Group K-Nearest Neighbors queries in spatial network databases

This paper addresses the problem of Group K-Nearest Neighbors (GKNN) queries in spatial network databases, and suggests a novel approach based on real network distances. This approach essentially uses network Voronoi diagram properties together with a progressive incremental network expansion for determining the inner network distances that are needed to obtain GKNN queries.      

数字地籍测绘实施中的技术问题

数字地籍测绘是目前土地管理部门的首选方案,需要制定街坊划分、界址线设定、工作底图与采集草图一体化作业、数据文件管理、街坊线、图斑的绘制等技术措施。     

隧道GPS网对横向贯通误差的影响

本文在阐述隧道工程独立坐标系和隧道ＧＰＳ网数据处理方法的基础上推导出ＧＰＳ网对横向贯通误差影响的通用计算公式，分析了隧道ＧＰＳ网基准数据位置对横向贯通误差的影响。     

应用分布式存储技术优化省级CIMISS数据服务能力

随着气象数据量的不断增长,进一步提升CIMISS数据管理和服务能力的需求变得日益迫切。为解决存储系统动态扩展能力不足、并行计算与吞吐效率低下等限制CIMISS继续发展的问题,采用分布式文件系统和NAS技术替代GPFS建设共享文件系统,实现非结构化气象数据的存储功能;采用分布式数据库替代Oracle RAC建设关系数据库管理系统,实现结构化气象数据的存储功能和非结构化气象数据的索引功能。实践证明,该方案能够有效地改善CIMISS的数据存储能力、并发响应能力,适应未来气象业务对数据存储和应用的需求。     

基于不同邻近标签数选择的LANDMARC定位算法研究

对于基于射频识别的LANDMARC室内定位算法而言,该系统采用"k近邻"算法,通过选取k个与待测标签相邻且符合特定条件的参考标签,最后根据这些标签的位置结合权值估算出待测标签的坐标信息.但是在实际的操作过程中,最近邻参考标签数的选取无指导原则,具有一定的盲目性.针对这一问题,通过选取与待定位参考点最近邻的参考节点作为未知点,通过搜索法获得最优的邻近标签数k,在随后的未知节点的定位过程中,选取k个邻近标签进行定位.经过多次实验,最终得出结论,使用改进后的LANDMARC算法的性能要优于原来经典的LANDMARC算法,精确度提升了10%左右,同时避免了k值选取的盲目性.     

北京及邻区断层形变综合信息函数研究

应用综合信息函数的理论和方法，根据形变观测点的空间非均匀分布和首都圈地区几次强震前形变场的变化规律，考虑各测点的不同地球物理性质，利用近20年来的形变资料，计算了首都圈及邻近地区1995年以来的综合信息函数值，动态地显示了在局部区域内中强地震前，形变异常的演化特征。     

Use of a shared tuned mass damper (STMD) to reduce vibration and pounding in adjacent structures

The dynamic response of tall civil structures due to earthquakes is very important to civil engineers. Structures exposed to earthquakes experience vibrations that are detrimental to their structural components. Structural pounding is an additional problem that occurs when buildings experience earthquake excitation. This phenomena occurs when adjacent structures collide from their out‐of‐phase vibrations. Many energy dissipation devices are presently being used to reduce the system response. Tuned mass dampers (TMD) are commonly used to improve the response of structures. The stiffness and damping properties of the TMD are designed to be a function of the natural frequency of the building to which it is connected. This research involves attaching adjacent structures with a shared tuned mass damper (STMD) to reduce both the structures vibration and probability of pounding. Because the STMD is connected to both buildings, the problem of tuning the STMD stiffness and damping parameters becomes an issue. A design procedure utilizing a performance function is used to obtain the STMD parameters to result in the best overall system response. Copyright © 2001 John Wiley & Sons, Ltd.     

Fast Nearest Neighbor Classification Methods for Multispectral Imagery

Nearest neighbor classifiers have not been widely used by remote sensing practitioners. The lack of acceptance of these classifiers may be partially due to their notoriously slow speed of execution which makes them impractical for the classification of mega-pixel images. However, training data reduction, distance measure optimization, and neighbor searching algorithms based on the modified k-d tree can speed nearest neighbor classification substantially.     

SAINF: A toolbox for analyzing the effect of point-like,line-like and polygon-like infrastructural features on the distribution of point-like non-infrastructural features

This paper introduces a GIS-based toolbox, called SAINF, for analyzing the effects of infrastructural features on the distribution of non-infrastructural features. One of the distinct characteristics of SAINF is that it can deal not only with point-like infrastructural features but also line-like (e.g. railways) and polygon-like (e.g. big parks) features. SAINF analyzes these effects with the conditional nearest neighbor distance method and the cross K-function method. First, the paper briefly refers to these methods. Second, the paper illustrates how to use SAINF with an actual example. SAINF can be downloaded via the Internet without charge for non-profit uses.We express our thanks to Exceed Co. Ltd. for helping us program SAINF, and to Miki Arimoto for making the data of high-class apartment buildings and parks in Kiba. This development was partly supported by Grant-in-aid for Scientific Research No. 10202201 (Spatial information science for human and social sciences) and No. 14350327 (Regional supporting network planning for the elderly) by the Ministry of Education, Culture, Sports, Science and Technology of Japan.     

基于直方图的遥感图像相似性检索方法比较

相似性度量是用于研究多源数据之间相似程度的，是对空间数据进行模式识别的基础。通过单波段遥感图像的检索对两组直方图相似性检索方法进行了实验研究，即基于特征向量的相似性度量和基于概率的相似性度量。实验中发现第一组相似度量中有两种以往较少用于遥感图像检索的方法表现出色，它们分别是，统计距离和相似夹角余弦度量。第二组实验中，针对其中包含较明显的目标物体且背景较为单一的遥感图像（其直方图可看作混合高斯分布），在类别可分离判据的基础上，根据K-近邻法则提出了一种计算该类图像之间相似值的方法。实验结果表明基于K-近邻法则的计算方法行之有效。所得出的结论将对多源数据分析中相似性度量的理解与选择有积极意义。