基于独立成分分析的掌纹识别

本文研究了独立成分分析(ICA)两种不同的结构ICA I和ICAII在掌纹识别中的应用.为了提高识别准确性和可靠性,该方法首先对掌纹图像进行预处理,提取掌纹感兴趣(ROI)区域进行特征提取和匹配.为了减少计算量,运用ICA算法之前,先采用主成分分析(PCA)算法去除掌纹图像的二阶统计特征相关性,其余的高阶统计特征由ICA分离.对于PolyU掌纹图像库,基于ICA模型的预测误差平方和(SPE)小于PCA,而且重构的原始图像优于PCA.为了比较两种算法识别性能,本丈分别用PCA、ICA I、ICAII提取特征掌纹子空间,然后将待识别图像投影到低维子空间上,最后用余弦距离进行掌纹匹配.实验结果表明,ICA算法两种结构的识别率均高于PCA,ICAII在性能上优于ICA I.     

基于非线性 PCA的微气体传感器阵列信号处理

在线性叠加模型基础上提出了气体传感器对混合气体的非线性叠加模型,并引入了非线性主成分分析(Nonlinear Principal Component Analysis,NLPCA)法对微传感器阵列的信号进行处理。使用该模型对由4个微热板式气体传感器组成的阵列的信号进行了分析,对照基于线性叠加模型的主成分分析法(Principal Component Analysis,PCA)的识别结果,说明该方法能够提高对混合气体识别和量化的准确度。     

ICA的梯度下降算法框架(英文)

为了设计更多有效的独立分量分析(ICA)算法,本文提出了ICA梯度下降算法(GDA)的一般框架,覆盖了许多目前流行的算法,如Infomax,MMI,MLE等等.该框架由一种新的基于Ⅱ类超加(减)性函数的参比函数理论导出,并采用推广的EASI形式作为更新规则来获得更好的性能.同时本丈也展示了一个基于二次熵函数的框架使用例子,并提出了其梯度的快速计算方法,最后仿真证明了它的有效性.实验结果表明,该框架非常实用,可作为开发更多有效ICA算法的有利工具.     

Cutting force denoising in micro-milling tool condition monitoring

An independent component analysis (ICA) algorithm for cutting force denoising was applied in micro-milling tool condition monitoring. In micro-milling, the comparatively small cutting force signal is prone to contamination by relatively large noise, and as a result it is important to denoise the force signal before further processing it. However, the traditional denoising methods, based on Gaussian noise assumption, lose here because the noise is identified as containing a high non-Gaussian component in the experiment. ICA was recently developed to deal with the blind source separation (BSS) problem. It solves the BSS problem by measuring the non-Gaussianity of the signal and it is particularly effective in the separation of non-Gaussian signals. This approach employs fixed-point ICA (FastICA), assuming the noises are sources and the force signal is an instantaneous mixture of sources and by treating the signal denoising process as a BSS. The results are illustrated both in time and frequency domains. The FastICA denoising performances are compared with the popular wavelet thresholding. The results show that FastICA performs better than wavelet. Theoretical discussion of the nature of ICA and wavelet thresholding supports the results: ICA separates both Gaussian and non-Gaussian noise sources, while wavelet only suppresses Gaussian noise.     

Fault diagnosis method for an Aeroengine Based on Independent Component Analysis and the Discrete Hidden Markov Model

The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis (ICA) and the Discrete Hidden Markov Model (DHMM), a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals ...     

A unified statistical framework for monitoring multivariate systems with unknown source and error signals

This article proposes a unified multivariate statistical monitoring framework that incorporates recent work on maximum likelihood PCA (MLPCA) into conventional PCA-based monitoring. The proposed approach allows the simultaneous and consistent estimation of the PCA model plane, its dimension and the error covariance matrix. This paper also invokes recent work on monitoring non-Gaussian processes to extract unknown Gaussian as well as non-Gaussian source signals from recorded process data. By contrasting the unified framework with PCA-based process monitoring using a simulation example and recorded data from two industrial processes, the proposed approach produced more accurate and/or sensitive monitoring models.     

Independent Component Analysis–Based Feature Extraction Technique for Defect Classification Applied for Pulsed Eddy Current NDE

With the development of nondestructive detection, the emerging testing techniques provide new challenges to signal analysis and interpretation approach applied to the inspection evaluation. Some researchers have developed the methods that focus on feature analysis of detected signals. This article presents a new feature analysis by the Independent Component Analysis (ICA) approach to evaluate the defects tested by the pulsed eddy current (PEC) technique. ICA is a high-order statistics technique used to separate multi-unknown sources, which has been successfully applied to facial image identification and separation of the components of 1D signal. In this article, the ICA approach is utilized to project the response signals of various defects into the independent components (ICs) feature subspace by signal representation model. Dependent on the selected ICs, each defect is represented by different projected coefficients, which are proposed to discriminate and classify the defects that belong to three categories. The improved ICA model is proposed to improve the classification of two similar categories of single defects: metal loss and subsurface defects. The evaluation using the series of experimental data has validated the classification of single defects and the defects with lift-off effect by our ICA approach. The comparison with Principal component analysis (PCA)–based approach further verified the better performance of the ICA-based model.     

独立分量分析在说话人识别技术中的应用

独立分量分析方法能够将线性混合信号进行分离,得到统计独立的源信号,能用于提取组合语音的特征基函数。倒谱矢量符合ICA变换的假设条件,用ICA方法对MFCC特征进行转换得到ICA特征基,继而用于说话人识别,建立了一个基于独立分量分析的说话人识别系统。实验结果表明,在噪声环境下此系统具有更高的识别率。     

基于核主元分析的头部姿势估计

现有头部姿势估计方法主要是基于几何分析和基于外现线性变换的方法,计算复杂、通用性不强.提出一种新的利用非线性的核变换算法进行姿势估计的方法,根据流形学习理论,不同姿势的高维人脸图像存在一低维流形结构,提取该流形结构可估计头部姿势.核主元分析是一种非线性降维算法,能够把这种流形结构嵌入到低维空间.利用核主元分析训练姿势估计曲线,然后把新图像投影到姿势曲线上,利用插值方法估计新投影点对应得姿势角度.核主元分析的方法克服了传统线性估计方法的缺点,实验证明该方法估计效果良好,并给出进一步提高估计效果的途径.     

应用ICA滤波器技术提取图像纹理特征

针对纹理图像分类问题,本文提出了一种应用ICA滤波器技术提取图像纹理特征的方法.该方法首先从训练图像集中随机抽取图像块作为观测信号,应用ICA技术,提取滤波器组.然后根据训练样本图像对滤波器组的响应值来评估和选择滤波器组,达到降维的目的.最后利用滤波器组对测试图像进行滤波,得到该图像的滤波响应结果,从该响应结果中得到最大响应滤波器编号,提取其直方图作为图像的全局特征和局部特征.对Brodatz纹理图像集中108个纹理类别进行了分类实验,结果表明,与MPEG-7纹理描述子相比,该图像特征对纹理图像具有更好的分类效果.     

独立分量分析的图像融合算法

独立分量分析可实现图像的稀疏编码并具有能很好地捕捉图像重要边缘信息的特性.本文提出一种基于独立分量分析的图像融合算法,结合支持向量机对多聚焦图像的清晰域、模糊域进行判断以及在ICA域中进行图像分割以提取图像的主要边缘特征信息来实现特征级的多聚焦图像的融合.实验结果表明,本文提出的融合算法是有效的.     

基于独立分量分析的图像特征提取及泊松噪声去除

针对泊松噪声具有与信号相关和不一致性分布的特性,提出了一种基于独立成分分析(ICA)的软阈值滤波算法。泊松噪声对基于高阶统计量的ICA变换不敏感,其能量均匀分布在ICA域。首先将包含泊松噪声的图像变换到ICA域,然后再对图像独立成分进行软阈值滤波。该算法对图像和噪声具有自适应能力,在噪声去除和图像细节保留方面达到一个平衡。实验结果表明:该算法不仅可以有效去除图像中的泊松噪声和提高图像质量,而且具有良好的鲁棒性。     

A Source Separation Technique for Processing of Thermometric Data From Fiber-Optic DTS Measurements for Water Leakage Identification in Dikes

Distributed temperature sensors (DTSs) show real advantages over conventional temperature sensing technology such as low cost for long-range measurement, durability, stability, insensitivity to external perturbations, etc. They are particularly interesting for long-term health assessment of civil engineering structures such as dikes. In this paper, we address the problem of identification of leakage in dikes based on real thermometric data recorded by DTS. Formulating this task as a source separation problem, we propose a methodology based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). As the first PCA estimated source extracts an energetic subspace, other PCA sources allow to access the leakages. The energy of a leakage being very low compared to the entire data, a temporal windowing approach guarantees the presence of the leakages on these other PCA sources. However, on these sources, the leakages are not well separated from other factors like drains. An ICA processing, providing independent sources, is thus proposed to achieve better identification of the leakages. The study of different preprocessing steps such as normalization, spatial gradient, and transposition allows to propose a final scheme that represents a first step towards the automation of the leakage identification problem.     

A process monitoring scheme based on independent component analysis and adjusted outliers

Recently, the independent component analysis (ICA) has been widely used for non-Gaussian multivariate process monitoring. An elliptical type measure is traditionally used for ICA-based process monitoring. However, it will not work appropriately since the extracted ICA components exhibit skewed distribution. Thus, this study aims to develop a novel process monitoring scheme for ICA. The basic idea of the proposed method is to first screen out outliers in order to describe well majority for training dataset. Hereafter, a rectangular type measure is applied to monitor the process. The efficiency of proposed monitoring scheme will be implemented via a five variables simulation example and a case study of Tennessee Eastman process. Results indicate that the proposed method cannot only deal with the contaminated training dataset but also shows superior fault detection ability when compared with alternative methods.     

直接无监督正交局部保持特征提取算法

基于局部保持投影发展出的一系列特征提取算法,在应用于人脸识别等高维小样本问题时,均需先采用PCA算法对高维样本降维后才能应用,故此以无监督鉴别分析算法为理论基础,提出了一种直接无监督正交局部保持算法。该算法利用拉普拉斯矩阵的性质进行相应的矩阵分解,可直接从高维样本的原始空间中提取投影矩阵,因而无需先采用PCA降维处理,且解决了无监督鉴别分析算法的小样本问题。为了进一步提高算法的识别性能,给出了基于QR分解的正交投影矩阵的求解方法。人脸库和掌纹库上的实验结果表明了所提算法的有效性。     

Advance warning for loss of separation in an industrial distillation column

This paper describes the development of a data‐driven advance warning system for the onset of loss of separation in an industrial distillation column. The system would enable preventive actions to avoid several hours of bad operation and subsequent recovery of the process. Data of more than 2 years of process operation were used to identify and validate various monitoring systems based on both static principal component analysis (PCA) and dynamic PCA. Despite the presence of autocorrelation in the data, only minor differences in advance warning were observed between PCA and dynamic PCA. The developed system provides warnings for 35% to 45% of the observed periods of bad column operation, with respective advance warning times of 16 and 6 minutes. It proves a valuable additional tool to monitor the operation of the distillation column and avoid losses of product, with the potential of reducing bad operation (and the associated costs) by up to 45% and substantially improving overall process reliability.     

基于主成分分析的多光谱相机灵敏度优化

研究了基于主成分分析的多通道光谱图像获取硬件系统即多光谱相机的灵敏度优化问题。利用多维向量空间理论和主成分分析法,系统讨论了多光谱获取系统优化灵敏度的理论和方法。提出灵敏度优化向量的概念,将滤光片透过率优化和光源辐射谱优化两种方法统一起来。利用四种灵敏度优化向量进行了仿真试验,并给出了在主成分分析算法下的实验仿真结果。结论是：多光谱系统灵敏度优化向量的正交化设计是系统光谱图像获取的必要要求;窄带灵敏度中,交叠的灵敏度优化向量具有更好的光谱反射率信息获取能力;在有限数目的宽带滤色片中,挑选滤色片透过率向量可以得到较好的多光谱相机的灵敏度向量。     

Independent component analysis applied on gas sensor array measurement data

The performance of gas-sensor array systems is greatly influenced by the pattern recognition scheme applied on the instrument's measurement data. The traditional method of choice is principal component analysis (PCA), aiming for reduction in dimensionality and visualization of multivariate measurement data. PCA, as a second-order statistical tool, performs well in many cases, but lacks the ability to give meaningful representations for non-Gaussian data, which often is a property of gas-sensor array measurement data. If, instead, higher order statistical methods are considered for data analysis, more useful information can be extracted from the data. This paper introduces the higher order statistical method called independent component analysis (ICA) as a novel tool for analysis of gas-sensor array measurement data. A comparison between the performances of PCA and ICA is illustrated both in theory and for two sets of practical measurement data. The described experiments show that ICA is capable of handling sensor drift combined with improved discrimination, dimensionality reduction, and more adequate data representation when compared to PCA.