Measuring the robustness potential of the least-squares estimation: geodetic illustration

For a linear least-squares parametric model analysis is carried out of the structure of the projection operator transforming the vector of standardised observations into the vector of standardised residuals. On this basis the properties of the model responses to observational disturbances (i.e. gross errors or blunders) are derived. A final outcome of the research can be summarised as: (1) proposing the robustness characteristics of a model and linking them with the local measures of internal reliability, being the diagonal elements in the projection operator; (2) determining the internal reliability levels satisfying specified robustness requirements, i.e. the possibility of detecting at least one of the k observational disturbances (k=1,2,…) having most disadvantageous locations in the system. The theory and a numerical example show that for the systems which have been designed to a proper level of internal reliability, the least-squares estimation can demonstrate an accordingly high level of robustness. Received: 11 June 1996 / Accepted: 28 April 1997     

神经网络计算机及其在遥感应用中的展望

本文概述了神经网络计算机的基本特点、神经网络的形式化描述、目前国际上神经网络计算机的现况以及在遥感图像处理中应用的潜力和展望。     

关于教育科学中评估问题的注记

本文对L·Saaty提出的多目标决策方法进行了分析,结合教育评估实例,指出方法中的某些形式计算,并对有关位置参数的稳健估计作了简单推导和讨论。这种稳健评估方法思想,可供现场进行教育评估参考和应用。     

1980—2020年滇池生态脆弱性评价及主要驱动因子

人类活动的加剧和经济社会的发展导致滇池开发强度持续增加,滇池生态系统结构与功能受到严重影响,评估滇池的生态脆弱性程度与主要驱动因子是明晰滇池生态系统现状和问题、实现其精准治理和可持续发展的首要任务。基于“暴露程度—敏感程度—适应程度”模型(VSD模型),选取3个准则7个要素24个指标构建滇池生态系统脆弱性评估指标体系,利用逼近理想解排序法(TOPSIS)进行权重方案比选,并通过鲁棒性检验分析,确定计算权重的最优方案。通过分析1980—1989、1990—2009、2010—2020年这3个时间段滇池的生态脆弱性,识别出影响滇池生态系统的主要驱动因子,以期为滇池未来生态保护与修复方向的确定提供参考。结果显示,1980—2020年滇池生态脆弱性呈现先增加后降低的趋势,生态脆弱度最高的是1990—2009年(0.502),属于中度脆弱。影响滇池生态系统的主要因素为敏感程度指标,其次为暴露程度指标。在暴露程度方面,影响生态系统的主要驱动因子逐渐从单一的工业污染向工农业的复合污染转变,1980—1989年工业废水排放量为主要驱动因子,1990—2009年建设用地面积是主要胁迫因素,2010—20...     

数字水印技术在遥感影像中的应用

为了保护数字影像、音频和视频资料不被非授权者使用,数字水印技术在多媒体领域受到了密切关注.本文介绍了数字水印技术的发展现状,探讨了空域和频域水印技术在遥感影像版权保护及处理后的确认等方面的应用前景.     

一种稳健性准则及相应的稳健估计

研究了已有的各种稳健性度量，根据稳健性的经典定义，本文建立了一种衡量稳健性的准则，根据这一准则，研究了在测量中常用的两种误差模型下的稳健结构。结果表明，在这一个准则和随机误差模型下的最优稳健估计，是李德仁教授提出的验后方差法；在这一准则与均值移动误差模型下的最优稳健估计，是具有均方误差最小的稳健估计。     

Improved regional scale processes reflected in projected hydrological changes over large European catchments

For the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the recent version of the coupled atmosphere/ocean general circulation model (GCM) of the Max Planck Institute for Meteorology has been used to conduct an ensemble of transient climate simulations These simulations comprise three control simulations for the past century covering the period 1860–2000, and nine simulations for the future climate (2001–2100) using greenhouse gas (GHG) and aerosol concentrations according to the three IPCC scenarios B1, A1B and A2. For each scenario three simulations were performed. The global simulations were dynamically downscaled over Europe using the regional climate model (RCM) REMO at 0.44° horizontal resolution (about 50 km), whereas the physics packages of the GCM and RCM largely agree. The regional simulations comprise the three control simulations (1950–2000), the three A1B simulations and one simulation for B1 as well as for A2 (2001–2100). In our study we concentrate on the climate change signals in the hydrological cycle and the 2 m temperature by comparing the mean projected climate at the end of the twenty-first century (2071–2100) to a control period representing current climate (1961–1990). The robustness of the climate change signal projected by the GCM and RCM is analysed focussing on the large European catchments of Baltic Sea (land only), Danube and Rhine. In this respect, a robust climate change signal designates a projected change that sticks out of the noise of natural climate variability. Catchments and seasons are identified where the climate change signal in the components of the hydrological cycle is robust, and where this signal has a larger uncertainty. Notable differences in the robustness of the climate change signals between the GCM and RCM simulations are related to a stronger warming projected by the GCM in the winter over the Baltic Sea catchment and in the summer over the Danube and Rhine catchments. Our results indicate that the main explanation for these differences is that the finer resolution of the RCM leads to a better representation of local scale processes at the surface that feed back to the atmosphere, i.e. an improved representation of the land sea contrast and related moisture transport processes over the Baltic Sea catchment, and an improved representation of soil moisture feedbacks to the atmosphere over the Danube and Rhine catchments.