共查询到17条相似文献,搜索用时 156 毫秒
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二进小波变换极大模法用于分析化学信号的滤噪 总被引:2,自引:0,他引:2
实验数据的滤噪在分析化学领域中具有重要的意义。小波变换技术具有很强的信号分离能力,容易把随机噪声从信号中分离出来,从而提高信号的信噪比。本文使用滤噪方法不同于传统离散小波变换方法,而是通过引入二进小波变换和李氏指数的概念,根据噪声与有用信号的极大模截然不同的特征,实现信号滤噪。实验数据的仿真结果研究也证明该方法的可行性。 相似文献
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一种基于免疫算法的新型因子分析算法 总被引:3,自引:0,他引:3
基于免疫算法的基本思想,提出了新的免疫主成分分析法(IPCA),该方法将免疫算法中抗体对抗原的消除运算应用于二维数据矩阵的正交分解,可得到矩阵的特征值和特征向量.结果表明,IPCA与传统的主成分分析法比较,对HPLC-DAD模拟信号的计算结果基本一致.对HPLC-DAD实验信号的解析结果表明,将IPCA与窗口因子分析技术结合比传统的WFA具有更强的解析能力. 相似文献
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一种新的小波滤波方法在化学谱图信号滤噪中的应用 总被引:2,自引:0,他引:2
仪器分析测定中,噪声的存在往往影响分析的准确度和仪器的检出限。小波变换多分辨分析的特性使得它成为一种很好的滤噪方法。基于小波分解后信号与噪声的小波系数随尺度变化规律不同的特性,提出了一种新的滤波滤方法-空域相关法,即通过不同尺度上相关系数模值与小波系数模模值的比较,达到滤波滤的目的。本文提出的方法具有无需人为选定无需人为选定滤噪阈值和小波函数、方法简单、失真度小等优点,可以大在提高信号的信噪比。模拟数据和ICP-AES实验数据证明了该方法的有效性。 相似文献
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基于小波和轮廓提取的色谱基线算法研究 总被引:1,自引:0,他引:1
把基于小波和轮廓提取的基线算法引入了色谱基线提取领域;基于轮廓提取的算法为:通过构造一个滑动窗,沿着色谱数据滑动求窗内的最小值。把这些最小值插值平滑就获得了色谱基线。基于小波和轮廓提取的算法为:先用小波初步提取基线,然后把色谱数据减去小波提取的基线后再用轮廓提取算法获得基线,把小波提取的基线和轮廓提取的基线相加即为原数据的基线。对这两个算法进行了比较实验研究,结果表明:基于小波和轮廓提取的算法比轮廓提取的算法效果好,能更准确地提取色谱基线。 相似文献
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一种基于二进小波变换的自适应滤波方法 总被引:3,自引:0,他引:3
根据信号和噪声经小波变换后在不同尺度上有不同的特征,将相邻尺度二进小波变换值的相关量进行归一化处理并与小波变换值比较来判断信号与噪声,以噪声在各尺度的方差作为终止迭代的标准,提出了一种基于二进小波变换小波域选择噪声的自适应滤波方法。研究了模拟信号的去噪过程、半峰宽和信噪比对去噪结果的影响,并对模拟含噪信号和含噪毛细管电泳信号去噪前后的结果进行了比较。实验结果表明:由于该方法具有良好的自适应性和显著的滤波效果,必将得到广泛的应用。 相似文献
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拉曼光谱成像数据存在基线漂移与宇宙射线干扰峰两类噪声信号,无法直接用于光谱分析研究,必须去除。现有单光谱去噪方法处理结果不稳定、可重复性差。针对这一问题,本研究提出了一种自适应拉曼光谱成像数据新型去噪法,采用优化的自适应迭代惩罚最小二乘法( Adaptive iteratively reweighted penalized least-squares,airPLS)和基于主成分分析( PCA)的干扰峰消除算法修正光谱基线漂移和宇宙射线干扰峰,具有输入参数少、光谱失真小、处理速度快、去噪结果稳定等优点。利用本方法去除了芒草( Miscanthus sinensis)细胞壁拉曼光谱成像数据(9010条光谱)中的噪声信号,并对去噪后数据进行PCA和聚类分析(CA),成功区分非植物光谱与植物光谱,分类结果优于未去噪数据。预期本方法可应用于其它光谱成像数据去噪,为光谱的解译和定量分析提供可靠的研究基础。 相似文献
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This work examines the factor analysis of matrices where the proportion of signal and noise is very different in different columns (variables). Such matrices often occur when measuring elemental concentrations in environmental samples. In the strongest variables, the error level may be a few percent. For the weakest variables, the data may consist almost entirely of noise. This paper demonstrates that the proper scaling of weak variables is critical. It is found that if a few weak variables are scaled to too high a weight in the analysis, the errors in computed factors would grow, possibly obscuring the weakest factor(s) by the increased noise level. The mathematical explanation of this phenomenon is explored by means of Givens rotations. It is shown that the customary form of principal component analysis (PCA), based on autoscaling the original data, is generally very ineffective because the scaling of weak variables becomes much too high. Practical advice is given for dealing with noisy data in both PCA and positive matrix factorization (PMF). 相似文献
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《Surface and interface analysis : SIA》2004,36(3):203-212
Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by ‘counting statistics’ and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time‐of‐flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis. Published in 2004 by John Wiley & Sons, Ltd. 相似文献
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Observed data often belong to some specific intervals of values (for instance in case of percentages or proportions) or are higher (lower) than pre‐specified values (for instance, chemical concentrations are higher than zero). The use of classical principal component analysis (PCA) may lead to extract components such that the reconstructed data take unfeasible values. In order to cope with this problem, a constrained generalization of PCA is proposed. The new technique, called bounded principal component analysis (B‐PCA), detects components such that the reconstructed data are constrained to belong to some pre‐specified bounds. This is done by implementing a row‐wise alternating least squares (ALS) algorithm, which exploits the potentialities of the least squares with inequality (LSI) algorithm. The results of a simulation study and two applications to bounded data are discussed for evaluating how the method and the algorithm for solving it work in practice. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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This paper describes the use of principal component analysis (PCA) to de-noise Raman spectra and considerably shorten data acquisition time in Raman mapping experiments. A solid dosage pharmaceutical material (bead) is mapped by a Raman line-mapping system. The mapping acquisition time was varied from 30 s (usually employed in practice) to only 3 s. Apparently excessive noise in the maps measured for 3 s is removed by PCA and the maps of all three components of the bead are then binarized and compared. It is found that spatial difference is negligible despite the remarkably different acquisition times employed. The spectra acquired for 3 s and reconstructed via PCA are found to largely overlap with the spectra acquired for 30 s. The signal to noise ratio of the Raman mapping spectra does not obey the expected root t dependence, thereby preventing straightforward estimation of the shortest usable acquisition time (which is to a lesser extent also a function of the binarization threshold). The results reveal that Raman microscopy can be considered a fast method for mapping some materials, in contrast to the established opinion that it is an inherently slow technique. 相似文献