地震行业地面骨干网与应急卫星通信网互联路由设计方案的探讨

本文介绍了地震行业地面骨干网与应急卫星通信网的运行现状及互联存在的路由问题,讨论了实现两网互联的路由设计目标和策略,根据OSPF协议与BGP协议的特点和适用范围,提出了统一互联的接入方法,以及联合使用OSPF和BGP协议解决两网互联路由问题的具体方案,经网络仿真平台测试证明,该方案切实可行,效果良好。     

基于灰色预测法的超市选址模型研究

超市的选址是企业战略决策的重要内容之一,影响超市选址的各种因子错综复杂,无法进行精确的定量分析。文中对影响超市选址的各种因子建立二级选址指标体系,进行权重分析,同时结合选址因子结构关系模糊、动态变化随机、浮动范围不确定等灰色特性,采用灰色预测法建立超市选址模型,并利用缓冲区分析、叠置分析等GIS空间分析方法,基于Supermapobjects组件进行了二次开发实验。     

一种基于WiFi Direct技术的位置信息共享方法

当移动设备处于离线状态时,设备之间难以实现基于位置的信息共享。本文尝试利用Android设备搭载的WiFi Direct技术建立无线自组织网络的方法,实现了在该网络下位置信息的共享,并对该网络的性能进行了测试,同时考虑了设备中存在有设备与服务器连接时的信息共享情况。试验表明,该方法可在一定程度上解决因网络中断,设备之间不能进行位置等信息的共享或从服务器获取信息的问题。     

基坑支护结构水平变形预测的遗传神经网络方法

采用遗传算法和误差反向传播算法相结合的混合算法来训练前馈人工神经网络,先用遗传学习算法进行全局训练,再用BP算法进行精确训练。就遗传算法过程中的选择、变异进行了探索,提出了用BP网络训练产生变异的遗传算法。作为实例,将该方法应用于预测基坑支护结构水平变形中。结果表明,该方法有收敛速度较快、预测精度高等优点。     

近震震源深度测定精度的理论误差分析

2012年4月2日上海徐汇区发生M_L1.9地震,位于徐汇区、闵行区和浦东新区等地区部分房屋有晃动,部分人群有感。本文分析了上海有感地震的原因,在此基础上从地震精确定位、震源深度、地震烈度、地震发生时间以及人口密度和社会影响等对该次地震的有感原因进行了详细的分析。由于目前地震还不能准确预报,而地震的发生有很大的随机性,因此,提高民众防震减灾意识尤为重要。     

一种裸露土壤湿度反演方法

针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。     

港珠澳大桥沉管隧道贯通误差预计

针对港珠澳大桥沉管隧道的安全贯通问题,该文从隧道外GPS网的布设及施测方法出发,通过多期复测数据评价GPS网具有可靠的测量精度;通过对GPS网点的稳定性分析,阐明了沉管隧道地面控制点位无法长期保留、稳定性差的特点;最后就GPS网引起的贯通误差影响值进行了估算。     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.     

IDENTIFICATION AND ANALYSIS OF NATURAL CHANNEL NETWORKS FROM DIGITAL ELEVATION MODELS

The identification and analysis of natural channel networks from digital elevation models are discussed from the point of view of their environmental applications. An interactive, graphical software package that implements some of the most widely used techniques for the automatic recognition of channel networks and for the computation of some useful geomorphologic indices and functions is presented.