基于支持向量机的油封缺陷图像检测方法

提出一种基于支持向量机分类的油封缺陷图像检测方法,把油封外观中的有无缺陷看作两种不同的类别模式,应用支持向量机对两类不同的样本采样学习,然后进行分类判断。采集油封各部位图像并进行预处理,利用算法切割出各个检测区域图像,根据油封主要部位的各类缺陷特点,选取不同特征参数。应用径向基核函数建立支持向量机识别模型,并通过实验实现核函数参数寻优。实验结果表明,该方法具有检测成本低、可靠性高、泛化能力强、容易在线实施等特点,具有实用推广价值。     

Facial Expression Recognition Model Depending on Optimized Support Vector Machine

In computer vision, emotion recognition using facial expression images is considered an important research issue. Deep learning advances in recent years have aided in attaining improved results in this issue. According to recent studies, multiple facial expressions may be included in facial photographs representing a particular type of emotion. It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition. The main contribution of this paper is to propose a facial expression recognition model (FERM) depending on an optimized Support Vector Machine (SVM). To test the performance of the proposed model (FERM), AffectNet is used. AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online. The FERM is composed of three main phases: (i) the Data preparation phase, (ii) Applying grid search for optimization, and (iii) the categorization phase. Linear discriminant analysis (LDA) is used to categorize the data into eight labels (neutral, happy, sad, surprised, fear, disgust, angry, and contempt). Due to using LDA, the performance of categorization via SVM has been obviously enhanced. Grid search is used to find the optimal values for hyperparameters of SVM (C and gamma). The proposed optimized SVM algorithm has achieved an accuracy of 99% and a 98% F1 score.     

基于遗传算法优化支持向量机的超声图像缺陷分类

超声图像缺陷在分类时由于存在样本数量少、样本类别多、不易区分等问题,分类的准确率较低。针对这些问题,提出了基于遗传算法优化支持向量机的超声图像缺陷分类方法。该方法首先通过图像处理提取超声图像缺陷的特征数据,然后训练支持向量机作为超声图像缺陷分类器,最后采用遗传算法优化参数求得最优的分类器。实验结果表明,提出的超声图像缺陷分类器在识别率方面优于其他方法的分类器,综合识别率达到了90%,可以有效地辅助工作人员对超声图像缺陷进行分类识别。     

基于VMD能量熵与优化支持向量机的轴承故障诊断

滚动轴承早期故障信号比较微弱,且受噪声与振动耦合影响,导致其故障判别失准.基于变分模态分解算法(VMD)与能量熵结合构建多模态特征矩阵,以灰狼算法(GWO)优化支持向量机(SVM)参数,提出VMD-Entropy-OSVM轴承智能故障诊断,采用轴承实验数据验证所提方法的有效性与优越性.实验结果表明:VMD-Entrop...     

Detection of Number of Wideband Signals Based on Support Vector Machine

In array signal processing, number of signals is often a premise of estimating other parameters. For the sake of determining signal number in the condition of strong additive noise or a little sample data, an algorithm for detecting number of wideband signals is provided. First, technique of focusing is used for transforming signals into a same focusing subspace. Then the support vector machine (SVM) can be deduced by the information of eigenvalues and corresponding eigenvectors. At last, the signal number can be determined with the obtained decision function. Several simulations have been carried on verifying the proposed algorithm.     

Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector Machines (SVM), Random Forest, K-Neighbors Regressor, and Decision Tree Regressor were utilized as the basic models. The Adaptive Dynamic Polar Rose Guided Whale Optimization method, named AD-PRS-Guided WOA, was used to pick the optimal features from the datasets. The suggested model is compared to models based on five variables and to the average ensemble model. The findings indicate that the presented model using Random Forest results in a Root Mean Squared Error (RMSE) of (0.0102) for bandwidth and RMSE of (0.0891) for gain. This is superior to other models and can accurately predict antenna bandwidth and gain.     

基于自适应粒子群算法和支持向量机的控制图模式识别

针对目前多品种、复杂化的生产趋势,提出了一种基于自适应变异的粒子群算法（AMPSO）和支持向量机（SVM）的控制图失效模式识别的方法。利用SVM小样本学习能力,设计一对一的SVM多分类器进行控制图模式识别,并利用AMPSO算法优化SVM核函数的参数。通过对10种控制图模式(6种基本模式和4种混合模式)的20维特征仿真数据对该方法进行检验,并通过与BP、SVM、PSO SVM识别方法的对比分析。仿真试验表明该方法有效提高了控制图模式的识别精度,达到9814%,而BP仅有75%,为控制图在线实时识别提供了一种可行的途径。      

面向阀门内漏声发射检测的支持向量机分类建模

针对阀门泄漏声发射检测的研究中存在回归建模不准确的问题,综合考虑实际应用需要,开展了阀门液体泄漏的分类建模研究.分析了阀门泄漏声发射的机理和基本特征,建立了阀门泄漏声发射信号特征量与泄漏等级的支持向量机分类模型.在工业生产现场进行阀门泄漏声发射信号采集实验,对采集到的信号进行预处理和特征提取.采用网格搜索法寻找最优训练...     

基于商空间和支持向量机的滚动轴承故障智能诊断

针对滚动轴承故障诊断中存在的故障类型多样且有效特征难以选取等问题,提出了一种基于商空间和支持向量机的智能诊断模型。该模型利用商空间的粒化分层思想将输入样本按照不同等价关系进行粒化分层,并对每一粒度层的时域和频域特征进行约简,然后将每一层约简后的特征向量输入到支持向量机进行状态识别,最后对各粒度层状态识别结果加权融合得到最终结果。利用轴承全寿命试验数据对该模型进行验证,识别精度达到96.92%。     

基于BP神经网络和最小二乘支持向量机的灰熔点预测和对比

为了预测燃煤锅炉受热面的结渣情况,以灰成分金属氧化物、煤灰SO₃含量以及结渣评判指标为自变量,灰熔点变形温度(deformation temperature,DT)和软化温度(softening temperature,ST)为因变量,建立了BP神经网络(BP neural network,BPNN)和最小二乘支持向量机(least squares support vector machine,LSSVM)的灰熔点预测模型。回归分析和误差分析结果表明:针对样本量多的DT预测过程,2种模型精度接近,预测结果置信度均达到95%,相关系数均约为0.92,平均相对误差均约为3.4%;针对样本量较少的ST预测过程,LSSVM模型预测效果较优,相关系数为0.950 52,高于BPNN模型的0.904 26,平均相对误差为4.98%,并且大误差点个数少于BPNN模型。因此,LSSVM模型能够更准确预测飞灰的DT和ST。     

基于SVM的电子分析天平温度漂移补偿方法

电磁力平衡传感器等关键部件的温敏特性是引起电子分析天平温度漂移的主要因素。针对电子分析天平温度漂移问题提出了基于支持向量机的补偿方法。通过分析引起电子分析天平温度漂移误差的原因,将温度敏感部件的温升和电子分析天平的温度漂移数据作为模型输入,运用自适应参数优化方法寻找最优参数,建立电子分析天平温度漂移误差模型并进行温度漂移补偿。通过对量程200g、分辨力0.1mg的电子分析天平进行补偿检验,结果表明全量程内的示值误差绝对值≤0.3mg,优于国家标准GB/T 26497-2011《电子天平》规定的I级天平对温度漂移指标的要求。     

基于最小二乘支持向量机的反向建模的铝合金变形抗力模型

根据某1+4铝热连轧厂现场采集的大量轧制数据对几种铝合金变形抗力利用最小二乘支持向量机进行了反向建模回归分析,用细菌觅食优化算法对支持向量机的参数进行了优化。将回归后的变形抗力模型用于二级设定计算中的轧制力预报,结果表明回归后的模型适用于轧制现场,精度优于传统模型。     

A novel approach for MR brain tumor classification and detection using optimal CNN-SVM model

The segmentation of brain tumors in magnetic resonance imaging plays a significant role in the field of image processing. This process has high computational complexity when handled manually by clinical experts. The accuracy in classifying and segmenting the brain tumor depends on the radiologists' experience. The computer-aided diagnosis-based brain tumor segmentation approach is proposed to overcome the existing limitations. The proposed convolutional neural network and support vector machine approach consists of the following stages. In the preprocessing stage, unwanted noise and intensity inhomogeneity are suppressed using an anisotropic diffusion filter. Then, the features are extracted using the deep convolutional neural network, and based on the features; the input brain image is classified into normal or abnormal using a support vector machine classifier. The proposed method gives a more successful accuracy rate of 2.11%. Compared with the other methods, the sensitivity and specificity values are also improved to 4.79% and 1.19%.     

基于卷积神经网络模型的遥感图像分类

研究了遥感图像的分类,针对遥感图像的支持向量机(SVM)等浅层结构分类模型特征提取困难、分类精度不理想等问题,设计了一种卷积神经网络(CNN)模型,该模型包含输入层、卷积层、全连接层以及输出层,采用Soft Max分类器进行分类。选取2010年6月6日Landsat TM5富锦市遥感图像为数据源进行了分类实验,实验表明该模型采用多层卷积池化层能够有效地提取非线性、不变的地物特征,有利于图像分类和目标检测。针对所选取的影像,该模型分类精度达到94.57%,比支持向量机分类精度提高了5%,在遥感图像分类中具有更大的优势。     

基于PSO的SVR模型在多联机功耗预测上的应用

支持向量回归(SVR)模型在多联机系统功耗预测稳定性和精度上存在不足,本文引入粒子群优化(PSO)算法,对SVR预测模型的惩罚系数C和核参数γ进行最优求解,来改善模型预测性能。在制冷剂充注量为95. 75%工况下,对多联机组进行运行实验,并对实验数据进行预处理。基于PSO算法建立PSO-SVR模型,对多联机功耗进行预测,并与SVR模型的预测结果和理论公式计算结果进行对比。结果表明:SVR、PSO-SVR、理论公式计算法总体预测误差分别为1. 43%、1. 08%和1. 57%,均方根误差RMSE分别为105. 36、88. 79、91. 37 W,参数寻优结果为惩罚系数C=10 000和核参数γ=4. 275。粒子群优化算法的引入显著提高了SVR模型的预测精度和稳定性;相较于理论公式计算法,PSO-SVR精度更高,且需要测量的参数数目明显减少,在降低了测量系统复杂性同时更具经济适用性。     

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

An intelligent system for gastrointestinal polyp detection in endoscopic video using fusion of bidimensional empirical mode decomposition and convolutional neural network features

This paper presents an intelligent system for gastrointestinal polyp detection in endoscopic video. Video endoscopy is a popular diagnostic modality in assessing the gastrointestinal polyps. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of polyp and thus improve the accuracy of diagnosis results. The proposed method illustrates an automatic system based on a new color feature extraction scheme as a support for gastrointestinal polyp detection. The scheme is the combination of color empirical mode decomposition features and convolutional neural network features extracted from video frames. The features are fed into a linear support vector machine to train the classifier. Experiments on standard public databases show that the proposed scheme outperforms the previous conventional methods, gaining accuracy of 99.53%, sensitivity of 99.91%, and specificity of 99.15%.     

Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification

Lung tumor is a complex illness caused by irregular lung cell growth. Earlier tumor detection is a key factor in effective treatment planning. When assessing the lung computed tomography, the doctor has many difficulties when determining the precise tumor boundaries. By offering the radiologist a second opinion and helping to improve the sensitivity and accuracy of tumor detection, the use of computer-aided diagnosis could be near as effective. In this research article, the proposed Lung Tumor Detection Algorithm consists of four phases: image acquisition, preprocessing, segmentation, and classification. The Advance Target Map Superpixel-based Region Segmentation Algorithm is proposed for segmentation purposes, and then the tumor region is measured using the nanoimaging theory. Using the concept of boosted deep convolutional neural network yields 97.3% precision, image recognition can be achieved. In the types of literature with the current method, which shows the study's proposed efficacy, the implementation of the proposed approach is found dramatically.     

Machine learning aided nanoindentation: A review of the current state and future perspectives

The solution of instrumented indentation inverse problems by physically-based models still represents a complex challenge yet to be solved in metallurgy and materials science. In recent years, Machine Learning (ML) tools have emerged as a feasible and more efficient alternative to extract complex microstructure-property correlations from instrumented indentation data in advanced materials. On this basis, the main objective of this review article is to summarize the extent to which different ML tools have been recently employed in the analysis of both numerical and experimental data obtained by instrumented indentation testing, either using spherical or sharp indenters, particularly by nanoindentation. Also, the impact of using ML could have in better understanding the microstructure-mechanical properties-performance relationships of a wide range of materials tested at this length scale has been addressed.The analysis of the recent literature indicates that a combination of advanced nanomechanical/microstructural characterization with finite element simulation and different ML algorithms constitutes a powerful tool to bring ground-breaking innovation in materials science. These research means can be employed not only for extracting mechanical properties of both homogeneous and heterogeneous materials at multiple length scales, but also could assist in understanding how these properties change with the compositional and microstructural in-service modifications. Furthermore, they can be used for design and synthesis of novel multi-phase materials.