Aggregate Point Cloud Geometric Features for Processing

As 3D acquisition technology develops and 3D sensors become increasingly affordable,large quantities of 3D point cloud data are emerging.How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved.The point cloud geometric information is hidden in disordered,unstructured points,making point cloud analysis a very challenging problem.To address this problem,we propose a novel network framework,called Tree Graph Network(TGNet),which can sample,group,and aggregate local geometric features.Specifically,we construct a Tree Graph by explicit rules,which consists of curves extending in all directions in point cloud feature space,and then aggregate the features of the graph through a cross-attention mechanism.In this way,we incorporate more point cloud geometric structure information into the representation of local geometric features,which makes our network perform better.Our model performs well on several basic point clouds processing tasks such as classification,segmentation,and normal estimation,demonstrating the effectiveness and superiority of our network.Furthermore,we provide ablation experiments and visualizations to better understand our network.

Point Cloud Processing Methods for 3D Point Cloud Detection Tasks

Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).However,the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction.Overcoming limitations is critical for 3D point cloud processing.3D point cloud object detection is a very challenging and crucial task,in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance.In this overview of outstanding work in object detection from the 3D point cloud,we specifically focus on summarizing methods employed in 3D point cloud processing.We introduce the way point clouds are processed in classical 3D object detection algorithms,and their improvements to solve the problems existing in point cloud processing.Different voxelization methods and point cloud sampling strategies will influence the extracted features,thereby impacting the final detection performance.

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Joint asymptotic distribution of exceedances point process and partial sum of stationary Gaussian sequence

Let {Xi}i=1^∞ be a standardized stationary Gaussian sequence with covariance function τ（n） =EX1Xn＋1, Sn =∑i=1^nXi,and X^-n=Sn/n.And let Nn be the point process formed by the exceedances of random level （x/√2 log n＋√2 log n-log（4π log n）/2√log n） √1-τ（n） ＋ X^-n by X1,X2,…, Xn. Under some mild conditions, Nn and Sn are asymptotically independent, and Nn converges weakly to a Poisson process on （0,1].

Energy Efficiency Optimization for Heterogeneous Cellular Networks Modeled by Matérn Hard-Core Point Process

The Poisson point process(PPP) has been widely used in wireless network modeling and performance analysis due to the independence between its nodes. Therefore, it may not be a suitable model for many of the exclusive networks between the nodes. This paper analyzes the energy efficiency(EE) and optimizes the two-tier heterogeneous cellular networks(Het Nets). Considering the mutual exclusion between macro base stations(MBSs) distribution, the deployment of MBSs is modeled by the Matérn hard-core point process(MHCPP), and the deployment of pico base stations(PBSs) is modeled by the PPP. We adopt a simple approximation method to study the signal to interference ratio(SIR) distribution in two-tier MHCPP-PPP networks and then derive the coverage probabilities, the average data rates and the energy efficiency of Het Nets. Finally, an optimization algorithm is proposed to improve the EE of Het Nets by controlling the transmit power of PBSs. The simulation results show that the EE of a system can be effectively improved by selecting the appropriate transmit power for the PBSs. In addition, two-tier MHCPP-PPP Het Nets have higher energy efficiency than two-tier PPP-PPP Het Nets.

Reliability Analysis of Repairable Systems Using Stochastic Point Processes

In order to analyze the failure data from repairable systems, the homogeneous Poisson process (HPP) is usually used. In general, HPP cannot be applied to analyze the entire life cycle of a complex, re-pairable system because the rate of occurrence of failures (ROCOF) of the system changes over time rather than remains stable. However, from a practical point of view, it is always preferred to apply the simplest method to address problems and to obtain useful practical results. Therefore, we attempted to use the HPP model to analyze the failure data from real repairable systems. A graphic method and the Laplace test were also used in the analysis. Results of numerical applications show that the HPP model may be a useful tool for the entire life cycle of repairable systems.

The SCUSUM Estimation of Variance Change Point in Linear Process

这份报纸在线性过程考虑一个变化变化点的评价问题。一个 SCUSUM 类型变化点评估者的一致性被证明，它集中的率被建立。平均数未知的盒子也被考虑。

Model for Failure Point Process of a Repairable System and Application

A engineering system is usually repairable, and failure process of a repairable by a failure point process. The power law model is a commonly used approach to model syst the em is often described failure point process. This paper introduces the concept and model for the failure process of repairable system. The method of parameter estimation is developed, and failure observations are fitted into a power-law model by using the least square method. Two applications of the pressent model are discussed according to the practical failure data of the central cooling system of a nuclear power plant. One application is determining the optimal overhaul time, and the other is evaluating the quality of maintenance. This paper provides references for the overhaul decision making and maintenance quality evaluation in reality.

RATE OF CONVERGENCE FOR MULTIPLE CHANGEPOINTS ESTIMATION OF MOVING-AVERAGE PROCESSES

In this paper, the least square estimator in the problem of multiple change points estimation is studied. Here, the moving-average processes of ALNQD sequence in the mean shifts are discussed. When the number of change points is known, the rate of convergence of change-points estimation is derived. The result is also true for p-mixing, φ-mixing, a-mixing, associated and negatively associated sequences under suitable conditions.

Standby Redundancy Allocation for a Coherent System under Its Signature Point Process Representation

Willing to work in reliability theory in a general set up, under stochastically dependence conditions, we intend to characterize a not identically spare standby redundancy operation through compensator transform under a complete information level, the physic approach, that is, observing its component lifetime. We intend to optimize system reliability under standby redundancy allocation of its components, particularly, under minimal standby redundancy. To get results, we will use a coherent system representation through a signature point process.

Computer Processing of the Intersection of Two Cones with Their Axes Intersecting and a Method to Get the Special Points

The analytic expression of the special points on the intersection of two cones with their axes intersecting(ITCTAI) is given. It also presents a method to construct the special points graphically according to the analytic expression of them. Finally, with computer programming language, it gives a program to generate the intersection in several different cases.

New Approach in Processing of the Infrared Image Sequence for Moving Dim Point Targets Detection

The development of an efficient moving target detection algorithm in IR-image sequence is considered one of the most critical research fields in modern IRST (Infrared Search and Track) systems, especially when dealing with moving dim point targets. In this paper we propose a new approach in processing of the Infrared image sequence for moving dim point targets detection built on the transformation of the IR-image sequence into 4-vectors for each frame in the sequence. The results of testing the proposed approach on a set of frames having a simple single pixel target performing a different motion patterns show the validity of the approach for detecting the motion, with simplicity in calculation and low time consumption.

Pre-process algorithm for satellite laser ranging data based on curve recognition from points cloud

The satellite laser ranging （SLR） data quality from the COMPASS was analyzed, and the difference between curve recognition in computer vision and pre-process of SLR data finally proposed a new algorithm for SLR was discussed data based on curve recognition from points cloud is proposed. The results obtained by the new algorithm are 85 % （or even higher） consistent with that of the screen displaying method, furthermore, the new method can process SLR data automatically, which makes it possible to be used in the development of the COMPASS navigation system.

基于激光雷达扫描的料仓原料储料量测量方法

为提高仓内原料储料量测量自动化和智能化水平,设计了一种基于二维激光雷达扫描的储料量测量装置与系统。本系统采用RPLIDARS1型二维激光雷达扫描获取不同储料量物料的原始点云数据,通过坐标变换、重叠点提取、滤波、分割等方法对原始点云进行预处理,采用贪婪投影三角化算法将预处理后的点云进行三维重建,获得仓内原料的三维模型,结合物料三维模型和物料的容重获得仓内原料的储料量,从而实现储料量的自动测量。以玉米为试验对象,测量小型储料塔内玉米储料量并对玉米不同储料量进行扫描测量,验证模型的准确性,结果显示:测量结果的平均绝对误差为8.05kg,平均相对误差为1.52%。研究结果表明,基于二维激光雷达扫描的储料量测量方法是可行的,具有较好的稳定性和测量精度,能够满足实际生产需求。

基于场景点云重建的移动焊接机器人作业轨迹提取方法

为保证移动作业焊接机器人能够在建筑、石化、船舶等非结构化场景中精准高效的完成作业,提出一种基于点云重建的焊接轨迹提取方法。焊接机器人变换作业场景后,首先通过深度相机实时检测引导标志物,明确作业范围和起点并重建场景点云。然后基于点云分割和边界提取算法获取焊缝点云轨迹,并提出一种焊接机器人位姿调整策略。最后,针对点云精度不足的问题,采用激光视觉传感器对焊缝进行精度修正,提取精确焊接轨迹。通过曲线焊缝场景实验,表明机器人运动平滑,焊缝轨迹提取误差小于0.5 mm,精度满足生产要求。

基于线结构光扫描的工件高精度三维测量方法

为了提高工业中对复杂轮廓工件的测量精度和效率,设计了一套高精度非接触三维测量系统,并提出了一种基于线结构光扫描的工件轮廓三维测量方法。首先,利用高精度相机和三轴移动平台采集线结构光图像。然后,通过基于差分区间的灰度质心算法,精确而高效地提取出线结构光中心线,并生成原始点云模型。接着,对采集到的点云数据进行必要的点云滤波和精简预处理。最后,将预处理后的点云数据与CAD模型精确配准,进行工件表面轮廓的测量与误差评定。实验结果表明:测量工件轮廓高度的绝对误差小于0.07 mm,相对误差小于0.5%。所提出的三维测量系统及方法测量误差较低,能够实现工件的高精度三维测量,具有一定的工业应用价值。

水利工程质量监督全过程全方位定量评价模型构建

为促进我国水利工程质量监督管理工作运行的高效化,确保工程监督的有效性和权威性,基于AHP(analytic hierarchy process)原理,针对水利部督查小组的现场检查情况,依据水利部下达的相关文件,结合负分制和一票否决制,将定性问题定量化,构建一套涉及水利工程建设、运行、管理全过程全方位的质量监督管理评价模型,共包含7层2112个指标。在综合试错的基础上,计算得出不同层级和不同指标的权重,然后选用该模型分析评价2020年7个省份受督查水利工程项目的质量管理与安全生产管理监督数据,间接说明模型的实用性和易操作性。评价结果表明:7个省份均在合格以上,整体质量管理情况较好,但得分差异显著,其中最高得分90.6、最低得分65.6,相差25分;不同类型管理评价结果显示,安全生产管理得分普遍高于质量管理得分;各参建单位评价结果显示,建设单位和勘察设计单位的情况最优、评分基本达到满分,而质量检测单位和施工单位得分最低、需重点加强相关监管。评价结果为水利工程监督管理工作提供了科学的数据支撑,构建的评价模型也为水利工程质量监督定量化提供了易实操的普适性工具。

中深层煤炭地下气化的气化腔安全宽度计算方法

煤炭地下气化是目前温度最高(超过1200℃)的化石能源非常规开发方式,中深层(本文指埋深800~1500 m)煤炭地下气化在提高气化压力、降低地质安全风险方面优势明显,科学预测气化腔安全宽度对保障气化稳定运行十分重要,由于目前基于可控注入点后退(CRIP)工艺的气化腔安全宽度计算方法尚未建立,为保证现场试验顺利实施,需要开展针对性研究。气化腔顶板“裸露”在气化腔后会受到高温影响,通过数值模拟方法研究了压应力约束条件下岩石内部热应力产生位置以及颗粒、基质热膨胀系数差异对热应力大小的影响规律,结合高温处理后的岩石电镜扫描结果,查明了高温下岩石热损伤机理。根据CRIP气化工艺造腔特点,建立了考虑高温影响的气化腔顶板薄板模型,结合“关键层”理论提出了气化腔安全宽度计算方法。研究表明:岩石热损伤是岩石物理化学反应与热应力互相促进、共同作用的结果,高温下岩石发生不规则变形,岩石热损伤引起的微观结构变化是导致岩石力学性质、物理性质变化的根本原因。岩石的最大拉张热应力出现在颗粒界面或热膨胀系数较小的颗粒中,颗粒与基质热膨胀系数比值在[0.01~1)时,最大拉张热应力随颗粒热膨胀系数减小而快速增加。泥岩加热到200℃时开始出现微裂隙;加热到400℃时裂隙发育更加明显,主要是沿颗粒边缘破裂;加热到600~800℃时,裂隙数量增多、尺寸变大;加热到1000℃时,除出现较大裂隙外,还产生了大量孔隙;1200℃时裂隙连通性明显增加,气孔发育较大。由于高温的影响,薄板模型的步距准数不再是定值,需要根据气化腔顶板热破坏范围与顶板硬岩层的空间位置关系确定具体数值。气化腔安全宽度受温度影响,在研究算例中,砂岩顶板在35、1000℃时安全宽度计算结果分别为34.3 m和14.1 m,相差达58.9%,泥岩顶板在35、1000℃时安全宽度计算结果分别为16.7 m和15.9 m,相差4.8%。最后从降低顶板垮落风险、有利于气化控制角度,提出了煤层纵向靶区位置的确定方法,当煤层厚度超过气化腔安全宽度一半时,建议将水平井纵向靶区设计在距离煤顶不超过气化腔安全宽度一半的位置。

基于GA-BP对叶片点云数据修补及逆向建模

为了获得某型号发动机叶片的三维建模及其相关数据,给逆向设计提供一些零件的三维建模尺寸。由于获得建模的三维点云数据时必然会产生孔洞,文中使用基于Genetic Algorithm(基因遗传算法)优化的Back Propagation(反向传播)神经网络(又称GA-BP神经网络)作为一个回归预测算法,来对产生的散乱点云孔洞加以修复和点云处理,最后再通过Geomagic Wrap对某型号发动机叶片进行逆向建模。通过对比BP神经网络可知,GA-BP修补孔洞的误差明显降低,满足对三维模型精度较高的要求,可应用于逆向工程。

基于卡尔曼滤波的激光雷达点云跟踪方法研究

激光雷达点云目标跟踪是环境感知与定位的重要研究内容,具有广泛的应用前景。由于激光雷达存在探测范围有限、易受遮挡和散射条件影响等问题,无法保证跟踪过程中目标点云特征的完整性和正确性,跟踪目标丢失时有出现。为了提高跟踪过程中的鲁棒性,提出一种基于卡尔曼滤波的2D激光点云跟踪方法。该方法首先对聚类后的点云建立包围盒集合,其次对包围盒跟踪框建立卡尔曼滤波模型,将前一帧跟踪框最优估计值与当前帧包围盒,通过交并比方法进行关联性匹配,将关联性最大结果作为当前帧目标点云,并更新卡尔曼滤波模型参数,从而实现目标点云的实时跟踪。实验结果表明,所提方法提高了跟踪过程的鲁棒性和准确性。