基于深度学习和灰度纹理特征的铁路接触网绝缘子状态检测

铁路接触网绝缘子状态检测对铁路行车安全有着 重大的意义,为解决目前人工对绝缘 子图像检测结果的不确定性,提出一种深度学习结合灰度纹理特征的检测方法。首先使用 Faster R-CNN (faster region-based convolutional neural network)目标检测算法对图像中绝缘子精确识别,再通过灰度共生矩阵对绝缘子纹理 特征进行分析提取,之后结合支持向量机将绝缘子分为正常绝缘子和异常绝缘子,实验数 据结果证明使用能量、熵、相关度3种纹理特征进行绝缘子状态分类时对实验数据中的正 常状态绝缘子的分类精度可达100%,异常状态绝缘子的分类精度达97.5%,最后依据绝缘 子图像灰度分布的周期性特点,利用灰度积分投影将异常绝缘子分为破损绝缘子和夹杂异 物绝缘子。实验结果表明所提方法可以有效对绝缘子状态进行检测分类。     

Banach spaces that admit support sets

It is shown that the existence of a closed convex set all of whose points are properly supported in a Banach space is equivalent to the existence of a certain type of uncountable ordered one-sided biorthogonal system. Under the continuum hypothesis, we deduce that this notion is weaker than the existence of an uncountable biorthogonal system.

     

基于改进NAS-RIF的三维显微图像盲复原

对二维NAS-RIF算法进行了改进,并将它用于三维显微图像复原,分别用模拟样本和真实生物样本进行了实验。针对原算法对噪声有放大作用,提出了基于中值滤波去噪的改进。在支持域的确定、最小点的搜索上进行改进,提高复原质量。改进了原算法仅适用于均匀背景的不足,扩大了应用范围。实验结果表明,该算法在三维显微图像盲复原中具有较好的效果。     

Optimization of Ultrasonic-Assisted Extraction of Active Components and Antioxidant Activity from Polygala tenuifolia: A Comparative Study of the Response Surface Methodology and Least Squares Support Vector Machine

Dried roots of Polygala tenuifolia (YuanZhi in Chinese) are widely used in Chinese herbal medicine. These components in YuanZhi have significant anti-oxidation properties owing to high levels of 3,6’-disinapoylsucrose (DISS) and Polygalaxanthone III (PolyIII). In order to efficiently extract natural medicines, response surface methodology (RSM) and least squares support vector machine (LSSVM) were used for the modeling and optimization of ultrasound-assisted extraction of DISS and PolyIII together to determine the antioxidant activity of the extracts obtained from YuanZhi. For the optimal combination of the comprehensive yield of DISS and PolyIII (Y), the Box-Behnken design (BBD) was used to improve extraction time (X₁), extraction temperature (X₂), liquid–solid ratio (X₃), and ethanol concentration (X₄). The optimal process parameters were determined to be as follows: extraction time, 93 min; liquid–solid ratio, 40 mL/g; extraction temperature, 48 °C; and ethanol concentration, 67%. With these conditions, the predictive optimal combination comprehensive evaluation value is 13.0217. It was clear that the LS-SVM model had higher accuracy in predictive and optimization capabilities, with higher antioxidant activity and lower relative deviations values, than did RSM. Hence, the LS-SVM model proved to be more effective for the analysis and improvement of the extraction process.     

采用过程推理系统的作战方案生成研究

作战指挥决策支持系统是指挥自动化系统的重要组成部分,作战方案生成是作战指挥决策支持系统的基本功能.为了能够快速、准确地生成作战方案,为指挥员提供决策依据,建立了一种过程推理系统的改进模型并将其运用于作战方案生成.根据对过程推理系统的介绍和分析,针对其重复劳动和交互不足的问题,对战术意图集进行分类处理、对所选作战规划进行合并处理以及对更新状态规则和评价标准进行改进.通过这些改进可提高其运行效率,实现对战术意图的批处理.最后建立了改进的过程推理系统的体系结构模型,描述了其生成作战方案的运行过程,表明其可以基本满足作战方案生成的要求.     

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...     

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。