Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.

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.

基于EMD-SVD的矿山微震信号降噪方法及其应用

为了提高微震监测技术对微震信号分析处理的准确性,充分提取微震信号波形中的有效信息,针对矿山微震信号非平稳、非线性的特点,提出了一种基于经验模态分解(EMD)和奇异值分解(SVD)的联合降噪方法。该方法首先通过EMD分解获得信号的IMF分量,利用相关系数、方差贡献率和相似度对IMF分量进行了优选;然后使用优选后的IMF分量重构一维微震信号时间序列的相空间数据,经过SVD分解后,利用奇异值能量百分比确立了SVD重构阶数,并根据SVD恢复原理得到了降噪后的一维微震时间序列;最后以山东某矿现场矿山爆破为例,采用不同降噪方法对3类典型微震信号进行了降噪处理,并对其降噪效果进行了对比分析。结果表明,EMD-SVD降噪方法与传统降噪方法相比,其平均信噪比提高了35%,平均均方根误差降低了50%,有效剔除了微震信号的噪声分量,保留了信号的特征信息。该研究对分析矿山微震信号、微震事件定位及煤矿动力灾害监测具有重要意义。

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。

基于天牛须优化算法和LP-SWT-SVD的鲁棒图像水印技术

为了有效保护数字媒体的知识产权,提出一种基于拉普拉斯金字塔和平稳小波变换(Stationary Wavelet Transform,SWT)及奇异值分解(Singular Value Decomposition,SVD)的图像水印嵌入算法。该算法首先对原始图像进行拉普拉斯金字塔分解,然后对得到的残差图像进行一级平稳小波变换,得到低频子带LL1和高频子带HH1,分别对其进行SVD分解,并将SVD分解后的水印分别嵌入低频和高频子带的奇异值矩阵中,使用天牛须算法(Beetle Antennae Search,BAS)优化水印嵌入过程。水印检测时,将从LL1和HH1子带中提取的水印进行比较,选择效果较好的作为最终结果。仿真实验与其他文献的对比分析证明该算法不可见性和鲁棒性都较好。

基于PE-CEEMD-SVD的Φ-OTDR信号降噪方法

为实现相位敏感光时域反射仪中相位信号的精确测量,提出了一种基于排列熵算法的互补集合经验模态分解联合奇异值分解的新型降噪方法(PE-CEEMD-SVD)。首先,对含有噪声的相位信号进行CEEMD分解,得到一系列频率不同的IMF分量;然后,将PE算法和相关系数机制相结合,保留较大相关的有用分量,对较小相关的噪声分量使用SVD算法进行二次降噪;最后将两次降噪后保留下来的有用分量进行重构。仿真和实验结果表明,相较于EMD、EEMD和CEEMD降噪方法,该方法可获得更高信噪比的信号,有利于相位信号的精确测量。

基于SVD的复数UKF及电力系统对称分量估计

电力系统对称分量的检测对于电力系统安全稳定的运行具有很重要的意义。利用复数域无迹卡尔曼滤波算法,对三相电压系统的正负序分量及频率进行了估计。为了提高复数无迹卡尔曼滤波的参数估计精度及算法稳定性,引入最优自适应因子并对预测协方差矩阵进行SVD分解,提出了基于SVD的自适应CUKF算法。为消除零序分量,对三相电压分量进行αβ变换,定义了复数形式的状态变量,建立了非线性状态方程及观测方程,实现了正序、负序对称分量估计。通过与普通复数域无迹卡尔曼滤波算法对比,所提研究方法在估计精度及收敛速度等方面优于传统无迹卡尔曼滤波方法。

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。

基于SVD和MED的滚动轴承特征提取

针对滚动轴承振动信号易受噪声影响,难以提取故障特征信息的问题,提出一种奇异值分解(singular value decomposition,SVD)重构结合最小熵反卷积(minimum entropy deconvolution,MED)增强的滚动轴承故障特征提取方法。首先,对振动信号进行SVD分解,并计算奇异分量(singular component,SC)对应线性峭度(L-kurtosis)值;其次,根据线性峭度值结合设定阈值筛选SC,叠加得到重构信号;随后,对重构信号利用MED进行增强,凸出信号中周期冲击成分;最后,结合包络解调提取故障特征频率。仿真信号及实测信号分析结果表明,该方法可以降低噪声对振动信号的影响且凸显故障的特征信息,实现故障诊断。

IMPROVED SINGULAR VALUE DECOMPOSITION TECHNIQUE FOR DETECTING AND EXTRACTING PERIODIC IMPULSE COMPONENT IN A VIBRATION SIGNAL

Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, there may exist a corresponding local fault in themachine, and if further extracting the periodic impulse components from the vibration signals, theseverity of the local fault can be estimated and tracked. However, the signal-to-noise ratios (SNRs)of the vibration acceleration signals are often so small that the periodic impulse components aresubmersed in much background noises and other components, and it is difficult or inconvenient for usto detect and extract the periodic impulse components with the current common analyzing methods forvibration signals. Therefore, another technique, called singular value decomposition (SVD), istried to be introduced to solve the problem. First, the principle of detecting and extracting thesignal periodic components using singular value decomposition is summarized and discussed. Second,the infeasibility of the direct use of the existing SVD based detecting and extracting approach ispointed out. Third, the approach to construct the matrix for SVD from the signal series is improvedlargely, which is the key program to improve the SVD technique; Other associated improvement is alsoproposed. Finally, a simulating application example and a real-life application example ondetecting and extracting the periodic impulse components are given, which showed that the introducedand improved SVD technique is feasible.

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.

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。

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.

A NOTE ON SINGULAR VALUE DECOMPOSITION FOR RADON TRANSFORM IN R^n

The singular value decomposition is derived when the Radon transform is restricted to functions which are square integrable on the unit ball in R-n with respect to the weight W-lambda(x). It fulfilles mainly by means of the projection-slice theorem. The range of the Radon transform is spanned by products of Gegenbauer polynomials and spherical harmonics. The inverse transform of the those basis functions are given. This immediately leads to an inversion formula by series expansion and range characterizations.

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.

A Color Image Encryption Scheme Based on Singular Values and Chaos

The security of digital images transmitted via the Internet or other public media is of the utmost importance.Image encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized entities.This study provides an approach to color image encryption that could find practical use in various contexts.The proposed method,which combines four chaotic systems,employs singular value decomposition and a chaotic sequence,making it both secure and compression-friendly.The unified average change intensity,the number of pixels’change rate,information entropy analysis,correlation coefficient analysis,compression friendliness,and security against brute force,statistical analysis and differential attacks are all used to evaluate the algorithm’s performance.Following a thorough investigation of the experimental data,it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.

Estimation of fracture density and orientation from azimuthal elastic impedance difference through singular value decomposition

Accurate estimation of fracture density and orientation is of great significance for seismic characterization of fractured reservoirs.Here,we propose a novel methodology to estimate fracture density and orientation from azimuthal elastic impedance(AEI)difference using singular value decomposition(SVD).Based on Hudson's model,we first derive the AEI equation containing fracture density in HTI media,and then obtain basis functions and singular values from the normalized AEI difference utilizing SVD.Analysis shows that the basis function changing with azimuth is related to fracture orientation,fracture density is the linearly weighted sum of singular values,and the first singular value contributes the most to fracture density.Thus,we develop an SVD-based fracture density and orientation inversion approach constrained by smooth prior elastic parameters.Synthetic example shows that fracture density and orientation can be stably estimated,and the correlation coefficient between the true value and the estimated fracture density is above 0.85 even when an S/N ratio of 2.Field data example shows that the estimated fracture orientation is consistent with the interpretation of image log data,and the estimated fracture density reliably indicates fractured gas-bearing reservoir,which could help to guide the exploration and development of fractured reservoirs.

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。