基于MAXWELL2D静磁场分析的特种电机教学

特种电机应用越来越广泛,已成为“电机学”教学的重要内容,其结构和磁场分布不同于传统同步电机,是“电机学”教学的难点之一。本文以永磁电机为例,探讨了MAXWELL2D建模实现电机旋转结构动画显示的方法,并利用MAXWELL2D对永磁电机进行静磁场有限元分析,直观呈现电机内磁场分布特点,便于学生理解和接受特种电机的结构和原理。本文方法对于其他特种电机的教学具有参考价值。     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

支持向量机漏电特征模型的电力设施漏电识别

针对电力设施中漏电引起的故障问题,提出了新型的漏电特性分析方法,该方法融合了基于支持向量机的漏电算法模型,实现了电力设施漏电特性的图像采集和A/D转换处理,最后输出后的数据信息通过DSP计算模块进行计算,最终输出电力设施的强电漏电信息或者弱电漏电信息,实现了电力设施漏电信息的识别.该研究将传统技术中的漏电难以分析的问题...     

一种两阶段变量选择的LIBS定量分析方法

使用机器学习方法结合激光诱导击穿光谱(Laser Induced Breakdown Spectroscopy,LIBS)进行定量分析,变量选择的结果直接影响最终的定标模型.现有的变量选择方法多存在需要先验知识、计算量庞大等问题,因此提出一种两阶段变量选择方法.第一阶段为排序阶段,以皮尔逊相关系数r为排序准则快速排除与...     

改进缺陷,套刻和聚焦性能的第五世代浸没式曝光系统

论述了第五世代双扫描平台浸液式扫描曝光机的性能和进展。表明了在高速扫描状态下有生产价值的套刻和聚焦性能的实现。浸液式设备更多的关键部分与缺陷有关,而且该机的改进是通过有生产价值的缺陷水平方面来体现的。为了保持这种缺陷水平的改进效果,需要在圆片应用中进行专门稳定的测量。特加是边缘空泡除去(EBR)设计和圆片斜面良流线性是很重要的。     

基于审计方法的通用信息安全风险评估的设计

提出一种基于审计控制环的风险评估软件设计方法,在模块设计和评估自动化方面结合了最新的风险评估发展方向,在评估结果上能对措施的有效性进行预控制.     

A Fault Detection Method for Electrohydraulic Switch Machine Based on Oil-Pressure-Signal-Sectionalized Feature Extraction

A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.     

基于CVMD和DBO-SVM的光纤周界安防信号识别方法

为降低光纤周界安防信号中噪声对分类结果的影响,提升信号分类的准确率和运行效率,提出一种融合了相关变分模态分解(Correlation Variational Mode Decomposition, CVMD)、蜣螂算法(Dung Beetle Optimizer, DBO)和支持向量机(Support Vector Machine, SVM)的分类方法。利用CVMD去除原始信号中的噪声分量,并提取去噪后信号的能量、能量熵和峭度作为特征向量。采用DBO算法优化SVM,得到最佳惩罚因子和核函数参数,并构建DBO-SVM分类模型。搭建了基于相位敏感光时域反射(Φ-OTDR)技术的周界安防系统,采集了攀爬、敲击、踩踏和无入侵四类信号。实验结果表明,CVMD-DBO-SVM的分类准确率相比CVMD-PSO-SVM和CVMD-GA-SVM更高,达到了98.75%,同时运行时间更短,综合性能最优。     

图像分割的新理论和新方法

图像分割是是计算机视觉研究中的一个经典难题,已成为图像理解领域关注的一个热点.本文对近年来图像分割方法的研究现状与新进展进行了归纳总结.首先,简单介绍了图像分割的传统方法,包括基于区域的、基于边缘的和两者结合的图像分割方法.然后,分别从数学形态学、模糊集、神经网络、支持向量机、免疫算法、图论和粒度计算等方面对图像分割方法进行了重点讨论,并对应用每一种理论的最新研究进展作了评述.最后,对图像分割方法的发展趋势进行了展望.     

基于马尔科夫和HHT的图像拼接盲检测

基于对图像拼接技术的分析,提出了一种基于马尔科夫模型与Hilbert-Huang变换（HHT）的图像拼接盲检测算法。该算法计算图像DCT域上的马尔科夫转移概率矩阵,同时对图像进行Hilbert-Huang分析,得到两类特征值集,并通过计算相关系数矩阵分析了两者之间的相关性,最后使用支持向量机进行训练与分类。实验结果表明,相对于已有文献,该算法具有较高的检测准确率。