银量法测定化学试剂氯化钠中氯化钠纯度的不确定度评定

针对银量法测定化学试剂氯化钠中氯化钠纯度,讨论了氯化钠纯度测定结果的测量不确定度的来源及评定方法,当K=2,氯化钠纯度测定结果的扩展不确定度为(99.70±0.64)%。     

Bayesian data fusion in a spatial prediction context: a general formulation

In spite of the exponential growth in the amount of data that one may expect to provide greater modeling and predictions opportunities, the number and diversity of sources over which this information is fragmented is growing at an even faster rate. As a consequence, there is real need for methods that aim at reconciling them inside an epistemically sound theoretical framework. In a statistical spatial prediction framework, classical methods are based on a multivariate approach of the problem, at the price of strong modeling hypotheses. Though new avenues have been recently opened by focusing on the integration of uncertain data sources, to the best of our knowledges there have been no systematic attemps to explicitly account for information redundancy through a data fusion procedure. Starting from the simple concept of measurement errors, this paper proposes an approach for integrating multiple information processing as a part of the prediction process itself through a Bayesian approach. A general formulation is first proposed for deriving the prediction distribution of a continuous variable of interest at unsampled locations using on more or less uncertain (soft) information at neighboring locations. The case of multiple information is then considered, with a Bayesian solution to the problem of fusing multiple information that are provided as separate conditional probability distributions. Well-known methods and results are derived as limit cases. The convenient hypothesis of conditional independence is discussed by the light of information theory and maximum entropy principle, and a methodology is suggested for the optimal selection of the most informative subset of information, if needed. Based on a synthetic case study, an application of the methodology is presented and discussed.     

激发极化法在早前寒武纪变质岩系找水中的应用

通过对在早前寒武纪变质岩系进行抗旱找水中利用地球物理勘探定井的实例分析,阐述了利用激发极化法在早前寒武纪变质岩分布区辨认含水层,确定含水层埋深,最终确定较好井位的方法。该方法对解决早前寒武纪变质岩系的干旱缺水现状有广泛深远的意义。     

遥感影像变化检测自适应阈值分割的Kriging方法

提出了一种基于空间统计理论的自适应阈值分割方法,用于多时相高分辨率遥感影像变化检测研究。针对未确定类别的目标像素点,分别以局部区域内的变化类和非变化类为样本点,将目标点的实际值和估计值进行比较,以确定目标点的类别。为验证本方法的优越性和自适应性,分别与系列传统阈值分割方法进行了对比分析。实验结果表明,本方法能够有效去除伪变化信息,减少目标的错判、误判率,提高检测精度。     

20世纪末广东省三大城市大气环境质量综合评价——改进密切值法的扩展应用

大气环境质量综合评价是一个城市环境质量考核的重要指标，改进的密切值法是其中一种切实可行的评价方法。通过对1991-2000年10年间广东省三大城市——广州、深圳和珠海的大气环境质量进行综合评价，发现20世纪末，大气环境质量最好的是珠海市，最差的是广州市。与广东省平均水平相比、三大城市中。广州市污染程度高于全省平均水平。从空气环境质量的变化趋势来看，自1997年开始，三大城市大气环境质量趋于好转，为新世纪城市空气环境质量改善提供了良好的基础。     

结构主动控制系统时间滞后测量与补偿方法

本文研究了结构主动控制系统时间滞后产生的原因,利用作者建立的结构主动控制系统进行了系统时间滞后测量方法的研究,测量得到了该系统时间滞后的具体数值,在此基础上,研究了时间滞后对控制系统的影响,提出了结构主动控制系统时间滞后的三种补偿,即移相法,泰勒级数展开法和预估状态向量法,通过主动控制试验证了时间滞后补偿方法的有效性。     

甘肃省“五化”协同发展时空演变格局及影响因素分析

以2005年、2010年和2015年甘肃省87个县区、县级市及自治县的工业化、城镇化、信息化、农业现代化与绿色化（简称“五化”）为研究对象,利用空间自相关分析、地理加权回归分析等空间分析方法,结合构建的“五化”协同发展模型,对甘肃省“五化”协同发展的时空分异格局、时空演变趋势和影响因素进行研究分析,以期为甘肃省各市州科学的制定发展政策提供参考。结果表明：甘肃省“五化”协同发展格局具有明显的空间相关性且空间差异显著,协同水平呈现出明显的西北高、东南低分布格局;“五化”发展水平和综合水平较低,协同水平总体不高,以轻度失调、濒临失调和勉强协同为主,但总体上升幅度较大;“五化”协同发展的影响因素,按其影响力大小依次为：农村居民人均可支配收入 > 城乡居民可支配收入差 > 固定资产投资总额 > 财政支出 > 地形起伏度 > 城乡居民消费差 > 降水量。     

流域水文模型计算域离散方法

常用的概念性水文模型 ,能够很好地模拟水文时间变化过程 ,但没有考虑水文变量和水文参数的空间变化与空间不均匀性。随着空间数据的获取手段的增多以及空间离散技术的发展 ,考虑水文参数和水文变量空间变化的分布式水文模型得到了极大的发展。本文详细介绍了分布式流域水文模型中用到的几种不同计算域离散方法 ,并讨论了河道汇流模型中常用到的有结构网格和无结构离散网格。地理信息系统技术对计算域离散有辅助作用 ,其有利于无结构离散网格的自动生成和交互修改 ,并可结合遥感技术 ,使水文模型能获取精确的空间分布的水文参数和水文变量。     

淮河流域水文随机方法的径流图化(英文)

Theoretical difficulties for mapping and for estimating river regime characteristics in a large-scale basin remain because of the nature of the variable under study: river flows are related to a specific area, i.e. the drainage basin, and are hierarchically organized in space through the river network with upstream-downstream dependencies. Another limitation is there are not enough gauge stations in developing countries. This presentation aims at de-veloping the hydro-stochastic approach for producing choropleth maps of average annual runoff and computing mean discharge along the main river network for a large-scale basin. The approach applied to mean annual runoff is based on geostatistical interpolation proce-dures coupled with water balance and data uncertainty analyses. It is proved by an applica-tion in the upstream at Bengbu in the Huaihe River Basin, a typical large-scale basin in China. Hydro-stochasitic approach in a first step interpolates to a regular grid net and in a second step the grid values are integrated along rivers. The interpolation scheme includes a con-straint to be able to account for the lateral water balance along the rivers. Grid runoff map with 10 km × 10 km resolution and the discharge map along the river with the 1 km basic length unit are the main results in this study. This kind of statistic approach can be widely used be-cause it avoids the complexity of hydrological models and does not depend on the meteoro-logical data.     

The Data Depth-weight-kernel Estimation of the Model Error of Satellite Orbit Determination

It is an objective fact that there exists error in the satellite dynamic model and it will be transferred to satellite orbit determination algorithm, forming a part of the connotative model error. Mixed with the systematic error and random error of the measurements, they form the unitive model error and badly restrict the precision of the orbit determination. We deduce in detail the equations of orbit improvement for a system with dynamic model error, construct the parametric model for the explicit part of the model and nonparametric model for the error that can not be explicitly described. We also construct the partially linear orbit determination model, estimate and fit the model error using a two-stage estimation and a kernel function estimation, and finally make the corresponding compensation in the orbit determination. Beginning from the data depth theory, a data depth weight kernel estimator for model error is proposed for the sake of promoting the steadiness of model error estimation. Simulation experiments of SBSS are performed. The results show clearly that the model error is one of the most important effects that will influence the precision of the orbit determination. The kernel function method can effectively estimate the model error, with the window width as a major restrict parameter. A data depth-weight-kernel estimation, however, can improve largely the robustness of the kernel function and therefore improve the precision of orbit determination.