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.      

基于退火BP神经网络的GPS高程转换

阐述模拟退火算法的基本思想和原理,提出并介绍模拟退火算法优化的BP神经网络模型在GPS高程转换中的具体应用,同时编写相应的MATLAB处理程序,结合大量数据进行仿真实验,结果表明文中提出的退火BP神经网络模型具有收敛速度快、精度高、避免陷入局部最小的优良特性。     

基于GPS基准站网的GPS测量

采用基于GPS基准网的GPS测量可以提高GPS测量的可靠性和准确性 ,可以消除和减小GPS测量观测值中与时间和空间相关的大气误差和轨道误差的影响 ,使中、长距离快速静态定位和RTK动态定位成为现实 ,还具有可直接得到WGS84Z坐标等优点。本文介绍基于GPS基准站网的GPS测量 ,简要地论述GPS虚拟参考站法VRS和区域改正数法FKP的原理、方法和特点 ,以及目前实施中存在的一些问题     

GPS网与地面网的坐标连接

本文对坐标系统的转换以及坐标的拟合推估进行了论述。     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

以TCSEC标准为基础对我国政府机关内部网络安全问题的探讨

通过对目前我国政府机关内网安全观念缺失的背景及安全缺陷的分析,提出目前内部网络安全缺陷的几种表现形式,并提出加强网络安全的几点对策和建议。     

Small-world characteristics on transportation networks: a perspective from network autocorrelation

Studies on small-world networks have received intensive interdisciplinary attention during the past several years. It is well-known among researchers that a small-world network is often characterized by high connectivity and clustering, but so far there exist few effective approaches to evaluate small-world properties, especially for spatial networks. This paper proposes a method to examine the small-world properties of spatial networks from the perspective of network autocorrelation. Two network autocorrelation statistics, Moran’s I and Getis–Ord’s G, are used to monitor the structural properties of networks in a process of “rewiring” networks from a regular to a random network. We discovered that Moran’s I and Getis–Ord’s G tend to converge and have relatively low values when properties of small-world networks emerge. Three transportation networks at the national, metropolitan, and intra-city levels are analyzed using this approach. It is found that spatial networks at these three scales possess small-world properties when the correlation lag distances reach certain thresholds, implying that the manifestation of small-world phenomena result from the interplay between the network structure and the dynamics taking place on the network.      

苏通大桥钢箱梁制造几何控制三维高精度基准网测设

本文详细介绍了对苏通大桥主桥钢箱梁制造几何控制三维高精度基准网的测设，通过实际工程的实施对高精度三维基准网的方案设计和精度控制进行了有效的探索。     

Detection of station coordinate discontinuities within the Italian GPS Fiducial Network

The Detection Identification Adaptation (DIA) procedure was applied to the coordinate time-series of some permanent GPS stations belonging to the Italian GPS Fiducial Network (IGFN), of the Italian Space Agency (ASI), to detect discontinuities and to reject outliers. The daily solutions of the stations of Cagliari, Genoa, Medicina, Noto, Turin, Perugia and Venice were computed for the period 1997.0–2003.0 using Bernese GPS software v.4.2. The data were interpolated using a model with a linear term and an annual periodic term. The parameters were estimated by least squares. The DIA procedure was organized to automatically detect discontinuities and outliers. Approximately, 70% of the discontinuities present in the coordinate time-series were identified and their magnitudes were estimated. The identified discontinuities are basically caused by equipment replacement and reference frame changes, but in a few cases the reason is still unknown. With regard to the outliers, roughly 6% of the data were rejected. These data were considered outliers on the base of the level of significance and of the power of the test adopted in this work. Except for the stations of Perugia and Venice, the estimated coordinates agree with ITRF2000 values at the 10 mm level, and the estimated velocities are within a few mm/year of the ITRF2000 values.     

径向基神经网络GPS高程转换方法

针对GPS高程转换问题,给出了基于径向基神经网络转换GPS高程的模型。用实际观测数据对该模型进行了试验,结果表明,用径向基神经网络转换GPS高程精度高于二次拟合法和BP神经网络法。径向基神经网络能够有效克服BP神经网络局部极小值的缺点,并且具有较高的收敛速度,在GPS高程转换方面具有广阔应用前景。