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排序方式: 共有147条查询结果,搜索用时 734 毫秒
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Practical implementation of Hilbert-Huang Transform algorithm 总被引:12,自引:0,他引:12
Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better t 相似文献
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基于EMD与神经网络的机械故障诊断技术 总被引:2,自引:0,他引:2
经验模式分解 (EMD)是分析非线性、非平稳信号的有力工具 ,它将信号分解为突出了原信号的不同时间尺度的局部特征信息的内在模函数 (IMF)分量。本文通过将各 IMF分量输入到 BP网络中进行训练学习和故障诊断 ,比直接输入原信号可以提高 BP网络对故障诊断的准确率 ,而且减少了训练时间。 相似文献
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在分析比较经验模态分解(EMD)、小波变换(Wavelet)和独立分量分析(ICA)优缺点的基础上,提出一种新的EMD-Wavelet-ICA耦合模型。该模型充分利用了EMD的自适应性,对原始信号进行分解获得不同频率的模态函数(IMF),采用标准化模量的累计均值对IMF进行尺度划分;进而分别采用Wavelet和ICA对高频和低频IMF进行降噪,将降噪后的IMF进行多尺度重构,获得降噪后的信号;采用信噪比、标准差、偏差和相关系数等指标对降噪效果进行评价。仿真数据和GPS坐标序列的处理结果表明:与EMD模型和EMD-ICA模型相比,新模型的标准差、偏差均有不同程度的减小;信噪比和相关系数有一定程度的增大,可以获得更好的降噪效果。 相似文献
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《Chinese Astronomy and Astrophysics》2019,43(4):579-589
A voice enhancement algorithm based on the Empirical Mode Decomposition (EMD) and the improved spectral subtraction is proposed for the low-SNR (Signal Noise Ratio) shortwave time signal. This method is proposed to solve the problem that the shortwave time signal cannot be used for timing in complex noisy environments. The core idea of this method is to use the Hilbert-Huang Transform (HHT) algorithm to make the empirical mode decomposition on the noisy shortwave signal, and to select the intrinsic mode functions containing the shortwave signal information for the signal reconstruction by through the maximum correlation. Then, to make the spectral subtraction on the reconstructed signal to achieve the purpose of noise reduction. The experimental result shows that this method has a better noise reduction than the traditional methods. 相似文献
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希尔伯特-黄变换(HHT)是近年来发展起来的一种新的时间序列信号分析方法.该文在对HHT深入研究与充分肯定的基础上,发展了信号的镜像闭合延拓和包络的极值延拓两种方法.通过几个典型的例子检验了两种方法,并与Huang等(1998,1999)进行了比较,得到了令人满意的结果.镜像闭合延拓法根据信号端点的分布特性,把镜子放在具有对称性的极值位置,通过镜像法把镜内信号映射成一个周期性的信号,不存在端点,从根本上避免了经验模态分解和希尔伯特变换的端点问题.极值延拓法简单易行,具有与镜像闭合法相当的效果,在处理非对称波形信号时更显其优越性. 相似文献
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张永 《沙漠与绿洲气象(新疆气象)》2015,9(5):9-15
高分辨率的树木年轮是记录历史时期气候变化的良好生物载体,在古气候研究中被广泛应用。但年轮宽度与气候因子之间有着复杂的联系,这种关系受气候因子之间的相互制衡和因物种而异的树木生长节律的共同影响。在利用树木年轮开展历史时期气候变化的研究中,剔除树木年轮与年龄相关的生长趋势是准确获取气候信号的先决条件。然而,传统的和相对改进的一些树轮标准化方法在拟合并剔除树龄相关的趋势及非气候干扰信息方面仍存在一些问题。本文利用经验模态分解(EMD)方法进行树轮资料的标准化方法研究, 对已获得的树轮生长序列所记录的信息进行分解,得到一系列不同物理意义的本征模态分量,结合多样本信息的对比及生物学特性,深入解读各分量表征的气候变化、环境干扰及缓慢生长趋势项等不同物理意义,进而剔除非气候信息,得到可以准确反映气候变化的代用序列,并将该方法与目前广泛采用的标准化方法进行对比,分析不同方法的利弊所在,为进一步改进树轮标准化方法提供新思路。 相似文献
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Sonar generated acoustic signals transmitted in underwater channel for distant communications are affected by numerous factors like ambient noise, making them nonlinear and non-stationary in nature. In recent years, the application of Empirical Mode Decomposition (EMD) technique to analyze nonlinear and non-stationary signals has gained much attention. It is an empirical approach to decompose a signal into a set of oscillatory modes known as intrinsic mode functions (IMFs). In general, Hilbert transform is used in EMD for the identification of oscillatory signals. In this paper anew EMD algorithm is proposed using FFT to identify and extract the acoustic signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in a shallow water region.Data for analysis are collected at a depth of 5 m and 10 m offshore Chennai at the Bay of Bengal. The algorithm is validated for different sets of known and unknown reference signals. It is observed that the proposed EMD algorithmidentifies and extracts the reference signals against various ambient noises. Significant SNR improvement is alsoachieved for underwater acoustic signals. 相似文献