Ground-Based Cloud Using Exponential Entropy/Exponential Gray Entropy and UPSO

Objective and accurate classification model or method of cloud image is a prerequisite for accurate weather monitoring and forecast.Thus safety of aircraft taking off and landing and air flight can be guaranteed.Thresholding is a kind of simple and effective method of cloud classification.It can realize automated ground-based cloud detection and cloudage observation.The existing segmentation methods based on fixed threshold and single threshold cannot achieve good segmentation effect.Thus it is difficult to obtain the accurate result of cloud detection and cloudage observation.In view of the above-mentioned problems,multi-thresholding methods of ground-based cloud based on exponential entropy/exponential gray entropy and uniform searching particle swarm optimization(UPSO)are proposed.Exponential entropy and exponential gray entropy make up for the defects of undefined value and zero value in Shannon entropy.In addition,exponential gray entropy reflects the relative uniformity of gray levels within the cloud cluster and background cluster.Cloud regions and background regions of different gray level ranges can be distinguished more precisely using the multi-thresholding strategy.In order to reduce computational complexity of original exhaustive algorithm for multi-threshold selection,the UPSO algorithm is adopted.It can find the optimal thresholds quickly and accurately.As a result,the real-time processing of segmentation of groundbased cloud image can be realized.The experimental results show that,in comparison with the existing groundbased cloud image segmentation methods and multi-thresholding method based on maximum Shannon entropy,the proposed methods can extract the boundary shape,textures and details feature of cloud more clearly.Therefore,the accuracies of cloudage detection and morphology classification for ground-based cloud are both improved.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

A modified method of discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces

This paper presents an automated method for discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces.Specifically,the method consists of five steps:(1)detection of trace feature points by normal tensor voting theory,(2)co ntraction of trace feature points,(3)connection of trace feature points,(4)linearization of trace segments,and(5)connection of trace segments.A sensitivity analysis was then conducted to identify the optimal parameters of the proposed method.Three field cases,a natural rock mass outcrop and two excavated rock tunnel surfaces,were analyzed using the proposed method to evaluate its validity and efficiency.The results show that the proposed method is more efficient and accurate than the traditional trace mapping method,and the efficiency enhancement is more robust as the number of feature points increases.

Some Thoughts on the Earthquake Science Experimental Site——The Underground Cloud Map

The Western Yunnan Earthquake Predication Test Site set up jointly by the China Earthquake Administration,the National Science Foundation Commission of America,and United States Geological Survey has played an important role in development of early earthquake research work in China. Due to various objective reasons, most of the predicted targets in the earthquake prediction test site have not been achieved,and the development has been hindered. In recent years, the experiment site has been reconsidered,and renamed the "Earthquake Science Experimental Site". Combined with the current development of seismology and the practical needs of disaster prevention and mitigation,we propose adding the "Underground Cloud Map"as the new direction of the experimental site. Using highly repeatable, environmentally friendly and safe airgun sources,we could send constant seismic signals,which realizes continuous monitoring of subsurface velocity changes. Utilizing the high-resolution 3-D crustal structure from ambient noise tomography,we could obtain 4-D (3-D space+1-D time) images of subsurface structures, which we termed the "Underground Cloud Map". The"Underground Cloud Map" can reflect underground velocity and stress changes,providing new means for the earthquake monitoring forecast nationwide,which promotes the conversion of experience-based earthquake prediction to physics-based prediction.

A multi-layered policy generation and management engine for semantic policy mapping in clouds

The long awaited cloud computing concept is a reality now due to the transformation of computer generations.However,security challenges have become the biggest obstacles for the advancement of this emerging technology.A well-established policy framework is defined in this paper to generate security policies which are compliant to requirements and capabilities.Moreover,a federated policy management schema is introduced based on the policy definition framework and a multi-level policy application to create and manage virtual clusters with identical or common security levels.The proposed model consists in the design of a well-established ontology according to security mechanisms,a procedure which classifies nodes with common policies into virtual clusters,a policy engine to enhance the process of mapping requests to a specific node as well as an associated cluster and matchmaker engine to eliminate inessential mapping processes.The suggested model has been evaluated according to performance and security parameters to prove the efficiency and reliability of this multilayered engine in cloud computing environments during policy definition,application and mapping procedures.

Mapping High-Level Application Requirements onto Low-Level Cloud Resources

Cloud computing has created a paradigm shift that affects the way in which business applications are developed. Many business organizations use cloud infrastructures as platforms on which to deploy business applications. Increasing numbers of vendors are supplying the cloud marketplace with a wide range of cloud products. Different vendors offer cloud products in different formats. The cost structures for consuming cloud products can be complex. Finding a suitable set of cloud products that meets an application’s requirements and budget can be a challenging task. In this paper, an ontology-based resource mapping mechanism is proposed. Domain-specific ontologies are used to specify high-level application’s requirements. These are then translated into high-level infrastructure ontologies which then can be mapped onto low-level descriptions of cloud resources. Cost ontologies are proposed for cloud resources. An exemplar media transcoding and delivery service is studied in order to illustrate how high-level requirements can be modeled and mapped onto cloud resources within a budget constraint. The proposed ontologies provide an application-centric mechanism for specifying cloud requirements which can then be used for searching for suitable resources in a multi-provider cloud environment.

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

Star sensors are an important means of autonomous navigation and access to space information for satellites.They have been widely deployed in the aerospace field.To satisfy the requirements for high resolution,timeliness,and confidentiality of star images,we propose an edge computing algorithm based on the star sensor cloud.Multiple sensors cooperate with each other to forma sensor cloud,which in turn extends the performance of a single sensor.The research on the data obtained by the star sensor has very important research and application values.First,a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition,which can reduce the amount of data computation.Then,a mappingmodel between content and space is constructed to achieve low-rank image representation and efficient computation.Finally,the data collected by the wireless sensor is delivered to the edge server,and a differentmethod is used to achieve privacy protection.Only a small amount of core data is stored in edge servers and local servers,and other data is transmitted to the cloud.Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage,and has strong privacy.

低成本手持式稠密点云采集系统

针对现有室内外场景无法使用低成本便携式LiDAR很好地采集高稠密度点云数据的问题,本文研究设计了一种对低成本LiDAR和IMU传感器在硬件层面进行组合的低成本手持式稠密点云采集系统。采用简化残差计算量后的Faster-LIO当作里程计,通过传感器紧耦合的方式,将LiDAR和IMU之间的外参矩阵作为优化量,在采集的过程中进行实时优化以获得准确的相对位姿,从而得到满足精度要求的稠密点云。本文方法与经典的基于LOAM特征的LiDAR里程计及RTK里程计进行对比,试验结果表明,本文方法是有效的,手持式旋转激光雷达扩增了LiDAR的视野,而且对采用的里程计算法进行了改进,在减少算力要求降低成本的同时,大幅度降低了LiDAR和IMU等设备安装时无法避免的相互扰动问题,能较好地完成对场景稠密点云的采集及初步重建。

基于CiteSpace的国内云制造研究热点与发展趋势分析

借助CiteSpace软件对2010年至2022年发表在中国知网核心期刊上的云制造领域文献进行文献计量分析,通过对发文量、发文作者、发文机构、研究热点与研究前沿的分析,旨在对国内云制造领域的研究现状与研究趋势进行梳理。研究发现:云制造文献数量在2010年至2012年呈现出井喷现象,此后经历了反复下降上升的波动趋势;云制造文献主要来源于计算机领域相关期刊;国内云制造高产作者之间与研究机构之间合作关系较为紧密,呈现高度专业化的现象;近十三年来国内云制造领域研究内容可分为云制造平台构建、云制造模式及应用研究以及云制造服务,其中云制造服务是当前该领域的研究热点与趋势,且云制造服务研究视角逐渐从服务需求方扩展到服务供需双方。

改进海马优化算法的永磁同步电机多参数辨识

为解决永磁同步电机参数辨识速度慢、精度不足等问题,提出一种融合云模型和混沌变异的海马优化算法。该算法以海马优化算法为基础,引入混沌映射和随机反向学习策略,改善种群初始空间分布;采用自适应云模型,解决算法收敛精度低和减少陷入局部最优情况;加入混沌映射和高斯变异调节种群分布,以提高算法全局和局部开发能力。通过采集电机电压、角速度等信息,在永磁同步电机辨识模型中,使用改进后的算法对电机参数进行辨识。由仿真和实验对比,验证改进后算法在永磁同步电机电气和机械参数辨识上,具有更快速、稳定和准确的辨识效果,且辨识误差均在1.4%以内。

基于数字图像相关方法分析土石混合体的变形行为

为探讨荷载作用下土石混合体的变形行为,通过开展土石混合体的无侧限抗压试验,结合数字图像相关方法(DIC技术)分析不同含石量试样单轴压缩过程中位移、应变(裂缝扩展)演化规律。结果表明:①随着加载的进行,试样水平方向位移较垂直方向更加明显,故变形能力在垂直方向较水平方向更强,抗压性能优于抗剪性能;随着含石量的增大,试样竖向承载能力持续增强;②随着含石量增加,土石比例对试样裂缝损伤发展的抑制作用强于促进作用,导致承载能力随含石量增加而增强;③试样整体在载荷作用下位移变化阶段主要为初始压实、弹性压缩、弹塑性(塑性膨胀)和破坏阶段;在峰值荷载水平下,加载板影响区域发生局部压碎、脱落,直接导致试样出现裂缝扩展行为且压实性能发生断裂失效;④试样在压缩过程中裂缝数量随含石量的增加而逐渐增加,且裂缝分布广泛,裂缝的宽度呈现出逐渐增大的趋势,且应变云图可以更好地表现试样变形、破坏特征;⑤DIC技术应用于土石混合体在抗压试验中变形破坏的研究,不仅能够得到试样破坏过程中观测面的三维全场位移和应变,而且能够直观地反映试样表面裂隙的产生、扩展及相互连通的演化过程,也能够确定裂纹的位置、形态及扩展方向等具体信息。

油液多层过滤去固分离及特性分析

针对油-固体系中单层滤网无法同时满足过滤精度高与使用寿命长的问题,采用多层过滤器并对其去固分离特性展开分析。通过耦合流场控制方程、颗粒运动方程、颗粒-壁面碰撞模型方程,建立了适用油-固体系的多层过滤数值分析模型,探究了油-固体系中多层过滤器的去固分离特性,分析了入口流速和滤网层数对流场特性及过滤比(β)的影响。结果表明:多层过滤器可有效过滤孔径与粒径比(k_(d))大于15的固体颗粒;增加滤网层数,d_(5)和d_(20)(β分别为5和20时对应的颗粒粒径)显著降低;增大入口流速,d_(5)和d_(20)变化较小;当β<5时,颗粒粒径的变化对过滤比的影响较小;当β>5时,颗粒粒径的小幅增大可能导致过滤比显著提高。

平滑注意力与谱上采样细化的非等距三维点云模型对应关系计算

为了解决非等距3维点云模型对应关系计算易受模型大尺度形变影响而导致对应失真、准确率低且平滑性差的问题,该文提出一种结合平滑注意力与谱上采样细化的非等距3维点云模型对应关系计算新方法。首先,利用点所在表面的几何特征信息设计平滑注意力机制与平滑感知模块,提高特征对大尺度形变区域非刚性变换的感知能力;其次,将深度函数映射模块与平滑正则化约束相结合,提升函数映射计算结果的平滑性;最后,在谱上采样细化模块中,以多分辨率重建的方式得到最终的逐点映射结果。实验结果表明,与已有算法相比,本算法在FAUST、SCAPE和SMAL数据集上构建的对应关系测地误差最小,处理大尺度形变模型时,能够提升逐点映射的平滑性和全局准确率。

基于车载三维激光扫描的城市道路竣工测量探讨

车载三维激光扫描系统融合了多种传感器和数据源,可以自动、迅速地获取道路的全方位信息。其扫描速度迅捷、数据信息丰富、精确度高、采集过程安全简单,并能节省人力。此技术显著提高了外业生产效率,并降低了生产成本。对车载三维激光扫描技术在道路工程竣工测量中的内外业处理流程的研究结果表明:该技术的精度可达到1∶500测图精度要求,满足城市高架路竣工规划测绘的精度需求。该技术方案是切实可行的,且能高效地提高生产效率。

基于深度学习的移动机器人语义SLAM方法研究

为了给移动机器人提供细节丰富的三维语义地图,支撑机器人的精准定位,本文提出一种结合RGB-D信息与深度学习结果的机器人语义同步定位与建图方法。改进了ORB-SLAM2算法的框架,提出一种可以构建稠密点云地图的视觉同步定位与建图系统;将深度学习的目标检测算法YOLO v5与视觉同步定位与建图系统融合,反映射为三维点云语义标签,结合点云分割完成数据关联和物体模型更新,并用八叉树的地图形式存储地图信息;基于移动机器人平台,在实验室环境下开展移动机器人三维语义同步定位与建图实验,实验结果验证了本文语义同步定位与建图算法的语义信息映射、点云分割与语义信息匹配以及三维语义地图构建的有效性。

基于云平台的新能源汽车通信网络定位技术

现行技术在新能源汽车通信网络定位中的应用效果不佳,定位偏移率较高,因此提出基于云平台的新能源汽车通信网络定位技术。利用云平台传输和存储新能源汽车全球定位系统(Global Positioning System,GPS)数据,通过地图匹配新能源汽车定位,采用差分定位算法确定新能源汽车位置,并通过地图可视化展示定位结果,以此实现基于云平台的新能源汽车通信网络定位。实验结果表明,所设计的技术定位偏移率在1%以内,在新能源汽车发展中具有较好的通信网络定位效果。

激光雷达点云特征线检测与结构化研究进展与展望

实景三维(3D)建设是国家新型基础测绘建设的重要组成部分,是对在3D地理场景上承载结构化、语义化、支持人机兼容理解和物联实时感知的地理实体进行构建。而结构化是实现地理实体单体分割并获取其几何轮廓及组成结构的过程。随着大范围、城市级激光点云数据采集成本逐渐降低,3D点云的应用也越来越广泛。基于3D点云能完成生成地物的结构化表达,涉及线、面、体重建及语义分割、实例分割等。点云数据处理的核心内容是点云特征线检测、特征线线段化及特征线结构化。因此,点云特征线对点云处理和行业应用具有理论和实践价值。进行城市场景点云自适应特征线检测和精细结构化研究具有重要意义;研究点云特征线智能感知理论及结构化表达方法能为点云数据处理提供新的视角,对各类工程实践具有重要的理论指导意义,可服务于实景3D建设、二维(2D)、3D高清地图生成等具体应用。本文从点云特征线检测、点云特征线线段化、点云特征线结构化角度进行国内外研究现状综述,总结归纳点云特征线检测与结构化研究存在问题,并明确下一步研究思路。

三维激光扫描技术在历史建筑测绘中的应用——以闽清县历史建筑测绘建档项目为例

历史建筑作为城市的文脉,承载着一座城市的历史,受各种因素影响遭受不断的侵蚀甚至灭失,其保护形势越来越严峻。文章以闽清县历史建筑保护测绘为例,采用三维激光扫描技术,结合无人机倾斜摄影技术,对历史建筑真彩色三维点云模型建设进行了探索研究,为历史建筑的测绘资料建档和文物保护工作积累了宝贵的技术经验。结语对该技术进行了总结分析,认为三维激光扫描技术不仅能大幅提升工作效率和测量成果精度,还可实现历史建筑三维可视化,具有广阔的应用前景。

基于Map-Reduce的海量数据高效Skyline查询处理

Skyline查询已成为现今数据库和信息检索领域的研究热点之一,伴随着人类可以采集和利用的数据信息的急剧增长,使得如何处理海量数据的Skyline查询成为急需解决的问题.近年来兴起的Map-Reduce编程框架能够有效地处理基于海量数据的应用,该文既是研究如何运用Map-Reduce编程框架解决海量数据的Skyline查询问题.在Map-Reduce框架下处理Skyline查询的直接方法是扫描整个数据集进而得到查询结果,但是在海量数据Skyline查询问题中,查询结果的数量远小于原始数据集的数据量,对此该文提出了一系列的Skyline查询算法及优化,有效地过滤掉部分不能成为Skyline查询结果的数据对象,大幅度提高了在Map-Reduce框架下处理Skyline查询的效率.大量运行在Hadoop平台上的实验验证了该文所提出的Skyline查询处理算法具有良好的有效性、准确性和可用性.

基于移动机器人的树木识别与冠层信息测量

针对人工测量苗圃冠层参数费时费力,无法快速提取果树冠层参数的问题,提出一种基于环境点云识别算法的树木冠层信息提取方法。首先利用LiDAR-IMU紧耦合里程计进行点云矫正和特征点提取,在建图中使用旋转约束解决Z轴偏移问题,完成测量区域的环境重建;将点云地图传输至后台工作站后,使用欧式聚类和3D-FV-DNNs算法对树木点云进行分割与识别;最后在找到第一主枝后利用立方体素法实现冠层体积建模,采用二维栅格法提取冠层面积参数。试验表明:本文采用的建图算法能较高精度地重建完整果园环境,基于DNN深度学习分类器的苗圃识别方法获取的P-R曲线的Bet值比SVM与RF分类器所获取的数值高出0.064 1与0.099 9,此外树冠体积与面积的R^(2)与RMSE分别为0.746 77、0.697 8以及0.097 54、0.076 77。表明本文算法测得的冠层参数与人工测量值有强相关性,为果园精细化管理提供重要支撑。