基于ArcGIS Server的服务器对象扩展方法研究

针对ArcGIS Server现有功能无法满足一些特殊业务逻辑的问题,本文对Arc GIS Server服务器扩展方法进行了研究。阐述了ArcGIS Server面向服务的底层架构,在此架构下对三种主要GIS服务器扩展方法进行对比分析,得出服务器对象扩展是最可行的方法。最后对该方法的模型结构和实现流程进行研究,并通过具体的项目实例进行说明。     

SOE技术在测绘成果网络分发服务系统中的应用研究

作为测绘信息化的重要组成部分,测绘成果网络分发服务系统的建设提升了测绘成果信息化管理水平,促进了测绘成果的推广应用。本文在总结福建省测绘成果网络分发服务系统建设成果的基础上,基于SOE技术解决其在推广应用过程中存在问题,提高了测绘成果的公共服务能力与水平。     

Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN

Grain-size distribution data,as a substitute for measuring hydraulic conductivity(K),has often been used to get K value indirectly.With grain-size distribution data of 150 sets of samples being input data,this study combined the Artificial Neural Network technology(ANN)and Markov Chain Monte Carlo method(MCMC),which replaced the Monte Carlo method(MC)of Generalized Likelihood Uncertainty Estimation(GLUE),to establish the GLUE-ANN model for hydraulic conductivity prediction and uncertainty analysis.By means of applying the GLUE-ANN model to a typical piedmont region and central region of North China Plain,and being compared with actually measured values of hydraulic conductivity,the relative error ranges are between 1.55%and 23.53%and between 14.08%and 27.22%respectively,the accuracy of which can meet the requirements of groundwater resources assessment.The global best parameter gained through posterior distribution test indicates that the GLUEANN model,which has satisfying sampling efficiency and optimization capability,is able to reasonably reflect the uncertainty of hydrogeological parameters.Furthermore,the influence of stochastic observation error(SOE)in grain-size analysis upon prediction of hydraulic conductivity was discussed,and it is believed that the influence can not be neglected.     

Stochastic observation error and uncertainty in water quality evaluation

When evaluating water quality, the influence of physical weight of the observed index is normally taken into account, but the influence of stochastic observation error (SOE) is not adequately considered. Using Monte Carlo simulation, combined with Shannon entropy, the Principle of Maximum Entropy (POME) and Tsallis entropy, this study investigates the influence of stochastic observation error (SOE) for two cases of the observed index: small observation error and large observation error. Randomness and fuzziness represent two types of uncertainties that are deemed significant and should be considered simultaneously when developing or evaluating water quality models. To that end, three models are employed here: two of the models, named as model I and model II, consider both the fuzziness and randomness, and another model, considers only fuzziness. The results from three representative lakes in China show that for all three models, the influence of stochastic observation error (SOE) on water quality evaluation can be significant irrespective of whether the water quality index has a small observation error or a large observation error. Furthermore, when there is a significant difference in the accuracy of observations, the influence of stochastic observation error (SOE) on water quality evaluation increases. The water quality index whose SOE is minimum determines the results of evaluation.     

当前我国矿业改革与发展的资本市场问题

通过对我国大型矿山企业基本情况的分析提出了推进资产重组的必要性和建议 ,认为走股份制道路是其有效途径。并充分利用现有政策、措施和两个市场 ,两种资源 ,促进企业扭亏为盈 ,对资产重组中可能出现的几个问题提出了看法。