A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

基于改进PointNet++的输电线路关键部位点云语义分割研究

输电线路的关键部位包括塔身、导线、绝缘子、避雷线以及引流线,无人机精细化导航的首要任务是构造输电线路的点云地图并从中分割出上述部位。为解决现有算法在输电线路的绝缘子、引流线等精细结构分割时精度低的问题,通过改进PointNet++算法,提出了一种面向输电线路精细结构的点云分割方法。首先,基于无人机机载激光雷达在现场采集的点云数据,构造了输电线路点云分割数据集;其次,通过对比实验,筛选出在本输电线路场景下合理的数据增强方法,并对数据集进行了数据增强;最后,将自注意力机制以及倒置残差结构和PointNet++相结合,设计了输电线路关键部位点云语义分割算法。实验结果表明:该改进PointNet++算法在全场景输电线路现场点云数据作为输入的前提下,首次实现了对引流线、绝缘子等输电线路中精细结构和导线、杆塔塔身以及输电线路无关背景点的同时分割,平均交并比(mean intersection over union,mIoU)达80.79%,所有类别分割的平均F_(1)值(F1 score)达88.99%。

Indoor Space Modeling and Parametric Component Construction Based on 3D Laser Point Cloud Data

In order to enhance modeling efficiency and accuracy,we utilized 3D laser point cloud data for indoor space modeling.Point cloud data was obtained with a 3D laser scanner and optimized with Autodesk Recap and Revit software to extract geometric information about the indoor environment.Furthermore,we proposed a method for constructing indoor elements based on parametric components.The research outcomes of this paper will offer new methods and tools for indoor space modeling and design.The approach of indoor space modeling based on 3D laser point cloud data and parametric component construction can enhance modeling efficiency and accuracy,providing architects,interior designers,and decorators with a better working platform and design reference.

Determination of Curie Point Depth and Heat Flow Using Airborne Magnetic Data over the Kom-Ombo and Nuqra Basins, Southern Eastern Desert, Egypt

The Kom-Ombo and Nuqra basins in southern Egypt have recently been discovered as potential hydrocarbon basins. The lack of information about the geothermal gradient and heat flow in the study area gives importance to studying the heat flow and the geothermal gradient. Several studies were carried out to investigate the geothermal analyses of the northwestern desert, as well as the west and east of the Nile River, using density, compressive wave velocity, and bottom hole temperature (BHT) measured from deep oil wells. This research relies on spectral analysis of airborne magnetic survey data in the Kom-Ombo and Nuqra basins in order to estimate the geothermal gradient based on calculating the depth to the bottom of the magnetic source that caused the occurrence of these magnetic deviations. This depth is equal to the CPD, at which the material loses its magnetic polarisation. This method is fast and gives satisfactory results. Usually, it can be applied as a reconnaissance technique for geothermal exploration targets due to the abundance of magnetic data. The depth of the top (Zt) and centroid (Z0) of the magnetic source bodies was calculated for the 32 windows representing the study area using spectral analysis of airborne magnetic data. The curie-isotherm depth, geothermal gradient, and heat flow maps were constructed for the study area. The results showed that the CPD in the study area ranges from 13 km to 20 km. The heat flow map values range from 69 to 109 mW/m2, with an average of about 80 mW/m2. The calculated heat flow values in the assigned areas (A, B, C, and D) of the study area are considered to have high heat flow values, reaching 109 mW/m2. On the other hand, the heat flow values in the other parts range from 70 to 85 mW/m2. Since heat flow plays an essential role in the maturation of organic matter, it is recommended that hydrocarbon accumulations be located in places with high heat flow values, while deep drilling of hydrocarbon wells is recommended in places with low to moderate heat flow values.

Comparative Analysis of Climatic Change Trend and Change-Point Analysis for Long-Term Daily Rainfall Annual Maximum Time Series Data in Four Gauging Stations in Niger Delta

The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.

Data point selection for weighted least square fitting of cavity decay time constant

For the accurate extraction of cavity decay time, a selection of data points is supplemented to the weighted least square method. We derive the expected precision, accuracy and computation cost of this improved method, and examine these performances by simulation. By comparing this method with the nonlinear least square fitting （NLSF） method and the linear regression of the sum （LRS） method in derivations and simulations, we find that this method can achieve the same or even better precision, comparable accuracy, and lower computation cost. We test this method by experimental decay signals. The results are in agreement with the ones obtained from the nonlinear least square fitting method.

DETERMINATION OF PERMANENT OPTIMAL DATA POINTS AND AN EFFICIENT ALGORITHM FOR LAD PROBLEM

This paper gives a definition of permanent optimal data point of Least Absolute Deviation(LAD)problem.Some theoretical results on non-degenerate LAD problem are obtained.For computing LAD problem,an efficient,algorithm is given according to the idea of permanent optimal data point.Numerical experience shows that our algorithm is better than many of others,including the famous B R algorithm.

C^1 C^2INTERPOLATION OF SCATTERED DATA POINTS

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The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System:Single-point Experiment

A four dimensional variational data assimilation （4DVar） based on a dimension-reduced projection （DRP-4DVar） has been developed as a hybrid of the 4DVar and Ensemble Kalman filter （EnKF） concepts. Its good flow-dependent features are demonstrated in single-point experiments through comparisons with adjointbased 4DVar and three-dimensional variational data （3DVar） assimilations using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model （MM5）. The results reveal that DRP-4DVar can reasonably generate a background error covariance matrix （simply B-matrix） during the assimilation window from an initial estimation using a number of initial condition dependent historical forecast samples. In contrast, flow-dependence in the B-matrix of MM5 4DVar is barely detectable. It is argued that use of diagonal estimation in the B-matrix of the MM5 4DVar method at the initial time leads to this failure. The experiments also show that the increments produced by DRP-4DVar are anisotropic and no longer symmetric with respect to observation location due to the effects of the weather trends captured in its B-matrix. This differs from the MM5 3DVar which does not consider the influence of heterogeneous forcing on the correlation structure of the B-matrix, a condition that is realistic for many situations. Thus, the MM5 3DVar assimilation could only present an isotropic and homogeneous structure in its increments.

Methodology for Extraction of Tunnel Cross-Sections Using Dense Point Cloud Data

Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minutes as an innovation technique,which provides promising applications in tunnel deformation monitoring.Here,an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed.First,the tunnel orientation is determined using principal component analysis(PCA)in the Euclidean plane.Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m.Next,a z-score method is introduced to detect and remove the outlies.Because the tunnel cross-section’s standard shape is round,the circle fitting is implemented using the least-squares method.Afterward,the convergence analysis is made at the angles of 0°,30°and 150°.The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner.The results indicate that the proposed methodology achieves an overall accuracy of 1.34 mm,which is also in agreement with the measurements acquired by a total station instrument.The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring,which can also be extended to other engineering applications.

A Statistical Comparison Method of the Differences among Single Points for Linear Dynamic Experimental Data

The experimental random error and desired valuse of non observed points in dynamic indexes were estimated by establishing the linear regression equations about variety regulations of dynamic indexes.The methods for difference significant test among different treatments using dynamic point as indexes were presented without setting the replication on each dynamic point observed.

Security Framework for Managing Data Security within Point of Care Tests

Point of Care (PoC) devices and systems can be categorized into three broad classes (CAT 1, CAT 2, and CAT 3) based on the context of operation and usage. In this paper, the categories are defined to address certain usage models of the PoC device. PoC devices that are used for PoC testing and diagnostic applications are defined CAT 1 devices;PoC devices that are used for patient monitoring are defined as CAT 2 devices (PoCM);PoC devices that are used for as interfacing with other devices are defined as CAT 3 devices (PoCI). The PoCI devices provide an interface gateway for collecting and aggregating data from other medical devices. In all categories, data security is an important aspect. This paper presents a security framework concept, which is applicable for all of the classes of PoC operation. It outlines the concepts and security framework for preventing security challenges in unauthorized access to data, unintended data flow, and data tampering during communication between system entities, the user, and the PoC system. The security framework includes secure layering of basic PoC system architecture, protection of PoC devices in the context of application and network. Developing the security framework is taken into account of a thread model of the PoC system. A proposal for a low-level protocol is discussed. This protocol is independent of communications technologies, and it is elaborated in relation to providing security. An algorithm that can be used to overcome the threat challenges has been shown using the elements in the protocol. The paper further discusses the vulnerability scanning process for the PoC system interconnected network. The paper also presents a four-step process of authentication and authorization framework for providing the security for the PoC system. Finally, the paper concludes with the machine to machine (M2M) security viewpoint and discusses the key stakeholders within an actual deployment of the PoC system and its security challenges.

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。

MIT测井数据的点云转换及井筒形变诊断

针对多臂井径仪(MIT)采集井筒内壁空间位置参数可视化需求,现提出将其转换为点云数据,再通过对数据模型诊断分析得到井筒的形变类型。建立MIT测井过程可视化模型,通过引入柱面坐标来标定每个测点三维坐标;将每个测点的空间位置信息由极坐标转换为直角坐标,形成点云模型。采用所提方法将SH54井风险段处MIT数据成功转换为点云数据,并对该井470~471 m处的点云模型以类似CT横断扫描诊断的方式进行平铺展开,利用曲线拟合、面积计算得到SH54井在470~471 m处每个横断面的实际轮廓线和实际面积。结果表明:利用该方法生成的点云模型通过图表对比分析,可推断出该井段产生了非对称挤压缩径形变。

基于边界点估计与稀疏卷积神经网络的三维点云语义分割

针对大规模点云具有稀疏性,传统点云方法提取上下文语义特征不够丰富,并且语义分割结果存在物体边界模糊的问题,提出基于边界点估计与稀疏卷积神经网络的三维点云语义分割算法,主要包括体素分支与点分支.对于体素分支,将原始点云进行体素化后经过稀疏卷积得到上下文语义特征;进行解体素化得到每个点的初始语义标签;将初始语义标签输入到边界点估计模块中得到可能的边界点.对于点分支,使用改进的动态图卷积模块提取点云局部几何特征;依次经过空间注意力模块与通道注意力模块增强局部特征;将点分支得到的局部几何特征与体素分支得到的上下文特征融合,增强点云特征的丰富性.本算法在S3DIS数据集和SemanticKITTI数据集上的语义分割精度分别达到69.5%和62.7%.实验结果表明,本研究算法能够提取到更丰富的点云特征,可以对物体的边界区域进行准确分割,具有较好的三维点云语义分割能力.

基于三维点云的采后香蕉表征褐变定量评估方法

【目的】研究采后香蕉的表征褐变并评估其衰老程度对香蕉保鲜管理至关重要,本研究致力于解决传统人工测量香蕉表征褐变存在的劳动强度大、效率低下的问题。【方法】提出一种基于三维点云的采后香蕉表征褐变过程定量评估方法。首先利用三维扫描仪获取香蕉的三维点云模型,重构出香蕉的几何模型;然后使用欧式聚类对香蕉几何模型进行点云滤波降噪处理;再结合图像阈值分割法与散点轮廓算法(Alpha Shapes)求出香蕉的体积、表面积和黑斑面积;最后利用傅里叶函数对香蕉表面黑斑变化过程进行模拟,确定香蕉表征褐变过程的评估模型。设计本算法与溢水法测量实际香蕉体积、手绘测量面积的对比试验。【结果】拟合香蕉的生长函数,回归直线对观测值的拟合程度R2=0.9816>0.75,验证了算法的有效性。对比试验结果表明,本算法与实际测量值的平均相对误差小于1%,验证了该算法的准确性和可行性。【结论】本研究可为香蕉的保鲜管理提供数据及技术支撑。

改进的密度聚类精确自适应提取LiDAR电力线点云方法

原有邻域半径r_(Eps)与密度阈值p_(MinPts)两个参数的初始赋值导致电力线点云的提取结果存在不确定性,在密度聚类的基础上增添了点云簇类自适应判别方法,该方法避免人员重复测试初始参数的繁琐过程,采用C++语言完成了对该算法电力线精确提取及电力线拟合程序的开发与测试。结果表明:改进后的密度聚类法在电力线点云提取的损失率仅0.02%,三维重建残差为0.213 m;该方法大幅提高了电力线点云提取的准确性与便捷性,适用于高压电力走廊的电力巡检与三维重建等工作。

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。

基于商用密码的水利重要数据点面结合安全保护方法

为保障数字孪生流域建设过程中的重要数据安全,针对破解海量高精度数据汇集面临的数据窃取、泄露、损毁等问题隐患开展研究,提出网络安全基础—数据分类分级—数据安全防护的数字孪生流域数据安全总体防护方案。针对数据安全传输、存储、使用中的安全薄弱点,提出全面通用防护和应用内加密相结合的点面结合加密保护方法,利用商用密码技术对数据进行机密性、完整性、可用性保护。本研究方法已在全国水利一张图等重要系统中得到应用,可实现数据安全保护和便捷高效应用平衡,广泛适用于数字孪生流域建设中的水利重要数据全生命周期保护。

基于车载点云的道路三维实景建模方法研究

传统的基础测绘存在组织管理固化、服务模式落后、产品形式单一等问题,在新型基础测绘体系下形成了全要素三维实景模型这一成果。本文探讨基于车载点云进行城市道路三维实景建模方法研究,并以某城市主干路为试验对象,对道路及道路两侧部件点云数据进行矢量化得到道路全要素地形数据,以部件点云数据为参考结合外业调绘尺寸用3ds Max软件制作道路部件模板库,并结合点云数据和矢量数据对各类要素进行单体化,最后将道路模型和部件模型融合。结果表明,基于车载点云数据构建的城市道路全要素实景模型不仅可以保证场景的完整性和真实性,还减少了作业时间和成本,实现了各类模型之间的无缝结合,制作完成的模型精度也能满足项目精度要求。