基于SVD-CWT和CNN的水轮发电机转子故障识别

水轮发电机转子振动故障识别是水电站运维的重难点问题,为此提出一种基于转子振动信号的故障识别方法。首先针对发电机转子的非平稳和非线性振动信号,采用奇异值分解(SVD)并结合能量差分谱理论进行降噪预处理;对预处理数据使用连续小波变换(CWT)转换为时频图并形成图像数据集;然后将该图像数据集作为卷积神经网络(CNN)输入,通过CNN多层池化及卷积形成分布式故障特征表达,最终实现发电机转子故障模式识别和分类。经实验验证,该方法准确率达到99.5%以上,能有效识别出发电机转子的故障类型。

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。

基于自适应动态粒子群优化的RAK-SVD方法

K均值奇异值分解(K-SVD)算法是一种行之有效的地震资料去噪方法,但由于其稀疏分解存在不确定性,需要引入正则项对其改进。为此,在常规粒子群算法的基础上,提出了一种自适应动态粒子群算法优化正则化参数的正则化近似K-SVD(RAK-SVD)去噪方法。首先通过修改字典原子和相关参数,解决了由于常规粒子群算法的惯性参数固定不变,导致后期搜索效率下降的问题;其次将正则化系数引入近似K-SVD(AK-SVD)方法,明显提升了去噪效果;最后利用自适应动态粒子群算法自动优选AK-SVD方法中的正则化参数,提高了稀疏分解的确定性,在对强反射信号进行去噪的同时加强了对弱信号的保护。模型测试和实际应用均表明,该方法有利于弱信号的提取和识别,不仅能够显著改善弱地震信号的去噪效果,还提升了计算效率。该方法具有一定的实际应用价值。

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。

Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

基于CRS-LMD和SVD的MMC-HVDC线路故障测距方法

直流输电线路故障行波波速不确定、波头提取困难以及噪声干扰等因素制约了直流电网中故障测距技术的应用。为了降低上述因素对定位准确性的影响,提出一种基于局部特征有理样条插值均值分解(LMD based on characteristic rational spline,CRS-LMD)和奇异值分解(singular value decomposition,SVD)的故障测距方法。首先,利用特征尺度选取最优极点系数,结合有理样条插值调节拟合曲线的松紧程度,实现对故障电压行波的局部均值分解。其次,采用奇异值分解对故障行波波头进行准确提取。最后,在PSCAD/EMTDC中搭建了张北±500 kV柔性直流电网的仿真模型,模拟各种故障情况并输出故障数据,利用Matlab对故障数据进行处理并验证定位算法。最后,仿真结果表明,所提故障测距算法在不同故障距离和故障类型下均能实现故障测距,且在叠加噪声和过渡电阻的情况下也能保障较高的精确性。

基于改进SVD++算法和K-means++算法的小文件合并方案

文章提出了一种基于改进SVD++算法和K-means++算法的小文件合并方案。通过引入自适应学习率函数和基于并行分组的SVD++算法,优化了小文件的合并过程,以提高Hadoop存储小文件的效率。同时,利用K-means++算法对合并后的文件进行聚类,优化了数据存储方式,降低了存储空间的浪费。在Hadoop平台上进行的实验表明,该方案在保持数据处理准确性和稳定性的同时,显著提升了Hadoop存储与处理小文件的性能。

基于K-SVD算法的数字图像自适应修复方法

为了提升数字图像的完整性和清晰度,提出一种基于K-SVD算法的数字图像自适应修复方法。通过FCM算法将数字图像划分成不同的图像块,将不同类别的数字图像依据K-SVD算法的稀疏编码和字典更新模块进行训练,获取各个不同类别数字图像块的字典,求出其稀疏系数,结合字典和稀疏系数更新数字图像中的每一类图像块,完成数字图像中每一类图像块的修复或重构,将修复好的图像块放回原数字图像中,实现数字图像的自适应修复。实验结果表明,该方法能够有效地恢复图像的细节和结构,修复后的数字图像均方根误差低,并且具有较高的峰值信噪比,同时,修复后的数字图像与原图像的结构相似性高达0.95,且在数字图像修复效率方面具备显著优势。

利用LMD-SVD方法进行GNSS坐标时间序列降噪

为降低噪声对GNSS坐标时间序列的影响、有效提取时间序列中的有用信息,在局部均值分解(LMD)降噪方法的基础上引入奇异值分解(SVD)方法,建立了LMD-SVD方法。首先通过LMD方法将时间序列分解为若干个乘积函数(PF)和余量,PF分量可反映时间序列的时频分布特性;然后通过连续均方根误差方法确定高频分量与低频分量的分界点;最后对经SVD方法降噪后的高频分量、低频分量和余量进行重构,得到最终降噪结果。利用5个GNSS测站U方向坐标时间序列对该方法进行验证。结果表明,相较于单一LMD方法,LMD-SVD方法结果的信噪比与相关系数分别提高了34.28%与17.11%,均方根误差降低了51.31%,降噪效果更好。

基于SVD配准的空间圆拟合方法

针对传统空间圆拟合方法精度低、方法不稳定等问题,提出一种基于SVD配准解算旋转、平移及缩放系数新方法,进而解算空间圆半径、圆心坐标、圆度和平整度等空间形态参数。与采用最小二乘方法拟合空间圆进行对比,该方法无须迭代,仅需一个缩放系数初始值即可解算空间形态参数,更为快捷高效。为保证计算精度,利用均值漂移的后验概率实现粗差剔除,并以某地铁隧道钢环测量工程实例验证该算法的可靠性与可行性。

基于正则化SVD算法的660MW机组煤粉加热炉炉膛三维温度场重建

针对现有加热炉炉膛内三维温度场重建方法存在的重建误差较大、重建消耗时间较长的问题,提出基于正则化SVD算法的660MW机组煤粉加热炉炉膛三维温度场重建方法。根据人眼视觉二维图像特征点提取原理,提取温度场立体图像特征点;利用小波变换方法计算子线段端点,获取特征点匹配结果;通过声学测温方法以及射线成像理论,重建声波传播速度分布形式,凭借正则化SVD算法构建声学测量系统模型,对声波飞行值进行修正,结合特征点匹配结果和对称轴,得到实现660MW机组煤粉加热炉炉膛三维温度场重建。实验结果表明,所提方法的最低AER、MER、RMSE分别为4.11、0.98、1.21,重建时间始终保持在0.6s以内,重建误差较小、重建消耗时间较短,抗噪声能力强,温度场重建效果好。

基于视觉密码和DCT-SVD彩色图像水印技术

本文提出了一种基于视觉密码和DCT-SVD的彩色图像水印算法,通过视觉密码方案将彩色秘密水印图像拆分成3份与彩色秘密水印图像等大小的分享份图像,并利用待标记图像、水印图像和密钥生成验证信息和测试图像。由版权所有者提供水印图像和验证信息,密钥交由图像所有者保存。当要验证图像所有者的所有权时,会要求图像所有者提供密钥,结合密钥、测试图像和验证信息便可生成水印,若密钥正确,生成的水印可识别为原始水印。将其中一份分享份图像利用DCT-SVD水印算法嵌入到载体图像中并进行攻击测试,利用数字水印的提取算法将分享份图像从遭受常见攻击的载体图像中提取出来,与剩余分享份进行叠加恢复秘密水印图像。实验结果表明,水印具有良好的不可感知性和鲁棒性。

Rate-limiting factors in hydrate decomposition through depressurization across various scales:A mini-review

Natural gas hydrate is an energy resource for methane that has a carbon quantity twice more than all traditional fossil fuels combined.However,their practical application in the field has been limited due to the challenges of long-term preparation,high costs and associated risks.Experimental studies,on the other hand,offer a safe and cost-effective means of exploring the mechanisms of hydrate dissociation and optimizing exploitation conditions.Gas hydrate decomposition is a complicated process along with intrinsic kinetics,mass transfer and heat transfer,which are the influencing factors for hydrate decomposition rate.The identification of the rate-limiting factor for hydrate dissociation during depressurization varies with the scale of the reservoir,making it challenging to extrapolate findings from laboratory experiments to the actual exploitation.This review aims to summarize current knowledge of investigations on hydrate decomposition on the subject of the research scale(core scale,middle scale,large scale and field tests)and to analyze determining factors for decomposition rate,considering the various research scales and their associated influencing factors.

Formation and transformation of metastable LPSO building blocks clusters in Mg-Gd-Y-Zn-Zr alloys by spinodal decomposition and heterogeneous nucleation

To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.

Enhancing the Interaction of Carbon Nanotubes by Metal-Organic Decomposition with Improved Mechanical Strength and Ultra-Broadband EMI Shielding Performance

The remarkable properties of carbon nanotubes(CNTs)have led to promising applications in the field of electromagnetic inter-ference(EMI)shielding.However,for macroscopic CNT assemblies,such as CNT film,achieving high electrical and mechanical properties remains challenging,which heavily depends on the tube-tube interac-tions of CNTs.Herein,we develop a novel strategy based on metal-organic decomposition(MOD)to fabricate a flexible silver-carbon nanotube(Ag-CNT)film.The Ag particles are introduced in situ into the CNT film through annealing of MOD,leading to enhanced tube-tube interactions.As a result,the electrical conductivity of Ag-CNT film is up to 6.82×10^(5) S m^(-1),and the EMI shielding effectiveness of Ag-CNT film with a thickness of~7.8μm exceeds 66 dB in the ultra-broad frequency range(3-40 GHz).The tensile strength and Young’s modulus of Ag-CNT film increase from 30.09±3.14 to 76.06±6.20 MPa(~253%)and from 1.12±0.33 to 8.90±0.97 GPa(~795%),respectively.Moreover,the Ag-CNT film exhibits excellent near-field shield-ing performance,which can effectively block wireless transmission.This innovative approach provides an effective route to further apply macroscopic CNT assemblies to future portable and wearable electronic devices.

Progress in the research on organic piezoelectric catalysts for dye decomposition

Organic contaminants have posed a direct and substantial risk to human wellness and the environment.In recent years,piezo-electric catalysis has evolved as a novel and effective method for decomposing these contaminants.Although piezoelectric materials offer a wide range of options,most related studies thus far have focused on inorganic materials and have paid little attention to organic materi-als.Organic materials have advantages,such as being lightweight,inexpensive,and easy to process,over inorganic materials.Therefore,this paper provides a comprehensive review of the progress made in the research on piezoelectric catalysis using organic materials,high-lighting their catalytic efficiency in addressing various pollutants.In addition,the applications of organic materials in piezoelectric cata-lysis for water decomposition to produce hydrogen,disinfect bacteria,treat tumors,and reduce carbon dioxide are presented.Finally,fu-ture developmental trends regarding the piezoelectric catalytic potential of organic materials are explored.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.