Slope deformation partitioning and monitoring points optimization based on cluster analysis

The scientific and fair positioning of monitoring locations for surface displacement on slopes is a prerequisite for early warning and forecasting.However,there is no specific provision on how to effectively determine the number and location of monitoring points according to the actual deformation characteristics of the slope.There are still some defects in the layout of monitoring points.To this end,based on displacement data series and spatial location information of surface displacement monitoring points,by combining displacement series correlation and spatial distance influence factors,a spatial deformation correlation calculation model of slope based on clustering analysis was proposed to calculate the correlation between different monitoring points,based on which the deformation area of the slope was divided.The redundant monitoring points in each partition were eliminated based on the partition's outcome,and the overall optimal arrangement of slope monitoring points was then achieved.This method scientifically addresses the issues of slope deformation zoning and data gathering overlap.It not only eliminates human subjectivity from slope deformation zoning but also increases the efficiency and accuracy of slope monitoring.In order to verify the effectiveness of the method,a sand-mudstone interbedded CounterTilt excavation slope in the Chongqing city of China was used as the research object.Twenty-four monitoring points deployed on this slope were monitored for surface displacement for 13 months.The spatial location of the monitoring points was discussed.The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

Cluster segmentation algorithm based on the Vicsek with static summoning points

Because of the low convergence efficiency of the typical Vicsek model,a Vicsek with static summoning points(VSSP)algorithm based on the Vicsek model considering static summoning points is proposed.Firstly,the mathematical model of the individual movement total cost on each summoning point is established.Then the individual classification rule is designed according to the initial state of the cluster to obtain the subclusters guided by each summoning point.Finally,the summoning factor is introduced to modify the course angle updating formula of the Vicsek model.To verify the effectiveness of the proposed algorithm and study the effect of the cluster summoning factor on the convergence rate,three groups of simulation experiments under different summoning factors are designed in this paper.To verify the superiority of the VSSP algorithm,the performance of the VSSP algorithm is compared with the classic algorithm by designing the algorithm performance comparison verification experiment.The results show that the algorithm proposed in this paper has good convergence and course angle consistency.The summoning factor is the sensitive factor of cluster convergence.This algorithm can provide a reference for efficient cluster segmentation movement.

Adding-Point Strategy for Reduced-Order Hypersonic Aerothermodynamics Modeling Based on Fuzzy Clustering

Reduced order models（ROMs） based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

Road Surface Modeling and Representation from Point Cloud Based on Fuzzy Clustering

为从吵闹的点云当模特儿的自动道路表面的一个计划被介绍。点云的正常向量被本地飞机的距离加权的适合估计。然后，从噪音的道路表面的自动识别基于正常向量，平均值与是计算的模糊聚类被执行，点云的投射飞机被创造因此获得几何模型。基于归因各指的紧张的模糊聚类，道路表面上的不同目标为代表丰富的外观被分配不同颜色。这个无指导的方法在重建并且显示更好的道路表面在实验和表演伟人有效性被表明。

Evolution of ion-irradiated point defect concentration by cluster dynamics simulation

The relationship between ions irradiation and the induced microstructures(point defects,dislocations,clusters,etc.)could be better analyzed and explained by simulation.The mean field rate theory and cluster dynamics are used to simulate the effect of implanted Fe on the point defects concentration quantitatively.It is found that the depth distribution of point defect concentration is relatively gentle than that of damage calculated by SRIM software.Specifically,the damage rate and point defect concentration increase by 1.5 times and 0.6 times from depth of 120 nm to 825 nm,respectively.With the consideration of implanted Fe ions,which effectively act as interstitial atoms at the depth of high ion implantation rate,the vacancy concentration Cv decreases significantly after reaching the peak value,while the interstitial atom concentration Ci increases significantly after decline of the previous stage.At the peak depth of ion implantation,Cv dropped by 86%,and Ci increased by 6.2 times.Therefore,the implanted ions should be considered into the point defects concentration under high dose of heavy ion irradiation,which may help predict the concentration distribution of defect clusters,further analyzing the evolution behavior of solute precipitation.

Use of Rough Sets Theory in Point Cluster and River Network Selection

In this paper, we applied the rough sets to the point cluster and river network selection. In order to meet the requirements of rough sets, first, we structuralize and quantify the spatial information of objects by convex hull, triangulated irregular network (TIN), Voronoi diagram, etc.;second, we manually assign decisional attributes to the information table according to conditional attributes. In doing so, the spatial information and attribute information are integrated together to evaluate the importance of points and rivers by rough sets theory. Finally, we select the point cluster and the river network in a progressive manner. The experimental results show that our method is valid and effective. In comparison with previous work, our method has the advantage to adaptively consider the spatial and attribute information at the same time without any a priori knowledge.

基于多线激光雷达的井下斜坡道无人矿卡定位与建图方法

井下斜坡道的定位与建图是实现井下斜坡道无人驾驶的关键技术之一,矿山井下斜坡道区域为典型非结构化环境特征,且道路具有一定倾斜角度,采用传统SLAM算法无法获得精确里程计信息,导致定位与建图精度难以满足无人矿卡行驶需求。针对上述问题,通过研究激光SLAM(Simultaneous Localization And Mapping)算法LeGO-LOAM,笔者提出一种适用于矿山井下斜坡道环境的定位与建图方法。首先,针对井下斜坡道口两侧均为光滑水泥墙壁,特征点稀少问题,设计了基于人工路标的辅助增强定位方法,有效增加点云特征数量,从而优化位姿估计结果,避免建图漂移现象;然后在特征预处理阶段,提出了一种基于激光点云高度差与坡度信息融合的提取地面点高效算法,通过改善地面地点的选取策略,针对倾斜坑洼路面仍能有效识别地面点,解决了井下斜坡道定位与建图倾斜角度大、误差大等问题;其次,基于CVC(Curved-Voxel Clustering)聚类算法设计了一种斜坡道点云曲率体素聚类算法,采用曲率体素和基于哈希的数据结构对点云进行分割,大幅提高在井下稀疏、噪声环境下点云聚类的鲁棒性;最后,运用Scan-To-Map进行点云匹配,同时兼顾点云配准的性能与速度。在中钢集团山东某井下斜坡道的现场实验证明:与原算法相比精度提升13.15%,Z轴误差降低22.3%,地图质量明显提升,能有效解决井下无人驾驶建图及定位的难题。

K-means聚类精简点云驱动PointNet++的行星齿轮故障诊断

复杂装备的三维模型点云数据具有非结构化、无序性、离散性的特点,数据精简策略和深度神经网络模型构建被视为点云数据驱动的机械设备故障诊断关键技术难点。提出了一种K-means聚类(K均值聚类算法)精简点云驱动PointNet++的行星齿轮故障诊断方法。首先,提出了基于K-means的点云数据精简策略实现了在充分保留细节特征的前提下,精简84%的冗余数据;其次,构建了简度、速度、精度的精简效果三维评价指标体系并对精简算法进行评价;最后,构建了能够提取局部特征的PointNet++故障诊断模型。实验结果表明,相比于点云数据直接驱动PointNet++,K-means聚类精简点云驱动PointNet++的行星齿轮故障诊断的准确率提升了6.9%,表明了所提方法的有效性。

应用于食品检测领域的分子即时检测技术研究进展

食品安全问题关系人类民生。为有效预防和控制食品安全风险,减少食品安全经济损失,迫切需要开发出针对食源性病原体、食物掺假、转基因作物的高特异性、高敏感性、快速的核酸检测技术。即时检测(point-of-care testing,POCT)作为一种新兴的快速检测手段,由于其检测速度快、操作简单、现场检查等特点,在食品检测领域应用广泛。本文首先介绍了POCT技术的发展历史,而后根据食品安全事件的特点,从食源性致病菌、食物掺假、转基因作物3个方向分析了食品检测的主要对象,阐述了分子POCT技术在食品检测领域的应用现状,最后对应用于食品检测领域的分子POCT技术存在的问题和解决办法进行了分析总结,对食品检测分子POCT技术的发展方向进行了展望。本文有助于进一步了解分子POCT技术在食品检测中的应用,并为分子POCT技术在食品快速检测中的研究和应用提供参考。

基于反射强度的改进欧式距离聚类钢轨点云分割方法

线路点云数据结构化是深化专业计算与分析的技术前提。点云分割是点云数据结构化的基础。钢轨作为轮轨走行面,其空间位置的连续及平顺情况直接影响行车安全,因此轨道结构分割中,首先需对钢轨进行分割处理。针对传统欧式距离聚类中线路全景点云数据遍历导致距离阈值难统一、难界定,造成分类过多不易查找,或人工选取初始点及调参带来的自动化程度不高的情况,提出基于反射强度的改进欧式距离聚类钢轨点云分割方法。在对轨道结构特性分析的基础上,采用布料滤波算法进行地面滤波,区分地面点与地物点,精简线路点云为轨道结构点云;融合点云反射强度属性,提出提取率概念,确定钢轨高反射强度区间,进行钢轨顶面点云预分割,进而以轨顶面预分割点作为初始点,根据钢轨断面轨头高和轨头宽构造对角线长度来计算距离阈值,由Kd-Tree找到小于轨头距离阈值的点进行欧式距离聚类,实现对轨头凸集点云的分割。多路段钢轨点云分割试验,精确率及召回率均大于90%,说明该方法可行有效。

基于船舶轨迹数据的几何航路网络建模方法

文中提出了一种基于船舶轨迹数据几何航路网络构建方法,通过选取符合条件的船舶轨迹特征点提取航路点区域,结合轨迹聚类与边界提取构建航路点区域的连接方法,建立几何航路网络.结果表明:该方法能够构建航行水域的几何航路网络.

产业集群社会资本能够打破传承期家族企业创新约束吗?——基于战略参考点转移的研究

探究传承期家族企业的创新问题,对正面临代际传承和国家创新驱动发展战略双重挑战的家族企业具有重要的理论和现实意义。基于战略参考点理论,本文提出对于传承期的家族企业而言,产业集群社会资本是驱动其战略参考点转移以及缓解创新约束的关键因素。对产业集群家族企业调查数据进行统计分析的检验结果表明:处于传承期的家族企业以保持家族控制这一内部导向的战略参考点为主导,从而抑制其创新活动;随着产业集群社会资本的提升,传承期家族企业战略参考点会“由内转外”,具有更强的外部导向,因此能够缓解传承期对家族企业创新的抑制。拓展研究发现,产业集群社会资本通过提升传承期家族企业风险承担能力促进其创新行为。无论是以创新投入还是以新产品创新作为家族企业创新行为的反映,以上结论均得到支持。研究揭示了产业集群社会资本对传承期家族企业战略参考点变化的重要作用,有助于引导和鼓励家族企业提高传承期的创新投入,最终实现“基业长青”。

城市休闲产业聚类模式APM算法模型开发与校验

城市休闲相关产业的高质量发展对当前我国城市消费升级以及人居环境质量提升具有重要现实意义。但是,现有研究未能精准地捕捉海量广域分布的城市休闲产业的基本空间分布规律与结构,而已有的空间聚类算法较多适用于城市用地分析,未能很好地适用于离散分布的城市休闲产业研究。为此,文章基于空间兴趣点数据,开发距离通达值及空间集群中心点等算法,构建城市休闲旅游产业聚类模式空间算法模型(APM)。在以广州为例的研究中,APM模型捕捉出3170个以500 m步行生活圈为范围的城市休闲产业集群,校验了APM模型的科学性与应用价值。整体上,APM算法可以较好地捕捉城市休闲业态集群的空间结构,清晰识别城市休闲产业空间冷、热点分布的基本结构,由其捕捉行程的聚类边界与实际道路和建筑走向、水系边界、区域范围等重合度高,聚类集群符合实际情况,具备可信度与有效性。该研究是休闲产业集聚机制研究的一次方法创新,在算法精度、实际应用、可视化效率上均做出了创新性推进。与Fishnet方法相比,可以更科学精准地识别城市内部多个休闲消费商圈的边界,实现了高效率的城市休闲产业集群捕捉;与同位模型相比,可以呈现多类别的城市休闲业态结构,突破了现有研究只能捕捉两类业态组团的局限。

结合载客热点和POI的出租车停车位划定方法

针对出租车随意停靠造成城市交通拥堵甚至交通事故的问题,利用成都实际区域的出租车GPS(Global Position System)数据和爬取的POI(Point of Interest)数据,使用DBSCAN(Density-Based Spatial Clustering of Application with Noise)聚类算法对上下客点进行聚类,得到出租车的载客热点,根据POI的类型划定载客热点区域的类型,对出租车不同时间的出行需求进行分析,进而划分出出租车的固定停车区域。研究结果表明,出租车固定停车区域的设定与出行者的出行需求有关,即将固定停车区域设置在出行者出行需求多的区域,可以满足出行者的不同出行需求。结合出租车载客热点和爬取POI数据划定固定停车区域的方法具有较高的实用性,可为城市交通安全方面提供理论和现实意义。

外卖配送电动自行车换电柜选址定容实证研究

针对外卖配送电动自行车换电柜布局不合理带来的部分换电柜利用率低与部分换电需求得不到及时满足的供需矛盾问题,本文通过聚类POI(Point of Interest)数据确定外卖配送起止点,并通过仿真模拟外卖骑手配送路径预测外卖配送电动自行车换电需求时空分布,构建换电柜运营商总成本最低和用户满意度最高的多目标换电柜选址定容模型,并以新乡市主城区为例,采用NSGA-II(Non-dominated Sorting Genetic Algorithm II)算法得到换电柜选址定容方案。研究结果表明:仿真模拟得出的换电需求时间分布预测值与实际值基本吻合,换电需求在11:00,14:00,17:00和20:00左右急剧增长,且11:00和14:00左右的换电需求量显著高于17:00和20:00左右的换电需求量,外卖骑手配送路径仿真模拟方法在换电需求预测上具有较高的预测精度;换电柜选址方案不能同时满足运营商和用户利益均为最优,用户满意度的提高需以增加运营商总成本为代价;同时,兼顾运营商和用户利益的新乡市主城区外卖配送电动自行车换电柜最佳建设数量为26,其中,容量为11的换电柜11个,容量为22的换电柜8个,容量为33的换电柜7个;新乡市主城区应按照备选点编号15-7-19-17依次新增换电柜至30个,此时,用户满意度最大,若继续增加换电柜建设数量,只会增加运营商总成本。

基于视觉图像与激光点云融合的交通标志快速识别方法

交通标志对车辆交通起到重要作用和意义,而智能交通中交通标志识别由于标志特征提取效果差,导致识别率低、识别时间长,因此,提出一种新的基于视觉图像与激光点云融合的交通标志快速识别方法。采用双边滤波方法预处理原始激光点云数据;通过归一化处理得到视觉图像激光点云融合的目标空间激光点云位置测距数值;通过测距值获取目标图像位置,归一化处理交通标志视觉图像,引入k均值聚类算法二聚类处理图像,采用制作的切割模板切割图像感兴趣区域,提取交通标志图像的深度特征,结合卷积神经网络二次过滤特征,重新标定二次过滤后的特征,最终利用卷积神经网络模型实现交通标志快速识别;经实验对比证明,采用所提方法提取各个类型交通标志特征的提取效果较好,并且识别率达到89.74%,识别时间仅为13.1 s,干扰下识别时间最高仅为15.1 s,验证了该方法可以快速且准确识别各个类型的交通标志。

基于图神经网络聚类的土壤监测点位优化

在土壤污染的监测过程中,由于需要大面积的监测点位布设,所以需要大量的人力和物力,同时原有的土壤监测点位存在冗余和代表率低的问题,对监测结果的准确性和全面性产生了影响。使用一种基于自编码器降维和图卷积网络(GCN)推理的土壤点位优化方法,通过将原有监测点位聚类成不同簇,来实现用尽量少的监测点位全面地表现区域污染情况的目的。使用GCN推理结合自编码器降维的土壤点位优化方法,该方法同时利用土壤监测点位数据的结构信息和高维特征表示,将监测点位聚类成不同簇。通过对相关性进行分析,发现聚类后的簇中心点位可以作为原始簇的代表,从而降低土壤监测点位的冗余性,实现了监测点位的优化。实验证明GCN方法可以有效地减少监测点位的数量,同时保持监测结果的准确性和全面性。

地图综合图卷积神经网络点群简化方法

地图综合是一个多因素影响的复杂决策过程,针对不同场景判断下的综合算子优化选择常规上通过基于规则的方法实施。顾及不同特殊条件影响,这些地图综合规则需要“打补丁”,导致地图综合规则体系越来越复杂,从而失去普适性。人工智能技术下的数据驱动综合方案通过机器学习提取典型案例中隐含的综合规则,并迁移到新的数据场景,为处理特殊规则下的地图综合提供了一种思路。本文引入深度学习技术,采用领域知识与数据驱动相结合的策略,提出了一种基于图卷积神经网络的点群自动综合方法。本文方法通过样本训练与深度学习获取不同数据场景下的地图综合知识,同时融入既有规则进行引导,可以更有效地向人工地图综合结果的目标迈进。首先,构建Delaunay三角网,在点群之间建立空间邻域关系,并根据地理空间上下文关联、空间异质性等领域知识计算各个点的特征信息,构造点群的特征向量。其次,引入拓扑自适应图卷积神经网络,构建点群数据自动综合网络模型。试验表明本文方法在局部区域与整体地图上均可以保持原始点群的各项特征,体现在相对数量保持、上下文特征继承、属性特征一致方面均有良好的表达效果。

面向弱纹理空间目标的特征点匹配方法

特征点提取与匹配是遥感图像处理中关键的一环,目前成熟的算法大多面向对地成像类型的遥感图像,对于空间目标的遥感图像,没有考虑成像条件与探测平台的影响因素,特征点匹配质量较差。针对空间目标的匹配精度不高这一问题,文章提出了一种基于聚类的特征点匹配算法。首先,根据空间目标的重复弱纹理进行特征点提取与描述,再利用特征点的空间位置进行聚类,并对特征点簇进行匹配;之后将特征点的主方向减去目标整体方向,利用特征点主方向对每一个点簇进行再分组,并完成特征点匹配;最后利用最近邻次近邻比率方法和随机样本一致算法(RANSAC)剔除外点。采用该特征点匹配方法进行的模拟成像数据实验结果表明,对于空间目标图像,基于聚类的特征点匹配较直接匹配,匹配数量的提升最高可达50%,重投影误差优于1/4个像元。文章提出的这一方法使用目前通用的各种特征描述子,能够大幅度提高空间目标图像特征点匹配的数量与精度。