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.

Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.

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.

Analysis of feasibility of application of Beishu point acupressure therapy in treating gastroesophageal reflux disease via AMPK / ULK1 mediated autophagy pathway based on "adjusting central axis via pivot" theory

Gastroesophageal reflux disease(GERD)is a digestive system disease characterized by uncomfortable symptoms caused by reflux of gastric contents.It has increased sharply with the development of my country’s society and economy.If there is no reasonable and effective Prevention and treatment measures will inevitably increase the financial burden of patients,and also pose a major threat to the quality of life and health of patients.Cell signal transduction mediated by various receptors participates in the regulation mechanism of the body's various levels of biological functions.By inhibiting or activating its functions,the purpose of curing diseases can be achieved,and cell signal transduction has been used in traditional Chinese medicine.Studying.The theory of"adjusting the central axis"was explored by Professor Xie Sheng through decades of clinical experience.It has been proven in practice to treat GERD.It starts from the model of TCM viscera and expounds that the pathogenesis of GERD involves multiple viscera.Multi-system and multi-factor,explain the correlation of the disease with a variety of zang-fu syndromes,and use this as a basis to guide the clinical use of hidden prescriptions.The back-shu pointer therapy can prevent GERD by correcting the unbalanced state of the viscera and qi machine,and promoting the junction of the two channels of Ren and Du.Based on the theory of"adjusting the hub by the pivot",this article expounds the pathogenesis of GERD from the perspective of traditional Chinese medicine.By consulting the literature and combining with the previous research,it proposes to analyze the methods and methods of Backshu pointer therapy to prevent and treat GERD from the AMPK/ULK1 mediated autophagy pathway.

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

A scheme for an automatic road surface modeling from a noisy point cloud is presented. The normal vectors of the point cloud are estimated by distance-weighted fitting of local plane. Then, an automatic recognition of the road surface from noise is performed based on the fuzzy clustering of normal vectors, with which the mean value is calculated and the projecting plane of point cloud is created to obtain the geometric model accordingly. Based on fuzzy clustering of the intensity attributed to each point, different objects on the road surface are assigned different colors for representing abundant appearances. This unsupervised method is demonstrated in the experiment and shows great effectiveness in reconstructing and rendering better road surface.

SINGULAR INTEGRAL EQUATIONS ON THE REAL AXIS WITH SOLUTIONS HAVING SINGULARITIES OF HIGHER ORDER

We transform the singular integral equations with solutions simultaneously having singularities of higher order at infinite point and at several finite points on the real axis into ones along a closed contour with solutions having singularities of higher order, and for the former obtain the extended Neother theorem of complete equation as well as the solutions and the solvable conditions of characteristic equation from the latter. The conclusions drawn by this article contain special cases discussed before.

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.

3D face registration based on principal axis analysis and labeled regions orientation

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed.The pre-registration is achieved by transforming the multi-pose models to the standard frontal model＇s reference frame using the principal axis analysis algorithm.Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics.These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration.Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.

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.

Recognition Method of Aircraft Axis Direction Based on Morphological Skeleton and Hough Transform

Because of the limit of angle of view(AOV) of IR imaging seeker during the approach of missile and target, the detector can only get the partial image sequence of aircraft nose after "lose point". Recognizing the axis direction on the basis of partial IR image sequence is a key issue of the advanced IR imaging guide air-to-air missile faced. In this paper, a recognition method was proposed based on the morphological skeleton and modified Hough transform, and this method can recognize correctly the axis direction of aircraft nose in different poses during missile-target encounter. Firstly, the morphological skeleton transform was used for the extraction of skeleton features. Secondly, the modified Hough transform was used for the straight-lines detection. Finally, According to the relations between aircraft nose and axis and invariant of nose features in high-speed IR image sequence, the axis direction can be detected and calculated. Experimental results indicate that the method is feasible and effective, and the precision of axis direction recognized can meet the requirement of accurate burst control of GIF fuze.

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。

基于点-轴理论的东北亚旅游空间结构优化研究

东北亚涵盖朝鲜、韩国、日本、蒙古国4个国家全境和中国的东北3省与内蒙古自治区及俄罗斯的远东与西伯利亚2个联邦管理区,是世界经济的重要板块。本文根据点—轴理论从旅游资源、城市布局、自然地理环境、人口规模、交通线路与地域关系等角度探析了东北亚旅游空间结构的优化布局。结果显示:东北亚旅游空间结构优化形成“点—线—面”的布局,构建9核26心78点的三级旅游节点与4纵2横的旅游发展主轴线以及6大旅游发展组团的空间布局;东北亚旅游空间结构优化的轴线分为主轴线、辅助线和外联轴线,其中主轴线包括4纵2横;东北亚旅游空间可进一步划分为6大组团;东北亚旅游空间结构优化有利于区域经济健康持续发展。

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。

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。

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。

基于三维激光点云的序列运动图像点目标快速跟踪方法

由于现有方法未充分考虑小目标和遮挡目标检测的问题,导致目标跟踪效果不佳,由此针对点目标跟踪较为困难的问题,提出基于三维激光点云的序列运动图像点目标快速跟踪方法。首先下采样序列运动图像三维激光点云并去除其中的地面数据,通过动态阈值改进欧式聚类方法,分割图像为目标和背景,然后改进SECOND算法,在目标检测阶段融入自适应空间特征融合模块和二维卷积神经网络,同时采用3D DIoU替代Smooth L1作为损失函数,提升SECOND算法的目标检测性能,最后在激光雷达坐标系中构建各点目标的三维中心,利用3D卡尔曼滤波器连续跟踪目标,以交并比和欧式距离为度量标准,通过贪婪算法匹配最近邻目标,实现序列运动图像点目标快速跟踪。实验结果表明,所提方法IoU值更高,可达到0.971,且对目标的跟踪更理想。

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。

倾斜隧道贴壁火灾顶棚烟气最大温升模拟研究

目的 研究在火灾浮力效应、壁面辐射热反馈及空气卷吸限制效应的协同作用下倾斜隧道贴壁火灾发展的特性。方法 利用FDS软件对倾斜隧道贴壁火进行数值模拟,分析顶棚烟气最大温升及其位置;考虑火源中心与侧壁的距离、坡度,建立了无量纲预测模型,进行拟合分析。结果 当贴壁火热释放速率分别为2.7、4.0和5.0 MW时,隧道贴壁火的顶棚最大温升分别在坡度为2%、1%、0时达到最大值,之后随着坡度的增大呈指数衰减;与轴线火相比,当坡度小于10%时,贴壁火的最大温升点与火源中心更近,坡度大于10%时,贴壁火的最大温升点位置更远;贴壁火和轴线火的拟合结果很好,相关系数分别为0.98和0.95。结论 侧壁限制对最大温升的影响随着火源中心与侧壁距离的缩小而更加显著;随着隧道坡度增大,倾斜隧道贴壁火的火焰同时向火灾上游和侧壁倾斜,空气卷吸和对流换热作用不断增强,从而影响火焰的受力机制和火源表面接受到的辐射热反馈,顶棚最大温升随坡度增加呈指数衰减。