Derivations of Vector Area and Volume Elements in Curved Coordinate Systems for Flux Vector Fields Helping Eye Disease

Our aim in this paper is to interest retinal eye specialists in preventing dry macula degeneration by a special flurry vector field through open or closed curved surfaces. The flux of vector fields through surfaces is based on vector element area and volume element. Therefore, we explain a few geometrical derivations of area and volume elements in curved orthogonal coordinate systems. We hope that by derivation of a spatial vector field flurry against drusen through open or closed surfaces due to the Gauss theorem might select drusen under eye retina cells without destroying the cells and prevent macula degeneration. A changed flurry of a magnetic or electric vector field through a closed line causes an electric or magnetic vector field on the surface closed by the line. We also hope that derivation by Stokes’ and Greens’ theorems, with the help of iron, might help eye cells to get in life.

Research on Measure Techniques Using CAD-Vector Method for Heteromorphism Leaf Area of Garden Trees

Heteromorphism leaf area of garden trees was measured by CAD-vector method.It was showed that all the coefficients of variation were below 1% when measurement accuracy was relatively high.This method was fit for accuracy area measurement of abnormity leaf,pests and disease leaf,big leaf,small leaf and so on.

基于广域信息处理的配电网故障隔离技术研究

针对分布式电源并入配电网后,传统算法进行故障检测时存在定位准确度偏低、反应速度较慢的问题,文中基于广域信息处理技术提出了一种配电网故障隔离方法。该方法采用模态分解算法将故障复杂信号分解为多种类基础小信号,使用支持向量机对这些小信号进行数据分类。但由于传统支持向量机的收敛速度较慢,因此通过引入粒子群算法对其参数加以优化,从而提升模型的运算速度。实验结果表明,在加入分布式电源的电网中,所提算法的故障定位准确率为96.7%,平均运行时间则为43.9 s,且这两项参数在对比算法中均为最优。由此证明,该算法可应用于实际工程中,为配电网故障隔离提供技术支撑。

基于InSAR监测和PSO-SVR模型的高填方区沉降预测

基于小基线集干涉测量技术(small baseline subsets interferometric synthetic aperture radar,SBAS-InSAR)和机器学习知识对高填方区域进行地表沉降监测及预测,对工程项目的施工、检修、运营等工作都具有重要的指导意义。文章以重庆东港集装箱码头为研究对象,选取2018—2019年覆盖研究区的31景Sentinel-1A数据,利用SBAS-InSAR技术获取该区域的地表沉降数据,并进行内外精度评定;通过信息量模型分析地表沉降易发地地势特点,选择预测点位;通过灰色关联分析计算动态影响因素与沉降量之间的灰色关联度,使用主成分分析法从影响因素中提取出主成分,构建训练集和测试集,通过粒子群算法-支持向量机法(particle swarm optimization-support vector regression,PSO-SVR)预测模型对测试集数据进行预测。为验证该模型在高填方区域沉降预测的可靠性和优异性,将自回归差分整合移动平均模型(autoregressive integrated moving average model,ARIMA)作为对比模型,分别将PSO-SVR模型的预测结果和ARIMA模型的预测结果与测试集进行对比。结果表明:PSO-SVR模型的预测精度优于ARIMA模型,在高填方区域地表沉降预测中具有较好的实用性。

基于图像处理的水培生菜冠层图像叶面积估测研究

为实现精准、高效、无损地获取植物工厂环境下水培生菜相关长势参数叶面积(Leaf area,LA),基于数字图像处理和机器学习回归方法建立单株水培生菜冠层图像LA估测模型。首先,通过智能手机获取2个生菜品种不同生长期的冠层可见光图像,利用Photoshop图像处理软件将原始图像统一剪裁为900像素×900像素大小,采用中值滤波(MedianBlur)法对剪裁后的图像进行去噪运算,将RGB图像转化为HSV颜色空间,再采用mask掩膜法分割彩色图像;然后,利用图像法获取单株生菜LA实测值,构建以LA实测值为因变量,以生菜冠层投影面积(Projected leaf area,PLA)为自变量的线性回归(Linear regression,LR)模型和以全局图像特征(颜色、形状、纹理等)为自变量的支持向量回归(Support vector regression,SVR)、多元线性回归(Multiple linear regression,MLR)和随机森林(Random forest,RF)等LA估测模型进行对比分析;最后,采用决定系数(Coefficient of determination,R^(2))和均方根误差(Root mean square error,RMSE)评估模型的准确性。结果表明:RF模型估测效果最好,对于生菜品种‘绿萝’单株LA估测结果的R^(2)为0.9714、RMSE为8.89 cm2,对于品种‘碧霄’估测结果的R^(2)为0.9201、RMSE为23.34 cm2。本研究验证了RF回归模型能够较准确地估测生菜单株叶面积,可为植物工厂水培生菜LA无损估测提供新的解决方案和研究基础。

基于矩阵轮廓的时间序列Shapelet发现算法

当前时间序列Shapelet发现算法普遍采用穷举法,需要计算所有时间序列子序列的信息增益,效率较低。针对此问题,提出一种基于矩阵轮廓的Shapelet发现算法。选出最具代表性的时间序列对,计算其轮廓矩阵和差异向量,找到一簇关键区域;对找到的关键区域进行剪枝;在关键区域上搜索Shapelet并计算其信息增益,提升算法效率。在15个UCR数据集上,通过时间序列二分类实验对所提Shapelet发现算法进行验证。实验结果表明,所提算法结合Shapelet转换后具有较强分类能力,计算效率明显优于现有Shapelet发现算法。

侦察卫星对地覆盖区域建模分析与仿真

针对侦察卫星过境预报的军事需求,提出一种侦察卫星对地覆盖区域建模分析方法。在SGP4/SDP4轨道预报模型的基础上,考虑传感器的视场参数和姿态参数,给出观测矢量与地球相交、相切、不相交3种情况下侦察卫星对地覆盖区域的计算方法。以STK软件为对比依据,进行单星和侦察卫星星座对地覆盖区域仿真。试验结果表明:该方法有效可行,可为规避敌方卫星对我侦察提供信息保障。

陕蒙矿区深部开采导水裂隙带发育特征与高度预测方法

导水裂隙带发育高度是矿井防治水及安全开采的重要依据。由于陕蒙矿区地层沉积环境和沉积过程的特殊性,使得该区域导水裂隙发育高度与传统认识差别较大。考虑到目前围绕陕蒙矿区导水裂隙带发育高度计算方法缺失问题,以鄂尔多斯市营盘壕煤矿2217工作面为工程背景,采用UDEC模拟研究了不同开采进度下导水裂隙带发育规律及演化特征,分析了大能量事件与导水裂隙带发育的关联关系,进而基于GA-SVR算法,建立了区域导水裂隙带发育高度精准预测方法。结果表明:直罗组砂岩巨厚的特性使得其在覆岩运动中表现出坚硬岩层的特点,裂隙带在其下充分发育,稳定后也形成“马鞍形”;微震监测数据能精准反映区域导水裂隙带发育高度,预测相对误差仅为3.90%;建立的导水裂隙带高度预测模型的决定系数R^(2)为0.81,相对平均误差r为14.55%,预测精度显著优于经验公式(R^(2)<0,r=59.57%)。研究成果对于陕蒙矿区保水开采与安全生产设计及矿井水防治具有重要的实践意义。

2015–2022年漓江流域12天时间分辨率地表水体面积数据集

喀斯特地貌在广西地区分布广泛,具有汛期降水强度大、水文过程迅速且时空异质性高等特点。水是重要的生态基础和自然资源,及时、准确地获取地表水体的时空变化特征,对于水资源的保护与管理、旱涝灾害的快速评估以及实现各个经济产业的可持续发展等具有现实意义。本数据集以广西桂林漓江流域为研究区域,针对当前地表水体监测频次低、水体提取方法受限于样本信息以及在复杂地形区域内提取精度较低等问题,结合Sentinel-1雷达和Sentinel-2光学等多源遥感数据,基于雷达和光学传感器成像原理的不同,提出了一种基于经验阈值进行矢量分类的方法,剔除了绝大部分地物阴影的干扰,构建了2015–2022年漓江流域时间分辨率为12天、空间分辨率为10米的地表水体面积数据集,共包括196期地表水体矢量数据。通过对数据集的空间分布和定量精度验证,结果表明:本数据集对河流、湖泊和水库的入、出水口以及复杂地形区域内小型水体等的提取精度较高,总体精度达到92.73%,Kappa系数为0.85。本数据集可用于支撑漓江流域水资源保护和生态环境的可持续管理,能够为应急管理、灾害防治、水利开发和经济发展等领域的决策提供可靠数据。

我国近海阵风预报研究

利用2021年1—12月实况观测数据及ECMWF确定性模式24 h预报数据中的中低层风速与温度产品,采用支持向量机回归方法构建我国近海阵风预报模型,以提升海上阵风预报服务能力。利用2022年1—9月数据进行独立样本检验,通过与阵风系数法的对比检验得出以下结论:不同高度层的风速及温度或垂直风速及温度的变化均会对阵风预报产生影响,因而造成仅考虑模式10 m风速预报的阵风系数法在某些情况下对于阵风的高估或低估。在阵风系数法的基础上加入高空气象要素信息所构建的预报模型,能够取得更好的预报效果。对于9级阵风,该模型的预报准确率为50%,明显高于阵风系数法的30%,对不同海域的大量级阵风同样具有较好的预报效果。在ECMWF确定性模式的10 m风速预报与实况存在一定偏差时,考虑了高空要素信息的支持向量机回归预报模型的阵风预报结果较阵风系数法更接近实况。

高山峡谷地区地质灾害易发性评价——以怒江州为例

高山峡谷地区地质灾害频发,目的为探究地质灾害易发性空间分布状况,方法以怒江州为研究区,综合地质条件、气象水文、植被覆盖等因素,筛选高程、坡度、坡向、曲率、起伏度等12个共线性低的评价因子,构建区域易发性评价指标体系,并基于栅格单元采用信息量(information value,IV)模型、信息量-BP神经网络(information value-back propagation neural networks,IV-BPNN)耦合模型和信息量-支持向量机(information value-support vector machine,IV-SVM)耦合模型进行地质灾害易发性评价。结果结果表明:(1)用实际地质灾害点验证易发性结果,灾害点与3种易发性结果在空间分布上具有较好的一致性;(2)将易发性指数划分为低、中、高和极高易发4个等级,其中IV模型、IV-BPNN耦合模型与IV-SVM耦合模型的高+极高易发区面积占比分别为37.12%,32.36%,23.08%,高与极高易发区呈线状分布,主要集中在怒江、澜沧江、独龙江等水系沿岸地区、道路附近和地质构造活跃的区域;(3)IV模型、IV-BPNN耦合模型与IV-SVM耦合模型的受试者曲线(receiver operating characteristic curve,ROC)的曲线下面积(area under the curve,AUC)分别为0.884,0.889,0.901。结论3种地质灾害易发性评价模型均有较高的预测精度,其中IV-SVM耦合模型准确率最高,分区结果较可靠,可为当地政府制定地质灾害防治措施提供参考。

基于LVQ神经网络的水果图像分割研究

由于传统边沿检测算子在水果颜色多样、亮度不均匀时,难以分割得到完整、无噪声的二值图像且依赖优化的阈值,本研究提出了一种基于LVQ神经网络的水果图像分割方案。首先将彩色图像转变为灰度图像;然后对Canny算子获得的边沿图像随机选取一些像素作为网络的学习监督信号,仅以灰度图像中相同位置像素3×3邻域的Kirsch算子梯度值作为输入,训练权值;最后重新将原灰度图像的Kirsch算子梯度值输入到训练好的网络中,获得封闭的边沿并填充得到二值图像。考察了14幅像素为640×480的水果图像,结果表明:网络在很宽广的阈值范围内(0.001~0.99)分割得到完整、一致的二值图像;面积误差最小为0.9%,最大为8.83%,不依赖于优化的阈值,不需要对原始图像滤波预处理。与没有阈值及滤波的算法相比,本方案的误差和时间复杂度均更低;与设置了阈值和/或滤波的算法相比,本方案与之相当,甚至效果更优。

Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallow-water model

Sensitive areas for prediction of the Kuroshio large meander using a 1.5-layer,shallowwater ocean model were investigated using the conditional nonlinear optimal perturbation(CNOP) and first singular vector(FSV) methods.A series of sensitivity experiments were designed to test the sensitivity of sensitive areas within the numerical model.The following results were obtained:(1) the effect of initial CNOP and FSV patterns in their sensitive areas is greater than that of the same patterns in randomly selected areas,with the effect of the initial CNOP patterns in CNOP sensitive areas being the greatest;(2) both CNOP- and FSV-type initial errors grow more quickly than random errors;(3) the effect of random errors superimposed on the sensitive areas is greater than that of random errors introduced into randomly selected areas,and initial errors in the CNOP sensitive areas have greater effects on final forecasts.These results reveal that the sensitive areas determined using the CNOP are more sensitive than those of FSV and other randomly selected areas.In addition,ideal hindcasting experiments were conducted to examine the validity of the sensitive areas.The results indicate that reduction(or elimination) of CNOP-type errors in CNOP sensitive areas at the initial time has a greater forecast benefit than the reduction(or elimination) of FSVtype errors in FSV sensitive areas.These results suggest that the CNOP method is suitable for determining sensitive areas in the prediction of the Kuroshio large-meander path.

Derivation of Cloud-Free-Region Atmospheric Motion Vectors from FY-2E Thermal Infrared Imagery

The operational cloud-motion tracking technique fails to retrieve atmospheric motion vectors （AMVs） in areas lacking cloud; and while water vapor shown in water vapor imagery can be used, the heights assigned to the retrieved AMVs are mostly in the upper troposphere. As the noise-equivalent temperature difference （NEdT） performance of FY-2E split win- dow （10.3-11.5 μm, 11.6-12.8 μm） channels has been improved, the weak signals representing the spatial texture of water vapor and aerosols in cloud-free areas can be strengthened with algorithms based on the difference principle, and applied in calculating AMVs in the lower troposphere. This paper is a preliminary summary for this purpose, in which the principles and algorithm schemes for the temporal difference, split window difference and second-order difference （SD） methods are introduced. Results from simulation and cases experiments are reported in order to verify and evaluate the methods, based on comparison among retrievals and the ＂truth＂. The results show that all three algorithms, though not perfect in some cases, generally work well. Moreover, the SD method appears to be the best in suppressing the surface temperature influence and clarifying the spatial texture of water vapor and aerosols. The accuracy with respect to NCEP 800 hPa reanalysis data was found to be acceptable, as compared with the accuracy of the cloud motion vectors.

基于变异系数和支持向量机的含DG台区线损智能诊断研究

分布式电源的接入改变了台区潮流,增加了台区线路高损概率。为找出引起台区线路高损成因,提出一种基于变异系数法和支持向量机的分布式电源接入台区线损智能诊断方法。首先利用变异系数法对分布式电源接入台区特征指标进行关联性分析;其次,采用支持向量机算法衡量线损预测数据与真实数据相对误差,进而筛选出线损异常台区;然后,基于变异系数法得到的各指标权重更新异常台区数据,诊断线路高损成因;最后,采用25节点台区案例,进行方法验证。试验结果验证了所提方法的有效性。

基于大数据分析的灌区配水渠道流量预测分析研究

为提升灌区配水渠道流量预测效果,有效指导灌区水资源管理,研究基于大数据分析的灌区配水渠道流量预测分析方法。以玛河灌区的玛纳斯县灌区为研究工程,在灌区配水渠道上下游分别安装水位计采集配水渠道水位信息,通过RTU终端汇总水位信息后,通过GPRS无线通信传输至上位机流量预测终端,流量预测终端利用大数据分析技术构建基于粒子群优化支持向量机(PSO-SVM)的流量预测模型,以水位信息为模型输入,输出灌区配水渠道流量预测结果。分析研究结果显示:该方法可有效预测灌区配水渠道流量,且流量预测结果与实际流量之间的相对误差较小。该方法具备灌区配水渠道流量预测实际应用性,可为灌区水资源规划提供数据基础。

Stand basal area modelling for Chinese fir plantations using an artificial neural network model

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

城区病媒生物控制的实践与思考

病媒生物控制水平是衡量城市环境的一项重要指标,关系到环境质量、食品安全以及多种传染病传播风险。本文结合病媒生物控制适宜技术在商业密集、物业小区密集和城中村为代表的3个社区试点情况对实践经验进行总结,以期为城区病媒生物防治提供借鉴和参考。

A Human Body Posture Recognition Algorithm Based on BP Neural Network for Wireless Body Area Networks

Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.

基于ArcGIS模型构建器的多灌区一张图矢量数据批量合并方法研究

灌区一张图建设是实现灌区现代化、信息化、数字化的首要工作。当前,全国开展以灌区为单元的灌区一张图建设工作,收集了大量灌区一张图矢量数据。文章研究了基于GIS模型构建器的多灌区一张图矢量数据批量合并方法,通过GIS模型构建器中的迭代工作空间结合追加工具,介绍了快速实现多灌区一张图矢量数据合并到指定地理信息数据库的模型建立步骤和使用方法。该模型的应用可快速高效地实现多个灌区一张图矢量数据合并,使用简捷。