Observation of Blobs and Holes in the Boundary Plasma of EAST Tokamak

Intermittent convective transport at the edge and in the scrape-off layer （SOL） of EAST was investigated by using fast reciprocating Langmuir probe. Holes, as part of plasma structures, were detected for the first time inside the shear layer. The amplitude probability distribution function of the turbulence is strongly skewed, with positive skewed events （＂blobs＂） prevailing in the SOL region and negative skewed events （＂holes＂） dominant inside the shear layer. The statistical properties coincide with previous observations from JET. The generation mechanism of blobs and holes is also discussed. In addition burst structure and dynamics character of them are also presented.

Plasma Blobs and Turbulence Impacting Magnetic Confinement(A Short Review)

Space-based plasma(i.e.,a highly ionized gas or the fourth state of matter)blobs are isolated pockets of this highly ionized gas made up of charged particles.These blobs are believed to have a substantial impact on the structure and dynamics of the cosmos and can be seen in a variety of astronomical objects,including stars,galaxies,and the intergalactic medium.Some plasma blobs are connected to intense phenomena like magnetic reconnection,shock waves,and supernovae,while others may be the result of more passive processes like cooling and gravitational collapse.In both astrophysics and plasma physics,there is ongoing research on the characteristics and behavior of plasma blobs.This phenomenon has a very adverse effect on tokamak-based MCF(magnetic confinement fusion),which is the subject of this short review paper.

Observation of blobs using a gas puff imaging diagnostic on the HL-2A tokamak

A gas puff imaging(GPI) diagnostic has been developed and applied to measure edge plasma turbulence on the HL-2A tokamak.The principle and experimental setup of GPI are described.GPI is applied to investigate blobs in the edge and scrape-off layer.Statistical characterizations of GPI line emission intensity are calculated, including the probability density functions(PDFs),skewness, and kurtosis of the intensity, which are found to be consistent with measurements by Langmuir probes.Besides, the track of blob motions is recorded by time sequence of individual frames.The characteristics of the original images and the relatively high-frequency(>10 kHz)/low-frequency(1–10 kHz) component images are illustrated.The observation of the blob’s structures and high-speed motions proves the success and high performance of the GPI diagnostic.

Searching of Images Based on Content Using Blobs

Now a day’s image searching is still a challenging problem in content based image retrieval (CBIR) system. Most system operates on all images without pre-sorting the images. The image search result contains many unrelated image. The aim of this research is to propose a new method for content based image indexing and research based on blobs feature extraction and existing edges in the image and classification of image to different type and to search image which are similar the given research.

J-TEXT托卡马克装置间歇性爆发事件的实验研究

为研究密度对J-TEXT托卡马克边缘湍流及间歇性爆发事件的影响,采用往复式朗缪尔静电探针测量边缘等离子体参数,采用概率分布函数以及条件平均等分析方法,详细比较了不同线积分密度情况下边缘等离子体电子温度、密度、电位和径向电场等径向分布及涨落特征。结果表明:随着等离子体密度的增加,边缘电子温度降低,在最后闭合磁面附近的E×B剪切流减弱;密度涨落在刮削层(SOL)具有更大的正偏度与峰度,涨落幅度>1.5倍标准差的气泡(blobs)所引起的粒子输运占总输运的占比从25%上升到45%;但blobs爆发的时间间隔不依赖于积分密度,保持在35μs左右。因此,随着密度增加,blobs对边缘输运的影响更大。

基于BLOB区域和边缘特征分析的准直图像双光学目标识别方法

针对光路对接准直目标识别算法对双目标粘连状态无法判别的问题,提出了基于二进制大对象(Binary Large Object,BLOB)区域和边缘特征分析的准直图像双光学目标识别方法。首先,对二值化图像进行数字形态学处理,计算全图各BLOB区域的面积、中心、轴长、区域、有效BLOB区域个数等信息。其次,对有效BLOB区域个数大于1的完全分离双目标准直图像,统计各BLOB区域中心分别为位于两个面积最大的BLOB区域内的BLOB数量,数量小的候选BLOB区域为主激光目标,数量大的候选BLOB区域为模拟光目标。然后,对于有效BLOB区域个数等于1的待识别图像,从左、右、上、下4个方向分别提取模板边缘图像的有效坐标序列和待识别边缘图像坐标序列,搜索有效坐标序列和待识别边缘图像坐标序列的最大相关系数对应的有效坐标序列。当4个方向的相关系数全部大于0.95时,待识别图像为模拟光目标;当4个方向的相关系数都小于0.95时,待识别图像为主激光目标;否则待识别图像为粘连图像。实验结果表明:提出的双光学目标识别算法,不仅能够识别完全分离的模拟光目标和主激光目标,误差小于3个像素,处理时间小于1 s,而且能够判别处于粘连状态的光学目标和单个独立的光学目标,满足光路对接准直图像识别算法对于自适应性、精度和效率的要求。

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

基于区域生长和BLOB分析的氢能电池涂布缺陷检测

针对氢能燃料电池涂布缺陷检测难度大的问题,提出了基于区域生长与BLOB分析的涂布缺陷图像检测方法。首先对图像进行ROI区域提取及高斯滤波处理,然后利用区域生长分割图像,最后利用Blob分析标记连通区域。实验结果表明,文中方法能够有效检测出氢能电池涂布缺陷,准确率达到检测要求。

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes.Various technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene comprehension.This paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an image.The methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet architecture.Afterward,we use an annotation tool to accurately label the segmented regions.Upon labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob characteristics.For the classification stage,a convolution neural network(CNN)is employed.This comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in images.The experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been documented.After analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

基于机器视觉引导的电容高精度装配系统研究

针对电容装配系统中多数量、不同位置排布下电阻容器的装配存在定位精度低、装配效率低等问题,提出了一种基于机器视觉引导的电容高精度装配系统。首先,运用十二点标定法确定坐标系间的转换关系;其次,提出基于blob分析的改进定位算法确定电阻容器的中心位置;最后,引入偏移量补偿,引导机器人完成每个对应位置的装配。实验结果表明,所提出的装配系统的平均装配误差为0.05 mm,且装配准确率达到99.8%,实际装配误差要求不超过0.1 mm,因此满足实际生产需求,有效提高了生产装配效率。

基于BLOB分析和深度学习的高温锻件尺寸测量研究

本文提出了一种使用单个测温相机和单个2D工业相机等测量设备,结合BLOB分析和深度学习技术,实现对高温锻件的全自动二维尺寸测量的方法。文章对目前国内外关于高温锻件尺寸测量的研究做了一定的分析,采用BLOB分析对锻件尺寸测量进行了实验研究和弊端分析,最后采用BLOB分析和深度学习相结合的视觉检测方法进行了实验研究,研究结果表明该方法可以实现对高温锻件单面尺寸的快速测量。

基于数据库的文档管理系统及其在企业中的应用

讨论了基于数据库的文档管理系统的解决方案及其实现的关键技术 ,特别是通过ADO中的Stream对象对数据库中的文档资料进行读写 。

应用处理器PXA255上的嵌入式Linux系统开发

介绍了Intel公司新近推出的一种嵌入式应用处理器———PXA255,研究了PXA255的开发平台DBPXA255,着重阐述了如何在基于嵌入式应用处理器PXA255的平台上进行Linux系统开发,特别讨论了怎样将Blob与Linux内核移植到PXA255,最后分析了该系统的应用前景。

在VC中利用ADO技术操作数据库中的BLOB数据

数据库中的BLOB(BinaryLargeObject,二进制大对象)字段可以存放如图形、声音文件或带格式的大文本文件等大对象。阐明其基本工作原理和工作过程,为多媒体信息管理系统的开发提供了有效的解决方案。

基于多方位视觉的果实形状特征的提取研究

针对传统瓜果分级中捕捉单幅果实图像来分析果实特征时信息较少的缺陷,提出了一种基于多方位视觉系统的果实形状特征提取算法。同步采集3个方向的果实图像信息,经OHTA空间上的图像分割和改进的BLOB算法去噪声等处理后,分别给出果实的多方位的图像特征信息。运用统计学原理,设计了线性分类器,对3个方向的图像特征信息进行了互补性融合,得出分级判别准则。利用开发的智能瓜果精选分级试验样机,验证了该算法的有效性,试验结果证明识别成功率达到97%,达到了实用的要求。

基于Blob算法的印刷缺陷在线检测的研究

将常见的印刷缺陷分为点、线、面3类,介绍了印刷缺陷在线检测的基本流程,说明了Blob算法检测缺陷的具体步骤,并运用Blob算法结合缺陷面积及周长的特征,成功对印品缺陷进行了提取及归类,最终得到了缺陷的面积、位置、类型等特征参数,在程序中实现了印刷品缺陷的检测。

基于Blob的车辆识别及其跟踪算法研究

智能交通系统是未来交通发展的必然趋势,乒丶际醯难芯烤哂猩钤兜囊庖?本文以在静态背景下动态地识别并跟踪汽车为目的,对摄像头采集的图像进行处理.如何准确地从复杂的交通图像中提取车辆,是进行车辆识别的基础.本文采用Surendra背景[1]算法提取路面的背景,通过背景差法提取路面上的车辆.图像分割后,对二值图像进行Blob[2]分析,识别并对车辆进行分析,获取车辆的特征信息,包括车辆本身的参数信息和位置信息.依赖这些车辆信息,通过Kalman滤波器实现对车辆的跟踪,进而获得车辆的动态信息,包括车辆的速度、运行轨迹信息.

基于LZMA的数据库压缩存储应用研究

目前,诸多数据库应用系统的文字、图形、图像等大数据是直接存入数据库的大字段,其中文字数据以ASC II码存储,这使数据库数据急剧膨胀,且保密性差、传输速度慢、存取效率低。采用LZMA压缩算法,并自定义压缩二进制数据存储结构,把核心压缩二进制算法封装成API动态链接库,通过调用接口函数压缩大数据,再保存至数据库B lob字段;调用数据时,再将保存在数据库中的数据解压缩进行还原,这样不仅节省了数据库的磁盘空间,同时也提高了数据存取效率。

基于机器视觉技术的奶粉罐内壁缺陷检测系统设计

本文针对奶粉罐的质量检测进行研究,并设计了基于机器视觉技术的奶粉罐内壁缺陷检测系统。按检测精度要求,对该检测系统进行了硬件设计与选型,并采用LabVIEW技术编写了系统软件。针对奶粉罐内壁的特殊性,设计了适合罐体内壁缺陷检测的图像处理算法。检测系统已经安装到奶粉罐生产线上使用了半年,结果表明该检测系统能准确完成缺陷检测,满足生产需求。

长输管道泄漏检测系统设计与开发

设计并开发泄漏检测系统,该系统可以实时监测长输管道的运行状况。该系统主要包括3大模块:多源信息获取模块,数据存储与查询模块以及泄漏分析与诊断模块。通过OPC技术实时快速获取现场PLC信息,弥补SCADA系统采样频率低导致信息缺失的不足;利用BLOB格式存储大容量实时数据,并实现历史数据查询功能;应用投影降噪理论进行信号降噪,实现小泄漏特征信号获取;结合相关分析技术计算负压波传输时间差,并动态求解负压波波速。现场试验证明,该系统具有较高的定位精度,满足现场需求。