共查询到19条相似文献,搜索用时 140 毫秒
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介绍了一种适合于管道应力波检测的基于小波变换的去噪方法。利用小波变换多尺度分析的优点。根据有用信号和噪声在小波变换不同尺度下的传递特性的不同,进行小波系数阈值选取后,对剩余小波系数重建信号,得到有用信号的波形。结果表明,它能较好地抑制噪声,使信号的信噪比明显提高。 相似文献
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水下声信号分类是水声学研究的一个重要方向。一个有效的特征提取和分类决策方法对水声信号分类技术至关重要。文章将鱼声、商船辐射噪声和风关噪声三类实测的水声信号在小波包分解的基础上提取时频图特征,并搭建了一个七层结构的卷积神经网络作为分类器。研究结果表明:三种水声信号的小波包时频图特征结合卷积神经网络在不同测试集可达到(98±1)%的总体准确率。因此,小波包时频图特征结合卷积神经网络的水声分类方法可望推广至更多水声信号分类。该研究结果可为水声信号的分类识别研究提供应用参考。 相似文献
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水声目标分类识别是公认的水声信号处理难题,船舶辐射噪声是一种非线性非平稳信号,具有一定的混沌特性,更好地认识船舶辐射噪声的非线性性质,有助于更好地寻找有效的水声目标检测及识别算法。为了解决水声目标的分类识别问题,提出了利用小波包分形和支持向量机组合进行水声目标识别。利用小波包分解得到目标辐射噪声不同频带内信号分形维数作为特征矢量,并输入到支持向量机实现目标分类,实验结果表明,小波包分形和支持向量机的结合有比较好的分类识别效果,有一定的实际应用价值。 相似文献
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非高斯水声瞬态信号双通道Power-Law检测 总被引:2,自引:0,他引:2
根据水声瞬态信号的特点,在分析能量检测和高阶谱检测的特性基础上,研究了基于能量检测和基于高阶谱的Power-Law检测方法,将上述两种检测器联合构成一种双通道Power-Law检测器,更好地利用了水声瞬态信号中的统计信息。通过计算机仿真试验,证明了该检测器的有效性,它能在更大程度上适应复杂水声环境变化的要求,改善和提高水声瞬态信号的检测性能。 相似文献
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基于小波包的振动信号去噪应用与研究 总被引:6,自引:1,他引:6
小波包分析算法对上一层的低频部分和高频部分同时进行细分,具有更为精确的局部分析能力。基于小波包变换的优良时频分析特性,论述小波包分析的基本原理,研究小波包在振动检测信号消噪处理中的应用,给出应用小波包变换对基于MSP430F449单片机的信号采集电路所检测到的振动信号进行消噪处理的实例。结果表明小波包变换的方法可以降低系统噪声影响,通过变换分解出高频噪声部分,利用小波包收缩的阈值量化方法能够更好地去掉高频部分,从而达到有效去除信号中噪声的目的。 相似文献
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Kubinyi M Kreibich O Neuzil J Smid R 《IEEE transactions on ultrasonics, ferroelectrics, and frequency control》2011,58(5):1027-1036
An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering, and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for electromagnetic acoustic transducer (EMAT) pulse-echo signals, and therefore do not exploit their unique impulse nature. The approach to ultrasonic signal filtering proposed in this paper is based on stationary wavelet packet denoising with a threshold influenced by several information sources: a statistical echo detection, the amplitude distribution of the wavelet transform coefficients, and a priori known system frequency characteristics. The proposed method was evaluated on signals measured with EMAT probes and under various SNR conditions; it outperforms the wavelet transform with the Stein unbiased risk estimate (SURE) threshold estimation method and split-spectrum processing (SSP). The results indicate SNR enhancement of 19 dB with real EMAT data. 相似文献
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滚动轴承振动信号的小波奇异性故障检测研究 总被引:12,自引:3,他引:9
该文以滚动轴承振动信号为分析对象 ,基于小波奇异性分析原理进行滚动轴承故障检测新方法的研究。通过求解待测信号的小波变换极大模来检测和识别信号中奇异点位置和奇异性大小 ,以及对噪声极大模的抑制处理 ,达到抑制或消除噪声的目的 ;最后 ,在剩余小波极大模的基础上进行信号重构 ,展现原待测信号中的故障信号模式。通过对铁路货车车轮用滚柱轴承振动信号的分析表明 ,此方法在大幅度地提高信噪比的同时 ,对由轴承损伤冲击造成的信号突变仍保持了较高的灵敏度和分辨率。为滚动轴承故障检测打下了良好的基础。 相似文献
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针对轴承振动信号利用小波单奇异点检测无法克服噪声影响的不足,提出利用小波模极大值分析信号奇异性变化进而进行轴承故障检测的方法。实验中对信号的模极大分形指数,模极大分形指数熵,Lipschitz指数以及Lipschitz指数熵等奇异特征进行分析比较,实验结果表明这些特征都能有效克服噪声影响实现故障检测,但模极大曲线数最能体现故障特征且检测效果最好。将该方法同基于小波包能量谱特征和小波单奇异点检测的方法进行比较,结果表明本文建议的方法在检测时间及检测率上都有显著提高。 相似文献
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水声侦察的核心问题是在无先验知识条件下捕获其他平台发射的脉冲信号,单频(Continuous Wave,CW)信号和调频(Frequency Modulation,FM)信号是常用的水声探测脉冲。功率谱熵算法能有效检测低信噪比的CW信号,但对FM信号性能不佳,分数阶傅里叶变换(Fractional Fourier Transform,FRFT)则能聚集FM信号能量。利用FRFT的性质,结合功率谱熵算法,设计了分数阶功率谱熵检测器,可在分数阶域实施未知脉冲信号检测。理论分析了FRFT对FM信号的能量聚集作用,优化了FRFT阶数搜索方法。仿真实验以及海试数据处理结果证实检测器对FM信号性能良好,且对CW信号的侦察检测性能无影响。通过门限学习,检测器可实现对未知水声脉冲信号的统一自动检测。 相似文献
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《成像科学杂志》2013,61(7):408-422
AbstractImage fusion is a challenging area of research with a variety of applications. The process of image fusion collects information from different sources and combines them in a single composite image. The composite fused image can better describe the scene than any of the source images. In this paper, we have proposed a method for noisy image fusion in contourlet domain. The proposed method works equally well for fusion of noise free images. Contourlet transform is a multiscale, multidirectional transform with various aspect ratios. These properties make it more suitable for image fusion than other conventional transforms. In the proposed work, the fusion algorithm is combined with a denoising algorithm to reverse the effect of noise. In the proposed method, we have used a level dependent threshold that is based on standard deviation of contourlet coefficients, mean and median of the absolute contourlet coefficients. Experimental results demonstrate that the proposed method performs well in the presence of different types of noise. Performance of the proposed method is compared with principal components analysis and sharp fusion based methods as well as other fusion methods based on variants of wavelet transform like dual tree complex wavelet transform, discrete wavelet transform, lifting wavelet transform, multiwavelet transform, stationary wavelet transform and pyramid transform using six standard quantitative quality metrics (entropy, standard deviation, edge strength, fusion factor, sharpness and peak signal to noise ratio). The combined qualitative and quantitative evaluation of the experimental results shows that the proposed method performs better than other methods. 相似文献
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Bilgen M 《IEEE transactions on ultrasonics, ferroelectrics, and frequency control》1999,46(6):1407-1415
A new signal processing algorithm based on a wavelet transform (WT) is proposed for instantaneous strain estimation in acoustic elastography. The proposed estimator locally weighs ultrasonic echo signals acquired before tissue compression by a Gaussian window function and uses the resulting waveform as a mother wavelet to calculate the WT of the postcompression signal. From the location of the WT peak, strain is estimated in the time-frequency domain. Because of the additive noise in signals and the discrete sampling, errors are commonly made in estimating the strain. Statistics of these errors are analyzed theoretically to evaluate the performance of the proposed estimator. The strain estimates are found to be unbiased, but error variances depend on the signal properties (echo signal-to-noise ratio and bandwidth), signal processing parameter (time-bandwidth product), and the applied strain. The results are compared with those obtained from the conventional strain estimator based on time-delay estimates. The proposed estimator is shown to offer strain estimates with greater precision and potentially higher spatial resolution, dynamic range, and sensitivity at the expense of increased computation time. 相似文献