基于Google Maps API的光缆线路巡检系统的应用研究

本文介绍了基于WebGIS的光缆线路巡检系统,分析了GoogleMaps的API技术,GPS定位技术。建立基于GoogleMaps和AIAX技术的webGIS系统,以及如何将其应用到光缆线路巡检中,系统采用B／S开发模式。     

Evaluating MODIS data for mapping wildlife habitat distribution

Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect habitat data in the field. Many habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolution data are too coarse for habitat mapping. In this study we compare two models using data from different satellite sensor systems for mapping the spatial distribution of giant panda habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derived from a digital elevation model obtained from topographic maps of the study area). The second model is based on the Ecological Niche Factor Analysis (ENFA) of a time series of weekly composites of WDRVI (Wide Dynamic Range Vegetation Index) images derived from MODIS (Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. Results showed that the model using the seasonal variability of MODIS-WDRVI had a similar prediction success to that using Landsat TM and digital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provides an appropriate environmental predictor for giant panda habitat mapping. Therefore, the information contained in remotely sensed data acquired with low spatial resolution but high frequency of acquisitions has considerable potential for mapping the habitat distribution of wildlife species.     

基于Google Maps API的旅游特色数据库的设计与实现

Google Maps API[1]所具有的开放式、低成本和易用性等特点,使其能够应用到气象、地理,以及旅游等各个领域。旅游特色数据库是基于Web实现的一种新型GIS旅游服务,本文正是以建立旅游特色数据库为例,探讨了GoogleMaps API在旅游方面的应用。     

Extracting significant Website Key Objects: A Semantic Web mining approach

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.     

一种面向维文网站的动态天气图文信息服务支持系统

维吾尔文地理信息服务的研究与应用的匮乏,使维吾尔用户很难享受基于本民族语言的地理信息服务。该文以新疆动态天气图文信息服务为例,提出了一种面向维文网站的WebGIS服务解决方案。方案首先通过集成免费Google Maps API地图服务和实时免费Yahoo Weather RSS天气信息,制作新疆县域维吾尔文天气信息服务模块;其次,将该模块作为引用对象,根据用户对动态天气图文信息服务框架大小和位置的设定,个性化地嵌入到任意维吾尔文大众网站中,使更广大的维吾尔用户享受WebGIS带来的信息服务。该研究推动了WebGIS在维吾尔语语境的新应用,为新疆各族群众提供了便捷动态天气图文信息服务。     

Google Maps API在林业局级林下作物栽培中的应用

林下经济以其能够充分利用林下土地资源及大面积林荫的优势而得到众多农林专家和林农的青睐,其在实现农林资源共享、优势互补和循环生态方面发挥着很大的作用。结合林下经济发展现状及广大林农的需求特点,综合运用当前先进的Google Maps API技术和A-JAX技术,以JSON作为客户端与服务器的数据传送格式,构建基于B/S的林业局级林下作物栽培咨询平台。该平台提供给用户直观化的信息查询与呈现效果,不仅有效提高农户种植的科学性,也可在一定程度上帮助管理层制定决策。     

基于Google地图的KML空间数据查询

给出KML空间数据查询的解决方案,并在基于Google地图建立的Web地理信息系统中予以实现.该方案采用Linq to XML方法实现KML属性查询,采用Net Topology Suite计算空间对象之间的拓扑关系,综合应用此两种技术解决KML空间查询.基于此方案,在VS2010中开发出一套交互式KML空间查询工具,包括点选、连选、区域选择、依属性选择、去选等.     

在移动环境下基于Android的车辆位置信息监控应用研究

提出基于Android的车辆位置信息监控,通过建立Web服务发布车辆的GPS信息,利用Android平台的强大的网络功能及Google Maps位置感知,解决了在移动环境下车辆GPS监控系统对车辆位置信息进行监控的问题。     

Artificial Neural Network Model for Predicting Stable and Unstable Regions in Cu-Zn Alloys

Processing maps are developed using the Dynamic Materials Model (DMM) and instability criterion, which help in choosing optimum process parameters for hot-working of materials. Certain high-level expertise is required to interpret and extract the information on instability regimes to be avoided during processing. In recent years, Artificial Neural Network (ANN) models have been developed to predict flow stress by using the input vector; namely, temperature, strain rate and strain. In this study, using the available Cu-Zn alloy data, ANN model has been developed to classify the hot-working process parameters, such as temperature, strain rate and flow stress for instability regime, directly from the corrected flow stress data without applying the DMM. This model uses 10 compositions of Cu-Zn system, ranging from 3% Zn to 51% Zn. The developed ANN model has been able to learn the nonlinear classifier, which separates unstable region from the stable region in the Cu-Zn alloy system with zinc content less than 40%.     

基于中文文本分类的分词方法研究

文本分类有助于用户有选择地阅读和处理海量文本,因此其预备工作分词系统的研究是很有意义的。该文主要提出了一种基于中文文本分类的分词方法,区别于常用的基于字符串匹配等方法,并利用数据库特有的查询技术设计和实现了该分词系统,旨在通过新的分词方法提供更加准确的分词率,同时提高系统实现效率。