共查询到20条相似文献,搜索用时 78 毫秒
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针对在线采集时超声波检测信号中存在大量噪声,降低了材料内部缺陷诊断准确性的问题,提出了一种基于广义K+奇异值分解算法(K-SVD)和正交匹配追踪算法(OMP)相结合的超声回波信号去噪算法。该算法利用K-SVD算法将Gabor字典训练成能够最有效反映信号结构特征的超完备字典,然后基于训练完成的超完备字典,用OMP算法把一定数量的字典原子进行线性组合来构成原始信号,从而实现信号的去噪。通过仿真实验将本文方法与传统的小波阈值去噪方法进行了对比研究。实验结果表明,该方法对超声回波信号的去噪效果优于小波阈值去噪方法,且噪声越大对比越明显,不仅可更有效地滤除信号中的高斯白噪声,提高信噪比,且尽可能保留了原始信号有用信息。 相似文献
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低场核磁共振(low-field Nuclear Magnetic Resonance,low-field NMR)技术因其自身具有的独特优越性常被应用于极端条件下的测量,而且由于其采用的是永磁体,因而采集到的信号信噪比常常较低,在很大程度上影响了测量值的准确性.因此,如何去除混杂在信号中的加性高斯白噪声增加测量值的可靠性显得尤为重要.针对这一问题,国内外学者相继提出了众多优秀的去噪方法,其核心都是在不损失含噪信号中有效信息的基础上滤除掉夹杂在其中的噪声信号.本文在基于对小波变换理论分析的基础上,介绍了3种目前较为流行的用于低场核磁共振信号去噪的方法,分别是小波阈值去噪、模极大值去噪和小波系数相关性去噪,并给出了用于评价去噪效果的四个参数及其计算方法. 相似文献
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采用低场核磁共振技术进行检测时,接收到的回波信号微弱且信噪比低,真实的信号容易淹没在背景噪声中,严重影响到后续的反演等操作的准确性.针对这一问题,提出利用非局部均值滤波算法对CPMG(Carr Purcell Meiboom Gill)回波信号进行降噪的方法.首先,对算法中至关重要的参数选择的方法进行分析,提出了利用Stein无偏风险估计的自适应参数选取方法;然后,根据回波信号的特性对算法进行改进,即利用信号点数据方差的不同,自适应地求取各点进行非局部均值滤波时的相似窗宽度;最后,求取利用最优参数进行降噪后的CPMG回波信号.对仿真数据和真实数据的反演结果对比分析表明,该改进的非局部均值滤波算法能够取得更好的滤波效果,能够获得较优的反演谱. 相似文献
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拉曼光谱技术作为一种典型的光学检测方法,因其独特的非侵入性、快速、原位和极高的特异性,在生物分析、疾病诊断及分子识别等众多领域得到广泛应用.拉曼光谱的指纹特性使其成为生物医学分析领域的重要工具,但拉曼散射信号微弱,数据处理分析大量依赖分析人员、自动化处理能力低等因素都会极大影响该技术在实际中的应用.实验设备、环境产生的... 相似文献
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基于小波变换的木材近红外光谱去噪研究 总被引:3,自引:0,他引:3
木材近红外光谱常常被一系列噪声所污染,影响光谱分析结果。为了提高近红外光谱分析精度,需要对光谱数据进行预处理。光谱导数可以消除光谱背景干扰和基线漂移等因素影响,提高光谱分辨率,但导数光谱在增强信号的同时,也使信号噪声得到增强。应用小波变换对杉木木材近红外一阶导数光谱进行去噪研究,分别采用9点平滑法、25点平滑法、非线性小波硬阈值和软阈值法、9点平滑+小波变换法和25点平滑+小波变换法对光谱数据进行去噪研究。结果显示, 小波变换能够有效去除导数光谱中的噪声信号,保留光谱中的有效信息,提高光谱信噪比,提高光谱的分析能力,在木材近红外光谱分析中具有很好的应用前景。 相似文献
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《光学学报》2018,(10)
针对城市环境下三维激光雷达(LiDAR)点云数据密度不均匀、离群噪点多而不利于后期点云帧间匹配的问题,提出一种应用于城市环境下大规模散乱LiDAR点云的离群噪点滤除算法。该算法对传统的基于密度的噪声应用空间聚类(DBSCAN)算法进行改进,通过对三维点云进行体素栅格划分,创建了一个由栅格单元组成的集合,以此大幅减小每个对象在数据空间中邻域的搜索范围。改进后的算法能够快速发现各个聚类,使目标点云与离群点分离,从而剔除点云中的离群噪点。实验结果表明:所提算法能够实时处理点云数据,在保证点云三维几何特征的同时能有效识别并滤除点云中的离群噪点,降低点云规模,加快点云后续处理的效率,使帧间匹配的精确度提高了2倍,且匹配耗时仅为去噪处理前的1/3。 相似文献
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基于EMD-DISPO的Mie散射激光雷达回波信号去噪方法研究 总被引:2,自引:0,他引:2
激光雷达回波信号是典型的非稳态、非平稳信号,用传统的滤波方法难以对其进行有效地处理.利用经验模式分解方法(EMD)将信号按照不同的特征时间尺度分解为不同的IMF分量,将含有噪声的高频IMF分量剔除,可达到去噪的目的.但如果简单地将高频分量直接剔除,有可能造成有效信号的损失.可提出将EMD方法与Savitzky-Gola... 相似文献
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Zhong-Yu Li 《中国物理 B》2022,31(4):40502-040502
Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems. Accurate prediction can alleviate traffic congestion, and reduce environmental pollution. For the management department, it can make effective use of road resources. For individuals, it can help people plan their own travel paths, avoid congestion, and save time. Owing to complex factors on the road, such as damage to the detector and disturbances from environment, the measured traffic volume can contain noise. Reducing the influence of noise on traffic flow prediction is a piece of very important work. Therefore, in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction. At the same time, three denoising algorithms are compared to find the best combination mode. In this paper, the wavelet (WL) denoising scheme, the empirical mode decomposition (EMD) denoising scheme, and the ensemble empirical mode decomposition (EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data. In addition, we combine the denoising schemes with bidirectional long short-term memory (BILSTM) network to predict the traffic flow. The data in this paper are cited from performance measurement system (PeMS). We choose three kinds of road data (mainline, off ramp, on ramp) to predict traffic flow. The results for mainline show that data denoising can improve prediction accuracy. Moreover, prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods (BILSTM+WL, BILSTM+EMD, BILSTM+EEMD). The results for off ramp and on ramp show the same performance as the results for mainline. It is indicated that this model is suitable for different road sections and long-term prediction. 相似文献
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提出了一种基于非分样ridgelet标架的图像噪声滤除(UDRIFDA)的新算法。ridgelet标架的特点是:基函数不可分离变量且具有很强的方向性,能够实现对沿直线奇性的有效描述。离散非分样ridgelet标架是通过离散Radon变换切片上的一维非分样小波变换标架来实现的。由于非分样小波变换具有位移不变性,能够很好地刻画多尺度下一维信号的局部特征,基于一维非分样小波变换的软阈值去噪算法能够有效地降低一维信号急剧变化处所产生的震荡现象,故基于非分样ridgelet标架的图像滤噪算法能够大大降低恢复图像上的伪影,有效的克服了文献[1]中分样ridgelet标架滤噪算法(DRITDA)的缺陷。数值实验表明新算法较DRITDA和2D-DWT算法更能提高恢复图像的信噪比和视觉质量。 相似文献
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The focus of LiDAR data compression and reduction has been raised in recent years due to the dramatically huge amount of points cloud. To improve the disposal efficiency of LiDAR data, a data reduction method assisted by optical image for 3D reconstruction of building facade is proposed in this article. The method involves a series of procedures of “2D feature line extraction – 3D feature line converting – buffer area of LiDAR points”. The main issue here is finding out a LiDAR point dataset around the 3D feature segment converted from 2D feature lines, which benefits to improving the efficiency of post-processing based on LiDAR data. The reduced LiDAR data can be obtained with reliable structures and accurate geometric position. Furthermore, the experiment of the LiDAR data reduction was conducted over the Xingyuan Building in Nanjing Normal University. The reduction method in this paper proved to be suit for the regular objects modeling, on the basis of the reliable and rigorous mathematical models and computer algorithms, and was a fundamental and useful approach to improve the efficiency of 3D reconstruction of building with the higher modeling accuracy. 相似文献
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A semi-blind image restoration algorithm is proposed based on reduced non-convex approximation of Luminita Vese and Tony Chan's (C-V) denoising model. Compared with C-V denoising model, we modify the fidelity term and add a term on point spread function (PSF). The function depends on two variables: the image function to be restored u and the standard deviation of Gaussian kernel to be estimated σ. Then the problems consist in solving a system with two coupled equations. Compared with the Leah Bar's semi-blind image restoration model which must solve three coupled equations, our method only needs to solve two equations. Furthermore, the estimation of f by our algorithm is superior to Leah Bar's algorithm. The experimental results demonstrate that the proposed method is effective. 相似文献
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信号降噪与特征提取是超声检测数据处理的关键技术.基于超声信号有特定结构而噪声和超声信号的结构无关,本文提出一种旨在解决强噪声背景下超声回波的参数估计和降噪问题的方法.该方法将超声回波的参数估计和降噪问题转换为函数优化问题,首先根据工程经验建立超声信号的双高斯衰减数学模型,然后根据观测回波和建立的超声信号模型确定目标函数,接着选择人工蜂群算法对目标函数进行优化从而得到参数的最优估计值,最后由估计出的参数根据建立的超声信号数学模型重构出无噪的超声估计信号.通过仿真和实验表明本文方法可以准确估计出信噪比大于-10 dB的含噪超声回波中的无噪信号,且效果优于基于自适应阈值的小波降噪方法和经验模态分解方法;此外相比常用的指数模型和高斯模型,本文提出的双高斯衰减超声信号模型与实测超声信号更接近,其均方误差为9.4×10~(-5),波形相似系数为0.98. 相似文献
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为了降低噪声对实测红外光谱信号的影响,引入了一种非下采样小波变换的红外光谱数据去噪方法。采用非下采样小波变换对原始光谱信号进行多尺度分解,提取信号的多尺度细节特征;根据光谱信号和噪声在不同尺度上的差异,通过应用变分偏微分方程方法调整分解后的各子带系数;重构各子带就可以将原始光谱信号中真实信号和噪声分离,从而达到剔除噪声的目的。通过两组实验对比传统小波和该方法针对红外光谱数据的消噪效果,实验结果表明:非下采样小波变换在红外光谱数据去噪方面具有明显的优势,不仅能够有效地去除噪声,很好地保持信号的形状,并且均方误差较小;在实际的红外光谱数据处理中能够获得较好的去噪效果。 相似文献
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为了降低噪声对实测红外光谱信号的影响,引入了一种非下采样小波变换的红外光谱数据去噪方法。采用非下采样小波变换对原始光谱信号进行多尺度分解,提取信号的多尺度细节特征;根据光谱信号和噪声在不同尺度上的差异,通过应用变分偏微分方程方法调整分解后的各子带系数;重构各子带就可以将原始光谱信号中真实信号和噪声分离,从而达到剔除噪声的目的。通过两组实验对比传统小波和该方法针对红外光谱数据的消噪效果,实验结果表明:非下采样小波变换在红外光谱数据去噪方面具有明显的优势,不仅能够有效地去除噪声,很好地保持信号的形状,并且均方误差较小;在实际的红外光谱数据处理中能够获得较好的去噪效果。 相似文献
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The electromagnetic ultrasound is used in the detection of interfaces of the adhesive multilayer structures to solve the unstable coupling problem in ultrasonic testing by traditional piezoelectric transducers. Based on the analysis of the transforming mechanism of electromag-netic ultrasound energy and the resultant dead zone from mutual inductance of the transducer, the wavelet filtering by soft-thresholding and adaptive noise canceling methods are used simul-taneously to the detected electromagnetic ultrasonic signals to overcome the drawbacks of the low signal to noise ratio (SNR) and the wide intrinsic dead zone of the transducer. Processed results in the interface detection of a three layered adhesive sample of steel and rubber materials demonstrate that the wavelet filtering enhances the SNR about 12dB while the adaptive noise canceling narrows the dead zone effectively. 相似文献
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