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。     

基于Faster R-CNN的保偏光纤偏振轴检测方法研究

针对基于端面成像的偏振轴检测方法光照鲁棒性不强、无法同时检测多根保偏光纤偏振轴的问题,提出一种基于Faster R-CNN的检测方法.训练Faster R-CNN神经网络模型,通过参数调优增强了应力区识别的鲁棒性,采用基于Zernike矩算法对应力区边缘点进行亚像素级定位以提高测量精度,分析了测量精度与误差的关系.仿真与实验表明,该方法的测量精度达到±0.1°,在增强光照鲁棒性的同时实现了对多根保偏光纤偏振轴的检测.     

微量热法研究黄嘌呤氧化酶反应

在热导工热量计双参数理论模型的基础上，建立了较快酶仲反应研究的双参数初始速度法的热动力学模型，用微量热法研究了黄嘌呤氧化酶催化氧化黄嘌呤的热动力学，该较快酶促反应遵循Ｍｉｃｈａｅｌｉｓ－Ｍｅｎｔｅｎ这，在２９８．１５Ｋ和ＰＨ＝７．５时，其米氏常数为１．０４×１０＾－３ｍｏｌ．Ｌ＾－１，与文献结果相符。     

基于改进深度网络的车辆乘员数量检测研究

车辆乘员数量检测研究对推进HOV车道建设具有重要意义,本文以Faster RCNN网络模型为基础,结合多光谱红外系统获得的汽车驾驶室图像来展开研究.现有数据中因过曝、曝光不足等问题导致图像中目标特征差异大,网络检测的准确率不高,对此采用变形结构的卷积计算提高特征单元的感受野和目标边缘信息的表述能力,采用变形的ROI-P...     

基于Faster R-CNN的钢轨表面缺陷识别研究

外界因素常会干扰钢轨表面缺陷检测仪器,导致其精度和效率降低。文中研究了一种基于Faster R-CNN网络检测钢轨表面缺陷的方法。该方法将预处理后的图像进行反转,利用Radon变换实现钢轨图像的投影。投影曲线中,利用钢轨长度为定值且灰度值小于图像平均值的特性,完成对钢轨表面区域的提取。然后通过区域建议网络提取候选区域,并与Fast R-CNN网络的区域建议对比分析,完成Faster R-CNN网络对钢轨的表面缺陷检测。试验数据表明,裂缝、疤痕、磨损和划伤4种缺陷的识别精度分别为92.17%、91.85%、93.45%和93.27%,证明使用该方法能够高效而又准确地识别钢轨的表面缺陷。     

Monolithic silica rod columns for high-efficiency reversed-phase liquid chromatography

Chromatographic properties of a new type of monolithic silica rod columns were examined. Silica rod columns employed for the study were prepared from tetramethoxysilane, modified with octadecylsilyl moieties, and encased in a stainless-steel protective column with two polymer layers between the silica and the stainless-steel tubing. A 25 cm column provided up to 45,000 theoretical plates for aromatic hydrocarbons, or a minimum plate height of about 5.5 μm, at optimum linear velocity of ca. 2.3 mm/s and back pressure of 7.5 MPa in an acetonitrile-water (80/20, v/v) mobile phase at 40°C. The permeability of the column was similar to that of a column packed with 5 μm particles, with K(F) about 2.4×10(-14) m(2) (based on the superficial linear velocity of the mobile phase), while the plate height value equivalent to that of a column packed with 2.5 μm particles. Generation of 80,000-120,000 theoretical plates was feasible with back pressure below 30 MPa by employing two or three 25 cm columns connected in series. The use of the long columns enabled facile generation of large numbers of theoretical plates in comparison with conventional monolithic silica columns or particulate columns. Kinetic plot analysis indicates that the monolithic columns operated at 30 MPa can provide faster separations than a column packed with totally porous 3-μm particles operated at 40 MPa in a range where the number of theoretical plates (N) is greater than 50,000.     

Faster analysis by reversed-phase high-performance liquid chromatography

Summary Many analysts are not taking full advantage of the high speed possibilities of modern LC. Some analytical procedures reported in the literature, and many in regular use in control laboratories, could be achieved in less time without loss in precision. Some factors which affect retention times are discussed and the advantages and disadvantages of employing shorter column lengths and finer packing materials in reversed-phase HPLC are examined. The effect on efficiency of increased flow rates with 10,5 and 3 m ODS materials is shown. The ability to couple shorter column lengths without loss of efficiency is also demonstrated. This allows a minimum length to be selected that gives adequate resolution. Examples of high speed separations are shown and limitations in state of the art HPLC equipment and chromatographic data systems are discussed briefly.Presented at the 14th International Symposium on Chromatography London, September, 1982     

深度学习在绝缘子红外图像异常诊断的应用

绝缘子的红外图像分析一般采用图像处理的方法,易受背景环境和数据量的影响,准确率和效率均较低,本文提出一种深度学习的异常诊断方法,基于改进的Faster R-CNN方法搭建检测网络,开展不同类型的绝缘子测试.研究结果表明:相对于神经网络(Back Propagation,BP)、Faster R-CNN方法,本文方法可高...