Analysis of spatial clustering optimization

Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient.     

Optimising optimal image subtraction

Difference imaging is a technique for obtaining precise relative photometry of variable sources in crowded stellar fields and, as such, constitutes a crucial part of the data reduction pipeline in surveys for microlensing events or transiting extrasolar planets. The Optimal Image Subtraction (OIS) algorithm of Alard & Lupton (1998) permits the accurate differencing of images by determining convolution kernels which, when applied to reference images with particularly good seeing and signal‐to‐noise (S/N), provide excellent matches to the point‐spread functions (PSF) in other images of the time series to be analysed. The convolution kernels are built as linear combinations of a set of basis functions, conventionally bivariate Gaussians modulated by polynomials. The kernel parameters, mainly the widths and maximal degrees of the basis function model, must be supplied by the user. Ideally, the parameters should be matched to the PSF, pixel‐sampling, and S/N of the data set or individual images to be analysed. We have studied the dependence of the reduction outcome as a function of the kernel parameters using our new implementation of OIS within the IDL‐based TRIPP package. From the analysis of noise‐free PSF simulations of both single objects and crowded fields, as well as the test images in the ISIS OIS software package, we derive qualitative and quantitative relations between the kernel parameters and the success of the subtraction as a function of the PSF widths and sampling in reference and data images and compare the results to those of other implementations found in the literature. On the basis of these simulations, we provide recommended parameters for data sets with different S/N and sampling. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

一种点约束分块的矢量地理数据精确认证算法

脆弱水印可以实现对数据内容真实性的认证,并对发生变化的位置进行定位,鉴此,本文提出了一种点约束分块的矢量地理数据精确认证算法。在认证内容嵌入过程中,首先,按照点约束的方法对矢量地理数据进行分块,并将每块的数据点进行空间位置关系的“之字形”排序,建立数据点之间的位置关系;然后,将相邻点生成的脆弱水印信息嵌入到当前点。在内容精确认证时,比较原始的水印信息与提取的水印信息,判断二者是否相同来鉴别数据是否更新。将本文算法与均匀分块的方法进行了对比实验,结果表明,本文提出的认证算法能实现在数据更新时的精确认证,并可实现要素删除的精确认证,在检测数据变化时可对修改位置进行定位与标记。     

松潘地区岩金统计成矿予测中泛克立格法的应用

川西北地区是国内重要的岩金普查靶区。本文对松潘地区的地质特征,泛克立格法原理,及在本区的应用情况作了介绍,对主要计算步骤,给出基本结果,对计算获得的有意义异常区,进行地质解释,并将此方法与趋势面分析进行对比。     

中国经济增长与制度变迁的空间面板数据分析

以North的体制经济学为理论依据,将新古典经济学的Solow增长模型扩展为一个包含制度变量的计量经济学模型,并结合空间面板数据分析方法,使用1997—2005年中国省区的社会经济数据实证检验了经济制度环境对经济增长的影响。研究结果表明:该时期的制度变迁对中国的经济增长具有显著贡献力,证实了North的制度经济学理论假设。此外,空间面板数据的分析方法深入地揭示了省域经济活动之间存在的空间自相关性,即地区之间通过技术扩散、要素转移以及社会文化交流等在地理空间上发生相互影响。     

西部测图移动应急安全自救保障系统研究

移动应急安全自救保障系统是整个"西部测图工程"安全保障体系建设中的最重要组成部分。本文从"西部测图"移动应急安全自救保障建设实际需求出发,结合当前PDA硬件资源的特点,在分析测区作业特点的基础上探讨了系统建设的整体架构以及系统建设的需求特征,并详细阐述了系统的数据组织结构模型,最后给出了系统的相关功能实现和应用。     

基于深度学习的滑坡灾害易发性分析

滑坡灾害成因机理复杂、影响因素众多,深度学习作为当前人工智能领域的热点,能够更好地模拟滑坡灾害的形成并准确预测潜在的斜坡。为了挖掘深度学习在滑坡易发性的应用潜能,本文构建了一维、二维和三维的滑坡数据表达形式,并提出3种基于卷积神经网络模型（Convolutional Neural Networks, CNN）的滑坡易发性分析处理框架：基于CNN分类器、基于CNN与逻辑回归的融合和基于CNN集成,最后以江西省铅山县为研究对象进行验证,结果表明：所有基于CNN的易发性模型都能够获得准确且可靠的滑坡易发性分析结果。其中,基于二维数据的CNN模型在所有单分类器中预测精度最高,为78.95%。此外,二维CNN特征提取能够显著提升逻辑回归的预测精度,其准确率提升7.9%。最后,异质集成策略能够大幅度提升基于CNN分类器的滑坡预测精度,其准确率提升4.35%~8.78%。     

COMBINATION OF ECMWF REANALYZED DAILY RAINFALL AND PENTAD CMAP IN CHINA

In this study, 16 combinations of the ECMWF (European Centre for Medium-Range Weather Forecast) reanalyzed daily rainfall and the pentad CMAP in China for the period 1980- 1993( 1May - 31 Dec. ) were calculated. Correlation analysis was used to roughly evaluate daily rainfall for the whole of China and a combination of RPC (rotated principal component) and wavelet analyses was applied to data on observed and combined daily rainfall to obtain a detailed evaluation of the quality of these combined datasets in 6 selected major rainfall regions of eastern China. The results showed that except for intraweekly fluctuation, the best combination was roughly similar to or accorded well with observation in the aspects of space variation patterns and long period rainfall fluctuations related to monsoon onset and serious meteorologic disasters, indicating that this combination yielded better values of long term daily mean and standard deviation through the pentad CMAP ( CPC Merged Analysis of Precipitation), and can also represent rainfall fluctuations through the reanalyzed daily rainfall.