基于角微特征的神经网络识别人脸特征

提出一种利用人脸角微特征几何特性的图像预处理,建立BP神经网络识别人脸特征模型的方法。研究了角微特征提取和具体算法,讨论了BP网络结构的设计,输入、输出层设计和隐藏层节点选取问题。微特征提取,可以降低网络输入维度,对于识别不同角度、不同表情的人脸图像提供了可能性。利用ORL人脸图像数据库做实验,结果表明此方法有效。     

Electroencephalography‐based feature extraction using complex network for automated epileptic seizure detection

Electroencephalography signals are typically used for analyzing epileptic seizures. These signals are highly nonlinear and nonstationary, and some specific patterns exist for certain disease types that are hard to develop an automatic epileptic seizure detection system. This paper discussed statistical mechanics of complex networks, which inherit the characteristic properties of electroencephalography signals, for feature extraction via a horizontal visibility algorithm in order to reduce processing time and complexity. The algorithm transforms a time series signal into a complex network, which some features are abbreviated. The statistical mechanics are calculated to capture distinctions pertaining to certain diseases to form a feature vector. The feature vector is classified by multiclass classification via a k‐nearest neighbor classifier, a multilayer perceptron neural network, and a support vector machine with a 10‐fold cross‐validation criterion. In performance evaluation of proposed method with healthy, seizure‐free interval, and seizure signals, firstly, input data length is regarded among some practical signal samples by optimizing between accuracy‐processing time, and the proposed method yields outstanding performance on the average classification accuracy for 3‐class problems mainly for detection of seizure‐free interval and seizure signals and acceptable results for 2‐class and 5‐class problems comparing with conventional methods. The proposed method is another tool that can be used for classifying signal patterns, as an alternative to time/frequency analyses.     

基于小波包变换的脑电波信号降噪及特征提取

针对原始脑电波信号存在非平稳性且非常容易受到各种信号干扰等特点,对基于小波变换和小波包变换的脑电波信号的滤波降噪方法,和基于小波包变换的脑电波信号特征提取方法进行了研究。首先利用MindSet采集到原始脑电波数据,然后分别应用小波变换和小波包变换对其进行降噪处理,比较了两种方法的性能,验证了基于小波包变换的降噪方法的优越性和特征提取方法的有效性。     

基于希尔伯特变换联合卷积神经网络的脑电信号识别方法

传统运动想象脑电信号识别方法需要人为提取大量特征,识别性能受研究人员经验影响较大,主观性强;提出一种基于希尔伯特变换(HT)联合卷积神经网络(CNN)的运动想象脑电信号自动识别方法,首先利用HT对原始EEG信号进行分析,实现一维数据向二维幅-相图像转换的同时增加信息提取维度;然后将其作为输入利用CNN层次化的对幅-相二维图像进行理解和解译,自动提取特征并完成分类识别,基于BCI竞赛中所用Graz数据集开展试验,结果表明相对于传统特征提取方法,文章所提算法在低、中、高信噪比条件下均能获得更好的识别性能,具有更强的噪声鲁棒性.     

多视角神经网络非接触式脉搏信号提取

目的 远程光体积描记（remote photoplethysmography,rPPG）是一种基于视频的非接触式心率测量技术,受到学者的广泛关注。从视频数据中提取脉搏信号需要同时考虑时间和空间信息,然而现有方法往往将空间处理与时间处理割裂开,从而造成建模不准确、测量精度不高等问题。本文提出一种基于多视角2维卷积的神经网络模型,对帧内和帧间相关性进行建模,从而提高测量精度。方法 所提网络包括普通2维卷积块和多视角卷积块。普通2维卷积块将输入数据在空间维度做初步抽象。多视角卷积块包括3个通道,分别从输入数据的高—宽、高—时间、宽—时间3个视角进行2维卷积操作,再将3个视角的互补时空特征进行融合得到最终的脉搏信号。所提多视角2维卷积是对传统单视角2维卷积网络在时间维度的扩展。该方法不破坏视频原有结构,通过3个视角的卷积操作挖掘时空互补特征,从而提高脉搏测量精度。结果 在公共数据集PURE（pulse rate detection dataset）和自建数据集Self-rPPG（self-built rPPG dataset）上的实验结果表明,所提网络提取脉搏信号的信噪比相比于传统方法在两个数据集上分别提高了3.92 dB和1.92 dB,平均绝对误差分别降低了3.81 bpm和2.91 bpm;信噪比相比于单视角网络分别提高了2.93 dB和3.20 dB,平均绝对误差分别降低了2.20 bpm和3.61 bpm。结论 所提网络能够在复杂环境中以较高精度估计出受试者的脉搏信号,表明了多视角2维卷积在rPPG脉搏提取的有效性。与基于单视角2维神经网络的rPPG算法相比,本文方法提取的脉搏信号噪声、低频分量更少,泛化能力更强。     

基于反卷积特征提取的深度卷积神经网络学习

在深度卷积神经网络的学习过程中,卷积核的初始值通常是随机赋值的.另外,基于梯度下降法的网络参数学习法通常会导致梯度弥散现象.鉴于此,提出一种基于反卷积特征提取的深度卷积神经网络学习方法.首先,采用无监督两层堆叠反卷积神经网络从原始图像中学习得到特征映射矩阵;然后,将该特征映射矩阵作为深度卷积神经网络的卷积核,对原始图像进行逐层卷积和池化操作;最后,采用附加动量系数的小批次随机梯度下降法对深度卷积网络微调以避免梯度弥散问题.在MNIST、CIFAR-10和CIFAR-100数据集上的实验结果表明,所提出方法可有效提高图像分类精度.     

基于动态特征提取和神经网络的数据流分类研究

为提高数据流分类的精确性和适应性,提出了一种新的数据流分类方法。该方法基于总体最小二乘法对数据流进行分段拟合,并将传统曲线分析算法——滑动窗口(SW)和在线数据分割(OSD)进行结合、改进,以可变滑动窗口算法实现对数据流的合理分割,提高趋势分析精度。在此基础上,对数据流进行动态特征提取和判断,并以神经网络对数据流特征进行模式识别,精确分类,进而对监控对象提供早期预警、状态评估和决策支持。实验结果表明,该方法能对数据流进行有效的动态特征描述,分类效果明显。     

Classification trees with neural network feature extraction

The ideal use of small multilayer nets at the decision nodes of a binary classification tree to extract nonlinear features is proposed. The nets are trained and the tree is grown using a gradient-type learning algorithm in the multiclass case. The method improves on standard classification tree design methods in that it generally produces trees with lower error rates and fewer nodes. It also reduces the problems associated with training large unstructured nets and transfers the problem of selecting the size of the net to the simpler problem of finding a tree of the right size. An efficient tree pruning algorithm is proposed for this purpose. Trees constructed with the method and the CART method are compared on a waveform recognition problem and a handwritten character recognition problem. The approach demonstrates significant decrease in error rate and tree size. It also yields comparable error rates and shorter training times than a large multilayer net trained with backpropagation on the same problems.     

基于BP神经网络和特征选择的入侵检测模型

提出了一种基于后向传播神经网络和特征选择的入侵检测模型。通过使用该模型对经过特征提取后的攻击数据的训练学习,可以有效地识别各种入侵。在经典的KDD 1999数据集上的测试说明：该模型与传统的入侵检测模型相比,能够轻便、高效地对攻击模式进行训练学习,从而正确有效地检测网络攻击。     

基于特征优化和BP神经网络的入侵检测方法

为提高网络入侵检测率,提出一个集特征优化和人工神经网络于一体的网络入侵识别发现框架AS-BP.引入SMOTE技术和随机采样技术对数据进行平衡约简处理,解决数据不平衡问题,利用集成方法对网络入侵数据进行重要特征提取,降低数据处理维度,通过优化BP神经网络算法,对网络入侵数据进行判断完成分类.实验结果表明,该方法克服了传统BP神经网络建模时间过长的问题,在不降低其它攻击类型检测率的同时,提高U2R和R2L的检测率,克服了数据集中少数类数据量过少导致的少数类检测率低的问题.将实验结果与其它分类方法进行比较,验证了该方法的准确率、精确率和召回率优于其它方法.     

基于样本熵和模式识别的脑电信号识别算法研究

脑-机接口BCI是一种实现人脑和外部设备通信的新兴技术。基于时频特性进行特征提取的传统方法无法体现EEG信号的非线性特征。为了进一步提高分类的准确率,首先采用小波阈值降噪的预处理方法提高了EEG信号的信噪比。然后结合非线性动力学的样本熵参数,对3种想象运动的脑电信号进行特征提取,保留了脑电信号的非线性特征。其中,运动想象MI脑电信号的研究一直都是BCI这一高速发展领域的重点目标。还研究了支持向量机、LVQ神经网络和BP神经网络3种分类器。通过实验结果对比发现,BP神经网络具有较高的识别率,更适用于脑电信号的分类识别。     

一种小波特征与深度神经网络结合的信号制式识别算法

针对当前通信信号的制式识别算法在低信噪比情况下识别不准确的问题,提出一种新的小波特征与改进的深度神经网络结合(WL-DNN)的识别算法。该算法将生成的10种{2ASK、4ASK、2PSK、4PSK、2FSK、4FSK、OFDM、16QAM、AM、FM}含有高斯白噪声的通信信号,用小波分解重构算法提取出一类新的小波特征参数。本文测试了含有多层隐含层的改进BP神经网络作为分类器,利用弹性反向传播算法训练神经网络的参数,确定神经网络的最优超参数。仿真结果表明:在信噪比低至0 dB的情况下,单个调制信号最低识别率超过95%,平均识别率超过98%,大幅提高了制式识别在低信噪比下的识别率,由此表明了该算法的有效性和正确性。     

红外火焰探测信号的特征提取研究

在设计红外火焰探测器时,特征值的选取是识别火焰的关键因素.分别对火焰和高温热源信号的过零点与其对应时间关系进行研究,提出将信号过零点与其对应时间拟合直线的线性度作为区别火焰与高温干扰的一个特征值.通过对相同环境下,不同大小的酒精灯火焰的过零点时间进行分析,发现利用拟合直线的斜率,可计算出火焰的频率;提出拟合直线的斜率可作为识别火焰的另一个特征值.实验结果表明:物质燃烧时,过零点与其对应时间的线性关系在一定范围内保持不变,用拟合直线的线性度和斜率作为识别火焰的特征值具有一定的可行性.     

Hybrid feature vector based detection of Glaucoma

This paper focus on the investigation of the potential in retinal image analysis for the detection of Glaucoma. The computer-based analysis of the parameter involves the use of image processing algorithms for pre-processing, localization and segmentation of the region of interest (ROI), feature extraction from ROI, and classification. The initial step in computer based detection system includes the enhancing scheme for improving the contrast of the fundus image from the three databases, Drishti-GS1, FAU and RIMONE. The optic disc region has been localized from the enhanced image. Structural deformation of the optic disc region, one of the primary indicators of the glaucoma demands more accuracy in segmentation process. As a solution to this problem, non-morphological features are extracted from the enhanced optic disc region. The non-morphological features from spatial domain include Local Binary Pattern, Histogram of Oriented Gradient and Fractal features. The significant feature extracted from the spatial domain are selected using Sequential Floating Forward Selection method and are then fed into the Support Vector Machine, Naive Bayes and Logistic Regression classifiers. Performance of the classifier is analyzed by computing the accuracy, sensitivity, specificity and positive prediction value. The performance of the classifier is also validated using the receiver operating characteristics plot. The hybrid feature from the spatial domain contributes to increase the efficiency of classification.

     

cFEM: a cluster based feature extraction method for network intrusion detection

International Journal of Information Security - The recent trend in network intrusion detection leverages key features of machine learning (ML) algorithms to detect network traffic anomalies....     

BP神经网络在脑电图信号预测中的应用

由于EEG和BEAM的无创、低成本、能反映大脑病变部位的机能变化等特点,为了预测大脑疾病发生的可能性。设计了一个3层BP网络,并建立了相应的数学模型,以所记录数据中的一部分作为样本,利用Matlab的神经网络工具箱中的算法和函数,对上述神经网络进行训练、仿真、并预测另一部分数据,同时作出待预测数据和预测数据的脑电地形图,进行比较,验证了BP网络模型的有效性。其算法可开发出程序模块,集成到相关脑电图仪的软件系统中。     

Unsupervised neural network learning procedures for feature extraction and classification

In this article, we review unsupervised neural network learning procedures which can be applied to the task of preprocessing raw data to extract useful features for subsequent classification. The learning algorithms reviewed here are grouped into three sections: information-preserving methods, density estimation methods, and feature extraction methods. Each of these major sections concludes with a discussion of successful applications of the methods to real-world problems.The first author is supported by research grants from the James S. McDonnell Foundation (grant #93–95) and the Natural Sciences and Engineering Research Council of Canada. For part of this work, the second author was supported by a Temporary Lectureship from the Academic Initiative of the University of London, and by a grant (GR/J38987) from the Science and Engineering Research Council (SERC) of the UK.     

A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification.

In this paper, a hybrid neural network/genetic algorithm technique is presented, aiming at designing a feature extractor that leads to highly separable classes in the feature space. The application upon which the system is built, is the identification of the state of human peripheral vascular tissue (i.e., normal, fibrous and calcified). The system is further tested on the classification of spectra measured from the cell nucleii in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. As advantages of the proposed technique we may encounter the algorithmic nature of the design procedure, the optimized classification results and the fact that the system performance is less dependent on the classifier type to be used.     

基于KPLS的网络入侵特征抽取及检测方法

从特征抽取的角度研究提高入侵检测性能问题,提出应用核偏最小二乘(KPLS)进行入侵特征抽取和检测的方法.其优点在于KPLS能非线性地抽取输入特征的多个正交分量,并保持与输出类别的相关性,可同时完成入侵特征抽取和判别.将该方法应用于基于Linux主机的入侵检测实验,取得了比SVM和KPCR等方法更好的效果.     

Epileptic seizure detection using convolutional neural networks and recurrence plots of EEG signals

Multimedia Tools and Applications - Epilepsy is a neurological disorder causing abnormal activities in the brain such as seizures, unusual behavior, sensations and loss of awareness. This disorder...