基于高斯烟羽模型的船舶尾气扩散研究

为有效对港区大气污染进行治理、分析船舶尾气,本文详细介绍了一种基于高斯烟羽模型,通过MATLAB模拟仿真模型,其包括实验仿真过程、技术原理及理论模型对船舶尾气扩散进行的研究。该模型是在传统的高斯烟羽模型的基础上,通过对实源像源进行加权选择输入参数;通过矢量合成确定了气体扩散的方向,利用合成后的"风速"进行计算仿真,有效模拟了船舶尾气在港区或者海洋环境中的气体扩散模型。其模型简单且可以有效模拟船舶尾气扩散。并且进一步对后续模型的精确优化进行分析。     

成都地区大气平均温度建模及其在GPS/PWV计算中的应用研究

地基GPS气象学的关键技术是在于通过垂直方向上GPS信号的湿分量延迟值来确定大气可降水量-PWV,而这两个物理量间进行转换时需要用到一个关键性的参数——大气加权平均温度。本文首先讨论了估算加权平均温度的几种方法,然后利用成都地区2005年全年的大气探空数据,采用回归分析方法,建立了适合成都地区的大气加权平均温度模型,精度为±2.21K。最后将该公式应用到成都地区PWV的计算中,得到了非常理想的效果。     

变异函数模型参数的非线性加权总体最小二乘法

针对球状模型和指数模型两种变异函数模型线性化后的系数矩阵具有非线性形式,系数矩阵元素含有随机误差等问题,该文使用非线性加权总体最小二乘法估计变异函数模型参数。以高程异常数据为例,利用变异函数值的点对数定权法和系数矩阵中距离值的方差-协方差传播律定权法迭代解算参数,并与最小二乘法,加权最小二乘法和非线性总体最小二乘法进行对比分析。实验结果表明:非线性加权总体最小二乘法能够得到更高精度的变异函数模型参数。     

多类用户的混合星座导航系统定位性能分析

本文针对地面用户与空间用户在测量环境与观测几何动态性等方面的不同,基于导航卫星可见性与加权精度因子(WDOP)对混合星座导航系统的定位性能进行了分析.给出了不同用户的星座天空视图与可视时长;从理论上讨论了权值选取对WDOP的影响.仿真表明,地面静态用户对不同类型导航卫星的可见性差异很大;而低轨卫星用户的可视时长更短,观...     

一种可考虑历史洪水的马氏权函数法研究

导出了非连序样本下,马氏权函数法(适用于P-Ⅲ型分布)参数估计公式.在不偏性、有效性的标准下,统计试验结果表明,推导的非连序样本下的计算公式是较为有效的,且拓宽了马氏权函数法的使用范围.     

P-III分布参数的概率权重矩法S函数计算

概率权重矩法是一种估计统计分布参数的方法.本文根据不完全Γ函数在无限区间积分,推导了P-Ⅲ分布参数的S函数的计算公式.通过现有计算公式比较,其计算结果具有较高的计算精度,避免了的大量的数值积分计算.文中公式只要借助于计算编程进行求解,给定超几何函数项一定的截断误差,其运算具有较高的运行速度.文中计算公式是一种P-Ⅲ分布参数S函数的计算途径.     

油气勘探开发数据体平均值代表性分析

油气勘探开发涉及各类数据体,同一数据体可得多种平均值,目前尚无明确而有效的方法以判断何种平均值能客观反映数据体的典型水平。运用数据统计平均分析方法,对勘探开发实践中的孔隙度、渗透率、产量、成本等数据体进行系统分析,确立了加权中位数计算公式和平衡中位数法则。加权中位数计算公式适于分析不同领域正常有序数据体的基本特征,加权中位数为正常有序数据体的平衡点;平衡中位数法则适于确定有足够大数据容量、能满足统计分析基本要求、能选择合理权衡指标的正常有序数据体的典型水平;有明确物理意义的加权平均值也可确定数据体的典型水平。     

Spatially weighted supervised classification for remote sensing

A simple approach for incorporating a spatial weighting into a supervised classifier for remote sensing applications is presented. The classifier modifies the feature-space distance-based metric with a spatial weighting. This is facilitated by the use of a non-parametric (k-nearest neighbour, k-NN) classifier in which the spatial location of each pixel in the training data set is known and available for analysis. A remotely sensed image was simulated using a combined Boolean and geostatistical unconditional simulation approach. This simulated image comprised four wavebands and represented three classes: Managed Grassland, Woodland and Rough Grassland. This image was then used to evaluate the spatially weighted classifier. The latter resulted in modest increase in the accuracy of classification over the original k-NN approach. Two spatial distance metrics were evaluated: the non-centred covariance and a simple inverse distance weighting. The inverse distance weighting resulted in the greatest increase in accuracy in this case.     

Water quality monitoring at a virtual watershed monitoring station using a modified deep extreme learning machine

ABSTRACT

A new deep extreme learning machine (ELM) model is developed to predict water temperature and conductivity at a virtual monitoring station. Based on previous research, a modified ELM auto-encoder is developed to extract more robust invariance among the water quality data. A weighted ELM that takes seasonal variation as the basis of weighting is used to predict the actual value of water quality parameters at sites which only have historical data and no longer generate new data. The performance of the proposed model is validated against the monthly data from eight monitoring stations on the Zengwen River, Taiwan (2002–2017). Based on root mean square error, mean absolute error, mean absolute percentage error and correlation coefficient, the experimental results show that the new model is better than the other classical spatial interpolation methods.     

A probability-weighted moment test to assess simple scaling

We present a statistically robust approach based on probability weighted moments to assess the presence of simple scaling in geophysical processes. The proposed approach is different from current approaches which rely on estimation of high order moments. High order moments of simple scaling processes (distributions) may not have theoretically defined values and consequently, their empirical estimates are highly variable and do not converge with increasing sample size. They are, therefore, not an appropriate tool for inference. On the other hand we show that the probability weighted moments of such processes (distributions) do exist and, hence, their empirical estimates are more robust. These moments, therefore, provide an appropriate tool for inferring the presence of scaling. We illustrate this using simulated Levystable processes and then draw inference on the nature of scaling in fluctuations of a spatial rainfall process.