AMicroseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA

The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher.

Reduction of ultrasonic echo noise based on improved wavelet threshold de-noising algorithm for friction welding

In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.

Implementation of Adaptive Wavelet Thresholding Denoising Algorithm Based on DSP

By utilizing the capability of high-speed computing,powerful real-time processing of TMS320F2812 DSP,wavelet thresholding denoising algorithm is realized based on Digital Signal Processors.Based on the multi-resolution analysis of wavelet transformation,this paper proposes a new thresholding function,to some extent,to overcome the shortcomings of discontinuity in hard-thresholding function and bias in soft-thresholding function.The threshold value can be abtained adaptively according to the characteristics of wavelet coefficients of each layer by adopting adaptive threshold algorithm and then the noise is removed.The simulation results show that the improved thresholding function and the adaptive threshold algorithm have a good effect on denoising and meet the criteria of smoothness and similarity between the original signal and denoising signal.

De-Noising of ECG Signals by Design of an Optimized Wavelet

In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB.

New Wavelet Threshold Denoising Method in Noisy Blind Source Separation

In general conditions, most blind source separation algorithms are established on noisy-free model and ignore the noise that affects the quality of separated sources. Firstly, this paper introduces an improved natural gradient algorithm based on bias removal technology to estimate the demixing matrix under noisy environment. Then the discrete wavelet transform technology is applied to the separated signals to further remove noise. In order to improve the separation effect, this paper analyzes the deficiency of hard threshold and soft threshold, and proposes a new wavelet threshold function based on the wavelet decomposition and reconfiguration. The simulations have verified that this method improves the signal noise ratio （SNR） of the separation results and the separation precision.

Implementation of GPR Signals De-Noising Based on DSP

An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.

基于POA-VMD-WT的MEMS去噪方法

针对MEMS传感器所测得的加速度和角速度输出信号噪声较大问题,提出一种基于鹈鹕优化算法(pelican optimization algorithm,POA)的变分模态分解(variational mode decomposition,VMD)结合小波阈值(wavelet threshold,WT)的去噪方法。首先利用POA对VMD的参数组合进行优化选择,然后应用POA-VMD将含噪信号自适应、非递归地分解为一系列本征模态函数(intrinsic mode function,IMF)。再通过计算每个IMF的余弦相似度对IMFs进行分类,根据计算结果将IMFs分为噪声主导分量与信号主导分量,对分类后的噪声主导分量进行改进小波阈值去噪处理,最后对处理后的噪声分量与信号主导分量进行重构,获得降噪后的MEMS传感器信号。静态和动态实验结果表明,该方法去噪处理后信号的信噪比分别提高12和10 dB,均方误差分别降低75.5%和46.6%,去噪效果显著,能够提高MEMS传感器的精度。

基于空间变换技术的脉冲激光声信号检测

针对目前脉冲激光声信号检测方法极易受到环境噪声的影响,导致检测精确度不理想的问题。提出基于空间变换技术的脉冲激光声信号检测方法。首先,采用小波阈值法去除脉冲激光声信号中的噪声;其次,利用球傅里叶空间变换技术对声信号中的声源展开定位,获取声信号的准确位置;最后,分别提取声信号中的比值特征、比重特征、频率特征三种参数,在此基础上通过SVM算法对其展开分类识别,以此实现脉冲激光声信号的有效检测。实验结果表明,所提方法的声信号检测精度超过96%。

次同步振荡在交直流电网中传播的关键影响因素

随着“双高”电力系统的发展,次同步振荡问题日益凸出,亟需研究交直流线路次同步振荡传播的关键影响因素。从系统响应量测时序数据着手,提出了一种次同步振荡传播关键影响因素定量分析方法。首先,基于自适应噪声完全集合经验模态分解(complete ensemble empirical mode decomposition, CEEMDAN)的改进小波阈值去噪方法对量测数据进行降噪处理,减少噪声对Prony分析的影响;其次,基于次同步振荡传播各影响因素的相关系数和互信息量建立相关性评价组合模型;最后,计算交直流不同参数在综合模型中的评价指标,得出次同步振荡在交直流线路中传播的关键影响因素。通过在PSCAD搭建2区域4机系统进行分析,结果表明:影响交流线路次同步振荡传播的极强相关参数为交流线路潮流,影响直流线路次同步振荡传播的极强相关参数为次同步振荡频率下交流线路阻抗特性。

基于改进粒子群算法的UWB雷达人体动作识别研究

针对雷达信号中的杂波干扰及样本数量对人体动作识别精度的限制,提出一种基于改进粒子群算法(particle swarm optimization,PSO)优化支持向量机(support vector machine,SVM)模型的超宽带(ultra-wideband,UWB)雷达人体动作识别算法。利用动态目标指示(moving target indication,MTI)与小波阈值滤波对接收到的UWB回波信号进行预处理,消除回波信号中的杂波和噪声对人体动作识别的影响;结合二维离散小波包分解(two dimensional discrete wavelet packet decomposition,2D-DWPD)与奇异值分解(singular value decomposition,SVD),对预处理后的雷达信号进行特征提取和降维;提出一种改进粒子群算法,优化SVM模型的相关参数进行识别和分类。实验结果表明,提出的算法准确率可达到96.25%,具有良好的识别性能。

港口高压开关柜局部放电信号自适应小波去噪方法

为了有效抑制港口高压开关柜局部放电实测信号中存在的白噪声,文中提出一种基于小波的自适应阈值去噪算法。首先对小波阈值去噪算法中的阈值选取及阈值函数进行优化,通过添加变量增加算法的适用性和灵活性,并使用改进后的粒子群算法(SPSO)对添加的变量进行最优值求解从而实现小波分解层数、小波阈值和阈值函数的自适应选取;其次对仿真信号与实测信号进行去噪。结果表明:与传统软、硬阈值函数去噪相比,使用文中所提算法去噪后的信号信噪比分别提高了5.31 dB和2.38 dB。由去噪后信号的时域图可以看出,与其他几种算法相比本文提出的算法不仅去噪效果良好,还能极大地保留信号中的有效成分。

基于小波阈值降噪算法的滚动轴承故障诊断

滚动轴承因平稳的运行特性广泛用于工业生产领域,其安全稳定运行对工业生产有重要意义.针对滚动轴承的故障诊断问题,提出基于小波阈值降噪(wavelet threshold denoising,简称WTD)算法.研究结果表明:相对于其他3种算法,WTD算法具有较强的故障诊断能力.因此,WTD算法具有有效性.

基于HOG和TSO-SVM的水电机组轴心轨迹智能识别

水电机组的轴心轨迹能够反映机组不同的运行状态,为了提高轴心轨迹的识别率,准确判断机组运行状态,本文提出方向梯度直方图(Histogram of Oriented Gradient, HOG)结合由瞬态搜索优化(Transient Search Optimization, TSO)算法优化的支持向量机(Support Vector Machine, SVM)的方法。将轴心轨迹信号经改进小波阈值方法去噪后,生成轴心轨迹图像,之后提取图像HOG特征,经主成分分析(Principal Components Analysis, PCA)降维处理后,利用TSO-SVM对降维后的特征进行分类识别。结果表明所提方法能够很好地识别不同状态的轴心轨迹,具有识别准确率高和识别速度快的特点。

自适应差分阈值的心电信号R峰检测算法

针对传统差分阈值算法的不足,设计了一种自适应差分阈值算法实现对R峰的准确检测。该算法能够依据R峰高幅值、高斜率的特征自适应更新信号检测阈值,并且根据R-R峰之间的心率间隔特征,设计回溯判定机制提升算法的灵敏度和适用范围。文中选用MIT-BIH心率失常数据库中的心电数据作为信号样本,采用带通滤波和小波变换相结合的方法完成对心电信号的去噪,依据自适应阈值检测实现对心电信号的R峰特征提取。从实验结果来看,自适应后的差分阈值法将心电信号的检测准确率提高到了99.59%,灵敏度达到了99.64%,减少了R峰检测过程中漏检、误检情况的出现,提高了检测的准确性。

自适应新小波阈值函数中子图像去噪方法

中子射线照相技术是一种重要的无损检测技术,但在中子成像过程中会受到一些噪声因素的干扰,导致图像质量降低,不利于后期研究。本研究提出一种基于粒子群优化算法(particle swarm optimization,PSO)的新小波阈值函数去噪方法来降低噪声对中子图像的影响。其基本思想是将PSO算法与小波阈值函数去噪相结合。通过PSO算法寻找适合当前图像去噪的最优调节因子。Matlab软件实验的结果表明,新方法在去除高斯噪声、泊松噪声较其他四种对比方法可以明显提高噪声图像的峰值信噪比(PSNR)和降低噪声图像的均方误差(MSE),有效提高中子图像的质量。

改进小波阈值与优化BiLSTM组合的大坝变形预测方法

变形是反映大坝结构状态变化的重要指标。由于变形数据的非线性特点和其背后复杂的机理,提升变形的预测精度对大坝安全及结构控制具有重要意义。为此,基于融合建模理念提出了一种组合的大坝变形预测方法,该方法结合了改进小波阈值去噪与鹈鹕优化算法(POA)优化的双向长短期神经网络(BiLSTM)。首先,采用改进小波阈值去噪法对变形实测数据序列进行处理;其次,通过POA搜索最优超参数组合用于优化BiLSTM模型;最后,基于最优超参数下的BiLSTM模型进行大坝变形预测。工程实例表明,改进小波阈值法具有更好的去噪效果,POA-BiLSTM能够准确预测大坝变形。在最终测试集上平均MAE、MAPE、RMSE、R^(2)分别为0.244、0.041、0.301、0.906。相较于其他方法,表现出更高的预测准确性和稳健性,可为大坝变形监测提供参考。

改进MUSIC算法的超声波测风方法研究

针对传统超声波测风装置测风精度不高、抗噪声能力弱,提出了一种改进多重信号分类(multiple signal classification,MUSIC)算法的超声波测风方法。采用一种弧形6阵元超声波传感器阵列的测风结构,推导其阵列流型;在此基础上,添加小波阈值降噪算法提高信号信噪比,降低噪声信号协方差矩阵的秩;再使用PHAT加权广义互相关时延估计算法以提高时延估计的准确性,同时根据时延关系对传统MUSIC算法矢量矩阵进行改进;最后通过MUSIC算法实现对风速风向的测量。理论分析与仿真结果表明:改进后的MUSIC算法具有较好的抗噪性能和较高的风参数测量精度,测量风速绝对误差达到0.15 m/s,风向绝对误差达到2°,可以应用于对风参数要求较高的场景。

基于WTD-PPAR的中国碳排放预测研究

碳排放预测对我国的碳排放规划和政策颁布具有一定的参考价值。使用小波阈值去噪模型对碳排放数据建模能够有效缓解其存在的非线性和波动性问题,筛选得到数据的有效增长趋势。在此基础上,利用碳排放数据的时间序列特征建立其投影寻踪自回归(PPAR)模型,从而对碳排放进行预测分析。将该模型与未进行去噪的PPAR、BP、LSSVM、SVR、LSTM模型进行对比,发现WTD-PPAR模型的预测精度更高,预测结果更准确。分析结果显示,我国碳排放将在2029年达到峰值,约为1081.89 mt,能够实现碳达峰的目标。

基于Mask R-CNN的电力关键设备载波运行状态检测

为解决因载波信号大幅波动造成的电力设备运行状态检测失准问题,提出基于Mask R-CNN的电力关键设备载波运行状态检测方法。在Mask R-CNN网络模型中,通过定义小波基向量的方法确定电力载波暂态系数的计算结果。联合电量阻抗特征,求解连续阈值区间表达式,将获取到的电力载波信号放置在已定义阈值区间内。计算奇异值检测系数的取值条件,完成电力关键设备载波运行状态检测。对比实验结果表明,所提方法可以将电力载波信号波动幅值控制在±17 dB之间,对载波信号波动行为起到了明显的抑制作用,能够提升电网主机对电力设备运行状态检测的准确性。

基于改进小波阈值的自行火炮信号降噪方法研究

为有效滤除自行火炮柴油发动机振动信号中的噪声,提出基于改进小波阈值的振动信号降噪方法。运用改进的自适应噪声完备集成经验模态分解处理原始振动信号得到各个本征模态函数分量,通过多尺度排列熵检测分量的随机性,筛选出需要降噪的分量,使用改进的小波阈值降噪方法对筛选出的分量降噪,重构降噪后的分量与无需降噪的分量,获得所需的振动信号。同时,针对人工选取多尺度排列熵中各参数对计算结果影响较大的问题,提出一种改进麻雀搜索算法对多尺度排列熵中各参数进行寻优。分别通过仿真信号和实验室实测数据验证所提方法的可行性和有效性,结果表明:与小波阈值降噪、多小波相邻系数降噪和ICEEMDAN-MPE-小波阈值降噪方法相比,所提方法应用于仿真信号时信噪比分别提升5.989 4 dB、6.078 7 dB和1.565 3 dB;应用于实验室实测数据时,降噪误差比分别降低22.143 3、6.834 9和0.722 7,为自行火炮振动信号降噪提供一种新的思路。