基于Frank导联和Hermite展开的心肌梗死检测

多数现有的计算机辅助心电(ECG)诊断技术研究通常是基于常规心电导联展开的,而正交Frank心电导联比常规心电导联有着与解剖学更为密切的联系.基于Frank导联的心肌梗死(MI)心电特征提取和分类检测的研究,对MI ECG信号进行Hermite非线性展开,以Hermite系数为心电特征,并对其进行分类测试.与常规心电导联相比,此方法对早期MI和急性期MI进行分类,检测精度可分别提高30.06%和19.33%.     

一种改进的主元分析直线检测算法

为了解决基于主元分析的直线检测无法检测出直线间的交点和角点的问题,提出了一种新的主元分析直线检测算法,该算法增加了Freeman链码来检测直线间的交点和角点,在交点和角点处断开,再利用主元分析法检测图像中的直线特征。仿真结果表明,该算法与主元分析法相比具有更好的检测精度和效果。     

基于主元分析和RBF神经网络的火灾模拟实验炉温软测量

软测量也称为软仪表技术,采用主元分析和RBF神经网络相结合的融合模型构成火灾模拟实验炉温软测量。主元分析（PCA）实现输入变量的降维,RBF神经网络采用K－均值聚类算法进行隐层中心和连接权调节的学习,实现快速收敛。该融合模型使炉温估计精度比常规的最小二乘方法拟合精度提高２倍以上。     

基于数据驱动的显著误差检测

本文介绍了基于数据驱动的显著误差检测原理和检测步骤,重点介绍了主元分析法（PCA）和核主元分析法（KPCA）在显著误差检测中的应用。     

基于小波包变换与核主成分分析的铣削颤振识别

提出一种基于小波包变换（wavelet packets transform, WPT）与核主成分分析（kernel principal component analysis,KPCA）的颤振识别方法。铣削颤振会抑制或增强某些频段内的信号,利用四层小波包分解与重构,得到16个频段内的重构信号,获得各重构信号的面积,并进行归一化处理,完成铣削颤振特征向量的选择。继而通过对比基于主成分分析（principal component analysis,PCA）与核主成分分析的特征提取方法的特征提取效果,选取KPCA对特征向量进行降维处理,最后以降维后的数据作为最小二乘支持向量机分类器的输入对铣削状态进行识别。结果表明,在小样本的情况下仍能有效、准确地对铣削状态进行分类,分类准确率达95.0 %。     

二维MUSIC近场被动定位方法

介绍了一种高精度的近场被动定位方法——二维MUSIC被动定位方法。它是一种在距离和方位上进行二维联合搜索的高精度被动定位方法。将MUSIC（多重信号分类）算法与近场聚焦波束形成方法相结合,能大大提高对近场目标的定位精度。先推导了基于均匀线列阵的二维MUSIC近场被动定位方法的定位原理,通过仿真比较了二维MUSIC被动定位方法与常规聚焦波束形成的定位性能,仿真表明,二维MUSIC被动定位方法的定位性能要明显高于常规的聚焦波束形成被动定位方法。并仿真了该方法在不同的阵元间距以及不同的目标距离时的定位性能,验证了该方法的可行性和有效性。     

面向高保真再现的多光谱图像降维方法

目的研究满足面向高保真再现要求的多光谱图像降维方法。方法基于二进制小波对信号的分解与人类的视觉特性相匹配,以及非负主成分分析法可较好地保证降维的光谱精度,提出采用基于离散二进制小波变化与非负主成分分析法的综合降维方法,并基于多光谱图像高保真再现的光谱精度、色度精度与变光源色差稳定性的要求,提出采用CIELAB的标准色差ab?E、光谱保真度和平均梯度等3个指标来评价降维效果。结果经过多光谱图像的测试实验,基于离散小波变换和非负主成分分析法的综合降维方法相对于其他3种方法,其光谱精度、色度精度和图像清晰度保持良好。结论该方法较好地实现了多光谱图像的高保真再现问题,并且为颜色视觉的认知过程提供了新的理论解释。     

基于主成分分析和BP神经网络的钢丝绳断丝定量检测方法

针对目前钢丝绳断丝定量检测中存在的问题,充分利用主成分分析与BP神经网络的优点,提出了基于主成分分析与BP神经网络相结合的钢丝绳断丝定量检测方法。采用主成分分析法对钢丝绳断丝信号的原始特征属性进行预处理,得到钢丝绳断丝信号主成分特征属性,并以此作为BP神经网络的输入,建立钢丝绳断丝信号主成分特征属性与断丝数目之间的关系,并对钢丝绳断丝数目进行预测;主成分分析方法减少了原始特征属性的维数,消除了属性之间的相关性;同时,主成分特征属性作为BP神经网络的输入,也简化了网络的结构。实例测试结果表明,基于主成分分析的神经网络钢丝绳断丝检测方法与常规BP神经网络方法相比,具有更高的检测精度和更少的计算量。     

不变矩法分类识别带钢表面的缺陷

针对带钢表面缺陷的识别和分类技术,本文采用一种将不变矩与主成分分析法相结合的特征提取方法.首先,对每幅缺陷图像提取22 维不变矩特征向量,满足对图像平移、尺度及旋转变化都不敏感;然后,为了提高分类器的效率,应用主成分分析法对特征向量进行空间降维处理,得到4 维特征向量;最后,将特征向量作为BP神经网络的输入,对网络进行权值和阈值训练,达到缺陷分类的目的.实验结果表明,该方法对带钢表面缺陷的平均正确识别率可达到85%以上.     

饮用水中（NO3-）的检测方法比较及分析

硝酸盐的含量是生活饮用水标准中的一个重要检测指标,主要是以水硝酸盐的含氮量来计算。水中（N03）检测是水质卫生检验的重要指标之一。NO。的检测方法有很多,如极普法、光度分析法等,但以前所用的常规化学分析法多繁琐费时,本文将传统方法与最新仪器分析法做以简要比较。     

基于集合经验模态分解的脑电信号高阶张量特征提取

为了实现脑机接口系统需要有效的特征提取算法。针对二维主成分分析(2DPCA)的特征提取方法忽略脑电信号(EEG)频域特征的缺点和基于小波分解构建EEG高阶张量时小波参数难以确定的局限性,提出了基于集合经验模态分解(EEMD)构建高阶张量结合多线性主成分分析(MPCA)降维的特征提取方法。设计了3种不同特征提取方法的对照实验,并结合Fisher线性判别分析分类方法取得分类准确率。结果表明:新提出的方法相比基于小波分解构建高阶张量结合MPCA进行降维和2DPCA的特征提取方法,平均识别准确率分别提高4.75%和2.6%,且识别准确率的方差分别减小72.69%和23.86%。该方法在提高单次运动想象脑电信号识别准确率的同时还具有更好的适用性,为实现运动想象脑电信号解码奠定了基础。     

Feed‐forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection

Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. We proposed a novel automatic classification system based on particle swarm optimization (PSO) and artificial bee colony (ABC), with the aim of distinguishing abnormal brains from normal brains in MRI scanning. The proposed method used stationary wavelet transform (SWT) to extract features from MR brain images. SWT is translation‐invariant and performed well even the image suffered from slight translation. Next, principal component analysis (PCA) was harnessed to reduce the SWT coefficients. Based on three different hybridization methods of PSO and ABC, we proposed three new variants of feed‐forward neural network (FNN), consisting of IABAP‐FNN, ABC‐SPSO‐FNN, and HPA‐FNN. The 10 runs of K‐fold cross validation result showed the proposed HPA‐FNN was superior to not only other two proposed classifiers but also existing state‐of‐the‐art methods in terms of classification accuracy. In addition, the method achieved perfect classification on Dataset‐66 and Dataset‐160. For Dataset‐255, the 10 repetition achieved average sensitivity of 99.37%, average specificity of 100.00%, average precision of 100.00%, and average accuracy of 99.45%. The offline learning cost 219.077 s for Dataset‐255, and merely 0.016 s for online prediction. Thus, the proposed SWT + PCA + HPA‐FNN method excelled existing methods. It can be applied to practical use.     

ECG analysis: a new approach in human identification

A new approach in human identification is investigated. For this purpose, a standard 12-lead electrocardiogram (ECG) recorded during rest is used. Selected features extracted from the ECG are used to identify a person in a predetermined group. Multivariate analysis is used for the identification task. Experiments show that it is possible to identify a person by features extracted from one lead only. Hence, only three electrodes have to be attached on the person to be identified. This makes the method applicable without too much effort     

Performance analysis of classifiers for colon cancer detection from dimensionality reduced microarray gene data

Cancer disease is accountable for many deaths that are over 9.6 million in 2018 and roughly one out of six deaths occur because of cancer worldwide. The colon cancer is the second prominent source of death of around 1.8 million cases. This research is inclined to detect the colon cancer from microarray dataset. It will aids the experts to distinguish the cancer cells from normal cells for appropriate determination and treatment of cancer at earlier stages that leads to increase the survival rate of the patients. The high dimensionality in microarray dataset with less samples and more attributes creates lag in the detection capability of the classifier. Hence there is a need for dimensionality reduction techniques to preserve the significant genes that are prominent in the disease classification. In this article, at first ANOVA method used to select the best genes and then principal component analysis (PCA) and fuzzy C-means clustering (FCM) techniques are further employed to choose relevant genes. The PCA and FCM features are classified using model, discriminant, regression, hybrid, and heuristic-based classifiers. The attained results show that the heuristic classifier with PCA features is encapsulated an average classification accuracy of 97.92% for classifying both the colon cancer and normal samples. Also, for FCM features, the Heuristic classifier is maintained at an average classification accuracy of 99.48% and 97.92% for classifying the colon cancer and normal samples, respectively. The Heuristic classifier outperforms with high accuracy than all other classifiers in the classification of colon cancer.     

Chinese Spirits Identification Model Based on Mid-Infrared Spectrum

Applying computer technology to the field of food safety, and how to identify liquor quickly and accurately, is of vital importance and has become a research focus. In this paper, sparse principal component analysis (SPCA) was applied to seek sparse factors of the mid-infrared (MIR) spectra of five famous vintage year Chinese spirits. The results showed while meeting the maximum explained variance, 23 sparse principal components (PCs) were selected as features in a support vector machine (SVM) model, which obtained a 97% classification accuracy. By comparison principal component analysis (PCA) selected 10 PCs as features but only achieved an 83% classification accuracy. Although both approaches were better than a direct SVM approach based on the classification results (64% classification accuracy), they also demonstrated the importance of extracting sparse PCs, which captured most important information. The combination of computer technology SPCA and MIR provides a new and convenient method for liquor identification in food safety.     

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).     

基于样本选择的光谱重构研究

目的为了提高使用主成分分析法重构光谱反射率的重构精度。方法利用Matlab进行仿真实验,选择3种不同色卡作为训练样本,使用主成分分析法探究主成分个数和样本间隔对重构结果的影响。结果主成分个数为4时,贡献率均超过99%;样本间隔为10 nm时,RC24色卡重构效果最好,其平均色差2.37ΔE_(ab)~*平均均方根误差为0.0185。结论训练样本的选择会影响光谱重构精度,RC24色卡具有数据量小、重建精度较高的特点,在颜色复制领域可以优先选择。     

Fluorescence spectral imaging for characterization of tissue based on multivariate statistical analysis

A novel spectral imaging method for the classification of light-induced autofluorescence spectra based on principal component analysis (PCA), a multivariate statistical analysis technique commonly used for studying the statistical characteristics of spectral data, is proposed and investigated. A set of optical spectral filters related to the diagnostically relevant principal components is proposed to process autofluorescence signals optically and generate principal component score images of the examined tissue simultaneously. A diagnostic image is then formed on the basis of an algorithm that relates the principal component scores to tissue pathology. With autofluorescence spectral data collected from nasopharyngeal tissue in vivo, a set of principal component filters was designed to process the autofluorescence signal, and the PCA-based diagnostic algorithms were developed to classify the spectral signal. Simulation results demonstrate that the proposed spectral imaging system can differentiate carcinoma lesions from normal tissue with a sensitivity of 95% and specificity of 93%. The optimal design of principal filters and the optimal selection of PCA-based algorithms were investigated to improve the diagnostic accuracy. The robustness of the spectral imaging method against noise in the autofluorescence signal was studied as well.