Modeling structural change in spatial system dynamics: A Daisyworld example

System dynamics (SD) is an effective approach for helping reveal the temporal behavior of complex systems. Although there have been recent developments in expanding SD to include systems’ spatial dependencies, most applications have been restricted to the simulation of diffusion processes; this is especially true for models on structural change (e.g. LULC modeling). To address this shortcoming, a Python program is proposed to tightly couple SD software to a Geographic Information System (GIS). The approach provides the required capacities for handling bidirectional and synchronized interactions of operations between SD and GIS. In order to illustrate the concept and the techniques proposed for simulating structural changes, a fictitious environment called Daisyworld has been recreated in a spatial system dynamics (SSD) environment. The comparison of spatial and non-spatial simulations emphasizes the importance of considering spatio-temporal feedbacks. Finally, practical applications of structural change models in agriculture and disaster management are proposed.     

改进相位一致的岩心图像裂缝提取方法

针对传统相位一致算法提取裂缝连续性差的缺点,提出改进的相位一致算法.首先对岩心图像进行预提取,增大目标和背景区域的对比度,并标记疑似目标像素点;然后,利用双圆投影断点检测算子判定并重建岩心图像裂缝中的断点;最后利用相位一致算法延伸裂缝走向的特性,再次对岩心图像中的裂缝目标进行提取,并进行图像去噪处理.实验结果表明,改进后的相位一致算法,能够保留提取效果不受光照亮度影响、对目标区域敏感度高的传统相位一致算法的优点,并且有效改善所提取裂缝的连续性.     

Dealing with Process Overload Attacks in UNIX

ABTRACT

A process overload attack is an attack on a shared computer system in which a user repeatedly forks new processes and hence makes the system unusable for others. The specific problem we address is seen in an academic environment where student programs create unintentional process overload attacks in UNIX systems by careless coding. Instead of rebooting the system or manually examining and killing the processes, our approach to dealing with these attacks was to build a process load monitoring tool to detect and kill these processes automatically. This paper focuses on what we learned about the behaviors of different fork bombs, how we classified them based on their self-replicating capabilities, and our experience with detecting, killing and cleaning these unwanted processes.     

边界元插值法在含水层参数连续性问题中的应用

对于含水层参数连续性问题,传统的参数分区法会带来较大的计算误差,且其计算过程也较为繁琐。本文提出了一种新算法——边界元插值法,即在区域内对含水层压力传导系数采用二维反距离加权插值法、边界元上采用一维线性插值法,推导出边界积分方程的解析解,从而在整个区域上达到参数的连续性和可微性。将该方法与传统的有限差分法和边界元分区法同时应用于华北平原衡水试验场。计算结果表明:边界元插值法的计算结果与观测值的拟合误差在±3%以内,其精确度要略高于传统的边界元分区法和有限差分法,且计算过程较为简单。边界元插值法基本能够有效地处理含水层参数的连续性问题。     

带空间特性的角色访问控制研究

以自主开发的内嵌式安全空间数据库管理系统SECVISTA为平台,研究带有空间特性的角色访问控制SR-BAC(Spatial Role-Based Access Control)模型的特性。定义在空间环境下的空间对象区域范围约束、空间对象区域大小约束、空间对象责任分离约束和空间对象角色激活基数约束;对RBAC模型会话进行扩展,确定任一空间位置的无阻塞会话集合;讨论分析了空间环境下的角色继承关系;最终建立一个通用的、描述能力强的SRBAC模型。     

黑河中游试验区不同分辨率LAI数据处理、分析和尺度转换

008年开展的黑河综合遥感联合试验获取了大量野外实测叶面积指数(LAI)数据以及遥感LAI产品。在利用LAI地面点观测数据对遥感影像进行验证或者不同分辨率遥感产品相互比较的过程中存在由于地表异质性引起的尺度效应,导致无法直接进行验证、比较,需要进行尺度转换。以基于泰勒级数展开的尺度转换模型为基础,研究不同源LAI之间的尺度转换方法。包括两部分内容：① 以高分辨率影像为辅助数据将地面实测点尺度的LAI转换到中、低分辨率遥感像元尺度;② 利用高分辨率影像作为亚像元信息对低分辨率LAI产品进行尺度纠正。结果表明,利用泰勒级数展开模型进行尺度转换是一种简单可行的方法,经尺度转换的地面实测点尺度LAI可用作像元尺度数据比较验证的参考。     

基于增强现实技术的移动LBS系统的设计与实现

基于位置服务发展迅速,利用增强现实技术开发LBS系统,对人机界面与GIS服务整合具有重要的理论和现实意义。在此基础上,设计一个空间信息服务的构建与发布机制,利用智能终端配备的传感装置,在Android平台手持设备下扩展增强现实功能,融合真实场景和虚拟信息,搭建远程Web服务器完成数据交互,得到整体基于Web服务的LBS原型系统。     

面向空间数据抽取、转换、加载的元数据管理系统实现

空间数据仓库技术是为地理信息以及为社会经济发展提供高质量的决策信息而产生的技术,而空间数据抽取、转换和加载（ETL）是构建空间数据仓库必经的步骤,空间ETL过程面临的主要难点是空间数据的多源异质性问题。采用元数据技术辅助空间ETL方法,实现了空间元数据管理系统。     

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach.     

The impact of misregistration on SRTM and DEM image differences

Image differences between Shuttle Radar Topography Mission (SRTM) data and other Digital Elevation Models (DEMs) are often performed for either accuracy assessment or for estimating vegetation height across the landscape. It has been widely assumed that the effect of sub-pixel misregistration between the two models on resultant image differences is negligible, yet this has not previously been tested in detail. The aim of this study was to determine the impact that various levels of misregistration have on image differences between SRTM and DEMs. First, very accurate image co-registration was performed at two study sites between higher resolution DEMs and SRTM data, and then image differences (SRTM–DEM) were performed after various levels of misregistration were systematically introduced into the SRTM data. It was found that: (1) misregistration caused an erroneous and dominant correlation between elevation difference and aspect across the landscape; (2) the direction of the misregistration defined the direction of this erroneous and systematic elevation difference; (3) for sub-pixel misregistration the error due solely to misregistration was greater than, or equal to the true difference between the two models for substantial proportions of the landscape (e.g., greater than 33% of the area for a half-pixel misregistration); and (4) the strength of the erroneous relationship with aspect was enhanced by steeper terrain. Spatial comparisons of DEMs were found to be sensitive to even sub-pixel misregistration between the two models, which resulted in a strong erroneous correlation with aspect. This misregistration induced correlation with aspect is not likely specific to SRTM data only; we expect it to be a generic relationship present in any DEM image difference analysis.