基于内积延拓LMD及SVM的轴承故障诊断方法研究

针对特征提取中局域均值分解(Local Mean Decomposition, LMD)存在端点效应缺陷及模式识别中人工神经网络(Artificial Neural Network, ANN)存在收敛速度慢、过学习等不足,提出基于内积延拓LMD及支持向量机(Support Vector Machine, SVM)的轴承故障诊断方法。利用内积延拓LMD方法对信号延拓分解抑制LMD端点效应;利用分解的可描述信号特性主分量PF(Product Function)构建初始特征向量矩阵;用SVD(Singular Value Decomposition)方法对初始特征向量矩阵进行奇异值分解,获得信号特征参数作为SVM的输入进行训练;对训练的SVM进行测试及模式分类。通过实际轴承故障信号分析及故障类型分类表明,该方法不仅能抑制LMD端点效应缺陷,且在故障模式识别中能有效避免ANN网络结构难确定、收敛速度慢等不足,能较好实现轴承故障准确分类,可用于轴承故障诊断。     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.     

基于EMD-SVD模型和SVM滚动轴承故障模式识别

针对滚动轴承振动信号的非平稳特性和在现实条件下难以获取大量故障样本的实际情况,提出一种经验模态分解、奇异值分解、Renyi熵和支持向量机相结合的故障诊断方法。运用经验模态分解方法对其去噪信号进行分析,利用互相关系数准则对固有模式分量进行筛选,再对所选分量重构相空间得到吸引子轨道矩阵;对矩阵进行奇异值分解求取奇异值,再计算这些奇异值的Renyi熵以组成故障特征向量,并将其作为支持向量机的输入以识别滚动轴承的故障类型。最后,利用实际滚动轴承试验数据的诊断与对比试验验证了该方法的有效性和泛化能力。     

基于边缘识别的虹膜定位算法

为了提高虹膜定位算法的抗干扰能力,本文提出了一种基于虹膜边缘识别的虹膜定位算法.该算法应用图像几何矩函数提取虹膜内外边缘的特征,通过由支持向量机(SVM)训练的分类器进行虹膜边缘点的识别.最后由Hough变换对识别的结果进行参数求解并实现虹膜的定位.本算法经过了CASIA虹膜图形库的实验验证,仿真实验数据表明所提算法不仅具有较快的定位速度和较高的定位成功率,而且性能稳定.     

基于多超平面支持向量机的图像语义分类算法

由于图像的低层可视特征与高层语义内容之间存在巨大的语义鸿沟,而基于内容的图像分类和检索准确性极大依赖低层可视特征的描述,本文提出了一种基于多超平面支持向量机的图像语义分类方法.多超平面分类器从优化问题的复杂度和运行泛化能力两方面进行研究,是最优分离超平面分类器一种显而易见的扩展.实验结果表明,本文提出的方法在图像语义分类的准确性方面要优于诸如采用色彩特征和纹理特征的支持向量机分类器的其它方法.     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

Street-Level Landmarks Acquisition Based on SVM Classifiers

High-density street-level reliable landmarks are one of the important foundations for street-level geolocation. However, the existing methods cannot obtain enough street-level landmarks in a short period of time. In this paper, a street-level landmarks acquisition method based on SVM (Support Vector Machine) classifiers is proposed. Firstly, the port detection results of IPs with known services are vectorized, and the vectorization results are used as an input of the SVM training. Then, the kernel function and penalty factor are adjusted for SVM classifiers training, and the optimal SVM classifiers are obtained. After that, the classifier sequence is constructed, and the IPs with unknown service are classified using the sequence. Finally, according to the domain name corresponding to the IP, the relationship between the classified server IP and organization name is established. The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially, and the median geolocation error using evaluated landmarks is reduced by about 2 km.     

基于多核Boosting方法的通用目标分类和识别

针对传统基于SVM分类器的多核学习方法优化参数多、优化过程复杂、计算量大的缺点,本文提出基于Real Adaboost的多核学习方法解决通用目标分类与识别问题.该方法根据核函数能将高维特征映射到低维空间的特性,采用核函数空间上的线性平面分割构建弱分类器,并用Real Adaboost学习框架对弱分类器进行学习.先用分层...     

An efficient lymphocytic leukemia detection based on EfficientNets and ensemble voting classifier

Early detection of leukemia increases the chances of a speedier recovery. If a patient exhibits any symptoms, doctors would often examine a blood sample slide under a microscope to detect hematological malignancies. Manually categorizing leukocytes as normal or abnormal requires examining the many characteristics of the cells, which is time-consuming and error-prone. This research aims to create a transfer learning-based Acute Lymphocytic Leukemia (ALL) detection system that is both efficient and easy. To overcome the critical challenges associated with feature extraction, we used EfficientNet, the most recent and most substantial deep learning model. In this article, eight EfficientNets variations are used to extract features and are compared based on classification accuracy. This work uses an ensemble of three sophisticated classifiers, namely Support Vector Machine (SVM), Random Forest, and Logistic Regression, which achieves a classification accuracy of 98.5%.     

视频监控系统中的运动目标分类技术研究

基于视频的运动分析是近年来计算机视觉领域备受关注的研究方向.提出了一种用于视频监控系统的运动目标分类算法,可以将运动目标分类为几种事先定义好的类别:人、人群、汽车、自行车.定义了运动目标的几种简单有效的形状特征,并选择基于小样本学习理论的支持向量机对不同目标进行分类.最后,提出隔帧分类的思想并描述了几种提高分类效率的方法,以满足实时性要求.实验证明该方法能较好地区分人、人群、车、自行车等目标.     

Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection. In this paper, a new method is presented to capture the contour edge, texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm. More abundant texture information is extracted. Cloud images can be obtained a multiscale and multidirection decomposition. The coefficient matrix from Contourlet transform of ground nephogram is calculated. The energy, mean and variance characteristics calculated from coefficient matrix are composed of the feature information. The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis. Then Support Vector Machines (SVM) classifier is used to classify according to the frequency information of the trend graph of data series. It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.     

基于响应协方差小波变换和SVD的结构工作模态参数识别

对系统响应的协方差作小波的时频分解,利用信号互协方差与自协方差的小波变换系数的比值来识别结构的工作模态振型,由矩阵奇异值分解(SVD)从小波变换时频分析结果确定小波脊,通过实际结构多测点数据,利用小波系数比值来反映振型,识别结构各阶工作模态参数(固有频率、阻尼比和振型)。用数值模拟算例和实桥环境振动试验数据对方法进行了验证,并与频域峰值法和时域随机子空间识别方法结果进行了比较,结果表明,该方法可以准确地识别出结构的工作模态参数,特别是阻尼和振型的识别。     

Multi-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-class classification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland law enforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabis plant and then to conclude if the plantation is legal or not. This classification is mainly performed when the plant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a time consuming and costly procedure. A previous study made by the authors has investigated this problematic [1] and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at an early stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on the relative proportions of eight major leaf compounds. The aims of the present work are on one hand to continue former work and to optimize the methodology for the discrimination of drug- and fibre type cannabis developed in the previous study and on the other hand to investigate the possibility to predict illegal cannabis varieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namely Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest Neighbour Classification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines (RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method was assessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabis with drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiers are not able to manage the distribution of classes in which some overlap areas exist for both classification problems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and 12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBF SVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabis plants coming from police seizures. In forensic case work this study shows that the discrimination between cannabis samples at an early stage of growth is possible with fairly high classification performance for discriminating between cannabis chemotypes or between drug type cannabis varieties.     

齿轮传动系统损伤检测与多故障分类研究

研究了数据挖掘的支持向量机的智能故障检测与诊断方法。通过对齿轮系统在不同的运转状态下的工作状况进行试验测试分析,获取了有关的测试信号,并对不同的故障振动特征信号进行了特征提取与分析研究。在此基础上将支持向量机引入到齿轮传动的损伤检测与诊断之中,建立了两分类和多分类分类器,研究了支持向量机的两分类和多类分类算法。通过分析处理、训练和测试仿真数据以及齿轮振动特征信号,对齿轮系统在各种不同转速下不同故障进行了预测、分类和诊断。研究表明, 支持向量机能够很好的区分不同运转状况下各种典型齿轮损伤与故障,低转速下识别率更高,为95%,特别是对各种复合类故障具有较高的识别精度、识别率在81%以上。它在齿轮故障诊断中具有较好诊断识别能力与发展前景,是一种有效地损伤检测与诊断新方法。     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

Failure and reliability prediction by support vector machines regression of time series data

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.     

使用支持向量机的纹理识别方法

将支持向量机应用到纹理识别领域,提出了一种基于支持向量机和小波变换的新型纹理识别方法。该方法用小波变换各子带图像共生矩阵参数、分析窗口大小、像素均值和像素标准差等参数作为纹理特征,解决了描述不同尺度纹理的难题。以多类支持向量机作为分类器,用输出纠错码把二分类器扩展到多类,提高了分类器的泛化能力。在包含25类单色自然纹理的图像库上进行识别试验,结果表明,该方法识别错误率小于10%,识别正确率比传统的贝叶斯等方法提高了2%左右,获得了更高的识别正确率,且推广性更好。     

基于SVD-TRIM特征和LSSVM人脸识别方法

人脸特征的选择对识别结果起关键作用。传统上只提取较大奇异值特征作为识别特征的人脸识别方法,识别率不高,对表情和姿态变化敏感。SVD-TRIM算法选择的奇异值识别特征融合了人脸整体和局部细节特征,并采用基于＂一对一＂的LSSVM多类分类器分类识别。实验结果表明SVD-TRIM算法选择的识别特征对提高识别率具有较大贡献,且对光照、姿态和表情具有鲁棒性。     

基于最小均方无失真响应和支持向量机的被动声纳目标识别

为了有效地进行被动声纳识别,研究了一种运用最小均方无失真响应（Minimum Variance Distortionless Response,MVDR）谱系数作为特征参数,用多分类支持向量机作为分类器,进行被动声纳目标识别的方法。实验表明,在不同数目的训练样本情况下,基于最小均方无失真响应谱系数和多分类支持向量机的被动声纳目标识别方法使系统的性能显著提高,具有很好的识别效果和应用价值.其优于传统的神经网络作为分类器的识别方法,尤其是在训练样本较少情况下,识别率具有很大的提高。