Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold

Gyro's drift is not only the main drift error which influences gyro's precision but also the primary factor that affects gyro's reliability.Reducing zero drift and random drift is a key problem to the output of a gyro signal.A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro(FOG).The coefficients are obtained from the three-layer wavelet packet decomposition.By setting the high frequency part which is greater than wavelet packet threshold as zero,then reconstructing the nodes which have been filtered out noise and interruption,the soft threshold function is constructed by the coefficients of the third nodes.Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective.Simulation results show that the random drift compensation is enhanced by 13.1%,and reduces zero drift by 0.052 6°/h.

Rolling element bearing instantaneous rotational frequency estimation based on EMD soft-thresholding denoising and instantaneous fault characteristic frequency

The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.

PROJECTED GRADIENT DESCENT BASED ON SOFT THRESHOLDING IN MATRIX COMPLETION

Matrix completion is the extension of compressed sensing.In compressed sensing,we solve the underdetermined equations using sparsity prior of the unknown signals.However,in matrix completion,we solve the underdetermined equations based on sparsity prior in singular values set of the unknown matrix,which also calls low-rank prior of the unknown matrix.This paper firstly introduces basic concept of matrix completion,analyses the matrix suitably used in matrix completion,and shows that such matrix should satisfy two conditions:low rank and incoherence property.Then the paper provides three reconstruction algorithms commonly used in matrix completion:singular value thresholding algorithm,singular value projection,and atomic decomposition for minimum rank approximation,puts forward their shortcoming to know the rank of original matrix.The Projected Gradient Descent based on Soft Thresholding(STPGD),proposed in this paper predicts the rank of unknown matrix using soft thresholding,and iteratives based on projected gradient descent,thus it could estimate the rank of unknown matrix exactly with low computational complexity,this is verified by numerical experiments.We also analyze the convergence and computational complexity of the STPGD algorithm,point out this algorithm is guaranteed to converge,and analyse the number of iterations needed to reach reconstruction error.Compared the computational complexity of the STPGD algorithm to other algorithms,we draw the conclusion that the STPGD algorithm not only reduces the computational complexity,but also improves the precision of the reconstruction solution.

Parameter Estimation of Multiple Frequency-Hopping Signals Based on Space-Time-Frequency Analysis by Atomic Norm Soft Thresholding with Missing Observations

In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals.The hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components.Then,the frequency of multiple signal components can be estimated precisely by AST within each segment.After obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be realized.Results of simulation show that the proposed method is superior to the existing technology.Even when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.

基于多残差注意力深度收缩网络的超微光图像增强方法

超微光成像可在极度黑暗的环境中给观察者提供近乎白昼的视觉体验,在许多民用和军事应用中起着至关重要的作用。超微光环境下拍摄的图像和视频通常存在亮度与对比度极低、噪声水平高、场景细节和色彩严重缺失等固有缺陷,近年来,深度学习为超微光成像的研究带来了新的机遇。文中采集并提供了一组实用性更强的超微光训练数据集,提出了一种多残差注意力深度收缩网络(Multi Residual Attention Shrinkage Network),以此实现了一种新的超微光成像方法。通过成功研制的小型化样机证实了该方法的工业量产前景。实现了基于通道注意力和空间注意力的残差内注意力机制,以及基于深度软阈值收缩的外注意力机制,不仅可以有效提取并还原极低照度环境下的图像细节信息,恢复场景真实色彩,而且可以有效去除此类环境下由成像设备感光不足带来的巨量噪声。实测效果显示该方法可对极低照度环境进行有效的增强且实时性高。通过与多种业界最新方法比较,文中方法在主观视觉体验以及客观参数两方面均表现更好。

基于灰度均值的自适应FAST角点检测优化算法

光照不均、突变引起的灰度变化会影响图像特征检测效果。为此,设计一种基于灰度均值的自适应FAST-9-12角点检测算法。首先,利用特征点的延展性设计一种小面积双重检测模板,减少像素点与中心点的比较次数,提高区域正检率和检测速度;其次,依据图像局部灰度均值,在每个像素点检测模板内自适应调整阈值,避免灰度变化影响检测效果;最后,根据柔性非极大值抑制的思想设计角点半径抑制原则,筛选鲁棒性更强的角点。在Inria遥感影像数据集上的实验结果表明,FAST-9-12角点检测速度比FAST-12-16,FAST-9-16两种模板提高22%左右,因自适应阈值的提取方式不易受光照影响,检测准确率分别提高4.16和3.11个百分点,实现了图像特征快速和精准检测。

基于软阈值降噪的脉冲卷积神经网络轴承故障诊断方法

针对工业场景下滚动轴承信号易受噪声干扰,导致故障诊断准确率低和稳定性差的问题,本文提出一种基于软阈值降噪的脉冲卷积神经网络诊断方法。该方法使用软阈值滤波去噪,运用带时间标签的卷积层处理二维信号,增强动态特征提取能力。同时,通过引入IF和LIF神经元实现对时域和频域信息的联合编码,并采用替代梯度法进行端到端训练。实验结果显示,在信噪比为6dB时,所提方法的诊断准确率达100%,在信噪比为-6dB时诊断准确率达77.33%,优于其他常用方法,表明所提方法在噪声下具有良好的诊断效果和稳定性。

基于一维残差收缩网络的电能质量复合扰动识别

电网中强噪声的干扰会严重影响电能质量复合扰动识别,为提高电能质量复合扰动识别准确率,提出一种基于一维残差收缩网络的电能质量复合扰动识别方法。该方法能够以原始数据作为输入避免有效特征的丢失,采用子网络自动设置阈值对各个特征通道进行软阈值化,并通过加宽卷积层进一步增强网络抗噪性。仿真实验结果表明:所提方法在强噪声干扰下能快速准确识别电能质量复合扰动。

考虑局部自相似性的土地复垦规划空间预测算法

土地复垦规划时土地图像存在较大噪声,且不同地区的土地复垦情况可能存在空间异质性,无法达到理想空间预测效果。为此,提出一种考虑局部自相似性的土地复垦规划空间预测算法。利用低秩逼近处理方法对噪声污染的观测矩阵的核范数低秩逼近,得到矩阵的最小核范数,实现土地空间图像去噪;利用局部自相似性计算土地再利用贡献值,以此建立空间预测模型,计算子区域内栅格与所选区域栅格差异程度;利用ArcGIS软件计算评价因子,以此划分等级,预测土地复垦规划空间。由实验结果可知,该算法预测的空间规划基本适宜度比例最大,其次是不适宜度、中度适宜度、临界适宜度,与理想规划结果一致。

基于视觉传达技术的无参考低光照图像增强方法

为了优化低光照图像增强效果,提出基于视觉传达的无参考低光照图像增强方法。所提方法通过全景成像技术重构低光照图像,并采用计算机原理模拟光学成像系统,引入多重分形法和半软阈值指数衰减法去除重构图像上的噪声。通过亮暗双通道系数计算图像增强过程中的最佳透射率,并以此透射率对低光照图像展开增强处理。实验结果表明,所提方法重构图像完整、图像整体质量高、SIMM值接近于1,在不影响图像清晰度的前提下实现了图像去噪,对低光照图像的增强效果明显,避免了增强过程中的图像曝光,做到了图像细节的完整保留。

A Model-Based Soft Decision Approach for Speech Enhancement

Many speech enhancement algorithms that deal with noise reduction are based on a binary masking decision(termed as the hard decision), which may cause some regions of the synthesized speech to be discarded. In view of the problem, a soft decision is often used as an optimal technique for speech restoration. In this paper, considering a new fashion of speech and noise models, we present two model-based soft decision techniques. One technique estimates a ratio mask generated by the exact Bayesian estimators of speech and noise. For the second technique, we consider one issue that an optimum local criterion(LC) for a certain SNR may not be appropriate for other SNRs. So we estimate a probabilistic mask with a variable LC. Experimental results show that the proposed method achieves a better performance than reference methods in speech quality.

基于改进深度残差收缩网络的旋转机械故障诊断

旋转机械振动信号在多层深度学习过程中会出现退化和过拟合现象,同时含噪数据样本也会使模型故障诊断正确率偏低,数据样本不平衡会引起模型训练具有倾向性,针对以上一系列问题,提出了一种基于改进型深度残差收缩网络(DRSN)的旋转机械故障诊断方法。首先,对多故障、长时间序列数据样本进行了矩阵化处理,得到了模型容易接受的多维度灰度图故障样本;针对旋转机械从正常状态到故障状态的机械老化过程,采用了多点随机采样方法,构建了全寿命周期数据样本,用于后续的故障诊断;然后,在卷积神经网络(CNN)的基础上,通过引入残差项、注意力机制和焦点损失函数,构建起了多层深度残差收缩网络,对旋转机械进行了故障诊断(其中,残差项降低了训练过程中样本数据的特征损失,避免了模型的退化和过拟合;注意力机制和软阈值化自动设置噪声阈值,降低了噪声对故障诊断精度的影响;焦点损失函数修正了模型训练的倾向性,提高了模型训练效率和灵敏性);最后,利用滚动轴承数据库样本对模型的性能进行了实例验证。研究结果表明:DRSN模型在训练过程中没有出现明显的退化现象,能够始终保持较高的训练效率和故障诊断精度,有效避免了噪声干扰,在不平衡数据集上修正了模型训练的倾向性。与其他模型相比较,DRSN多层模型的平均故障诊断精度提高约1%~6%。

Soft Handover Probability Determination Considering New Direction of Motion

This paper presents prior determination of soft handover probability considering new direction of motion of mobile station (MS) coinciding with gravitation point of cells. Our simulation results for 3-cell scenario and considered new direction of MS motion can be potentially used as advance input to soft handover algorithms to minimize number of handovers.

一种NLF-对数小波阈值的脉冲干扰降噪方法

在脉冲噪声背景下,传统信号降噪方法性能退化,无法去除强脉冲噪声,而且会出现计算复杂度增加的问题.本文提出一种基于非线性滤波(Nonlinear Filtering, NLF)和小波阈值去噪的联合算法,该方法利用非线性滤波算法抑制强脉冲噪声点干扰,通过改进的对数小波阈值函数去噪方法进行全局处理,快速而且高质量的完成对信号降噪.实验结果表明,本文所提算法对脉冲噪声背景下的多种信号类型都具有可行性,能够获得更高的峰值信噪比,具有很好的降噪效果.

压缩感知重构算法的两步深度展开策略研究

针对压缩感知中重构算法的深度展开问题,提出了一种两步深度展开策略(two-step deep unfolding,TwDU)。已有深度展开重构算法通常依赖前一步估计值估计当前值,TwDU对已有深度展开重构算法增加估计深度,依赖于前两步估计值估计当前展开值。TwDU对已有深度展开算法前两步估计值增加了两个训练权重。训练权重优化利用了信号估计值之间的相关特性,可以随着数据的特性自我学习和调整,所提TwDU策略应用于可学习迭代软阈值算法(learned iterative soft thresholding algorithm,LISTA)、可训练迭代软阈值算法(trainable iterative soft thresholding algorithm,TISTA)、可学习近似消息传递算法(learned approximate message passing,LAMP)等已有深度展开算法。通过在一维和二维稀疏信号的仿真验证,TwDU策略在重构精度和收敛速度上都更具有明显优势。

基于深度残差收缩网络的油气柱高度预测

油藏的含油气柱高度在很大程度上反映了圈闭中油气层的丰富程度。石油含量高度的估计,对于钻前储量评价、井位优化部署等都有着一定重要性。为了提升油气柱高度预测精度,展开基于神经网络模型的油气柱高度预测方法的研究,并侧重于一维残差收缩网络的研究,因为一维的卷积核侧重对每一维特征的提取,更符合本实验数据的特性;其次模型使用了残差块,该模块使用链接跳跃方法来绕过输入信息直接输出来保护信息完整性,进而缓解梯度损失和网络退化问题;软阈值作为非线性变换层插入到深层结构中,以消除不重要的特征,来提高从高噪声数据中学习特征的能力。同时,为了验证模型的有效性,对目前应用较为广泛的模型,如CNN、1DCNN、GoogLeNet、DenseNet、1DRSN在圈闭数据上的应用进行了比较和分析。1DRSN预测准确率达到84.0%,优于其他模型,表明该模型对油气柱高度预测有更加准确的结果。

SAE-DSN:一种具有去噪能力的室内定位回归模型

Wi-Fi指纹定位技术因其部署成本低、实用性强等优点被广泛应用,但接收信号强度(RSS)的波动给定位带来了一定的挑战.现有的定位方法只考虑了离线阶段指纹数据的去噪问题,而没有考虑在线阶段指纹数据噪声对定位的影响,当在线阶段收集到的指纹数据噪声过大时,将严重影响定位精度.为此,本文提出一种基于稀疏自编码器(SAE)和深度收缩网络(DSN)的室内定位回归模型,该模型使用稀疏自编码器提取指纹数据的鲁棒特征,同时将软阈值算法作为可训练的收缩函数嵌入到全连接网络中,为每个神经元进行去噪处理,有效减少了在线阶段指纹数据噪声的干扰.实验表明,提出的模型能够有效应对在线阶段高斯和非高斯噪声的干扰,相较于其它同类方法在定位精度上有明显提升.

一种新的基于深度置信网络的电能质量扰动分类方法

针对在噪声干扰下多重扰动识别正确率不高的问题,提出一种新的基于深度置信网络的电能质量扰动分类方法。首先,对电能质量扰动信号进行平稳小波多尺度变换,再利用软阈值函数处理估计小波系数重构原始信号,从而实现对电能质量扰动信号的去噪。再利用软阈值函数处理估计小波系数重构原始信号,从而实现对电能质量扰动信号的去噪。然后进一步提出利用深度置信网络对重构后的单一扰动信号和多重扰动信号进行分类识别。最后算例显示,即使在20 dB噪声干扰下,其分类正确率高达到93%以上。结果表明该方法对7种单一扰动和13种多重扰动信号的识别正确率均较高,验证该方法具有较强的抗噪声干扰能力。

基于深度残差收缩网络和迁移学习的变工况轴承故障诊断

为了更快速、准确地提取轴承的故障特征,本文在卷积神经网络的基础上,引入残差项并添加软阈值和注意力机制,构建深度残差收缩网络,提取轴承的故障特征信息;并且为了避免出现神经元坏死现象,使用LeakReLU代替ReLU作为激活函数。由于轴承在实际应用中所处的工况并不固定,因此本文通过迁移学习方法,将训练的网络模型应用到不同工况中,并且对本文模型与传统的卷积神经网络模型在不同工况下轴承故障诊断的效果进行对比,验证本文所提方法的有效性。

基于经验模态分解和改进小波软阈值降噪法的电站信号降噪研究

在对电站各类信号进行故障诊断时,由于其所处的复杂工作环境,采集设备得到的信号中经常包含背景中强噪声干扰,导致在后续信号关键特征的识别和处理时会出现较大困难。为降低噪声的影响,本文将经验模态分解与改进小波软阈值降噪法相结合进行电站信号的处理并进行了方法有效性验证。该方法的主要流程如下:首先,将信号进行经验模态分解,分离出若干个本征模态分量和残差。其次,对其与原始信号进行互相关分析,识别有用信号主导的分量和噪声主导的分量,并将噪声信号主导的分量进行改进小波软阈值降噪法处理后,将其与有用信号分量和残差进行重构。最后,将经验模态分解和改进小波软阈值降噪法降噪处理后的信号与传统只采用小波阈值降噪法处理后的信号进行评价,结果表明本文方法的信噪比更高、均方根误差更小、相关系数更高、平滑度更好,可有效消除背景噪声的影响。