模态分解(empirical mode decomposition, EMD)、聚合经验模态分解(ensemble empirical mode decomposition, EEMD)及新型自适应聚合经验模态分解(novel adaptive ensemble empirical mode decomposition, NAEEMD)降噪方法进行对比,依据信噪比、能量百分比及标准差三个评价指标进行定量计算,实验表明该方法具有更好的降噪效果。

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.

基于BEMD、DCT和SVD的混合图像水印算法

水印的不可见性和算法的鲁棒性是图像版权保护领域关注的重要问题,然而大多数算法不能很好地平衡二者的关系。为此,提出了一种基于二维经验模态分解(BEMD)、离散余弦变换(DCT)和奇异值分解(SVD)的不可见性高、鲁棒性强的混合图像水印算法。首先,对水印图像采用Arnold置乱,增强算法的安全性,并对置乱后的水印图像进行二维DCT。然后,对宿主图像进行BEMD,得到有限个尺度不同的内蕴模态函数(IMF)及余量,选择与宿主图像相关性较低的IMF执行二维DCT,根据水印的大小对其进行不重叠分块,分别对每个分块图像以及经DCT的水印图像执行SVD。最后,根据自适应最优嵌入准则确定水印嵌入强度,并将水印嵌入每个分块,以增强算法的容错性。大量实验以及与现有算法的对比表明,所提算法不仅具有抵抗大尺度攻击的鲁棒性,而且具有较高的不可见性。