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。     

一种自组织特征映射网络的模糊学习方法

本文提出一种模糊熵准则以及基于模糊熵准则的复合学习准则,可以较好地指导自组织特征遇射网络的学习过程。     

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%.     

Coping with Uncertainty in Map Learning

In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refer to such a representation as a map, and the process of constructing a map from a set of measurements as map learning. In this paper, we develop a framework for describing map-learning problems in which the measurements taken by the robot are subject to known errors. We investigate approaches to learning maps under such conditions based on Valiant's probably approximately correct learning model. We focus on the problem of coping with accumulated error in combining local measurements to make global inferences. In one approach, the effects of accumulated error are eliminated by the use of local sensing methods that never mislead but occasionally fail to produce an answer. In another approach, the effects of accumulated error are reduced to acceptable levels by repeated exploration of the area to be learned. We also suggest some insights into why certain existing techniques for map learning perform as well as they do. The learning problems explored in this paper are quite different from most of the classification and boolean-function learning problems appearing in the literature. The methods described, while specific to map learning, suggest directions to take in tackling other learning problems.     

First and second order dynamics in a hierarchical SOM system for action recognition

Human recognition of the actions of other humans is very efficient and is based on patterns of movements. Our theoretical starting point is that the dynamics of the joint movements is important to action categorization. On the basis of this theory, we present a novel action recognition system that employs a hierarchy of Self-Organizing Maps together with a custom supervised neural network that learns to categorize actions. The system preprocesses the input from a Kinect like 3D camera to exploit the information not only about joint positions, but also their first and second order dynamics. We evaluate our system in two experiments with publicly available datasets, and compare its performance to the performance with less sophisticated preprocessing of the input. The results show that including the dynamics of the actions improves the performance. We also apply an attention mechanism that focuses on the parts of the body that are the most involved in performing the actions.     

基于ZedGraph和Google Maps的地应力分析系统设计

随着信息技术的不断发展,计算机技术和网络技术在各行各业中日益被广泛应用。以往的一些科学计算,数据分析都是依靠工作人员的高负荷重复劳动来实现,随着网络开发技术、数据库技术、电子地图等一系列相关技术的相继出现,利用计算机来分析和处理各种序列数据越来越受到相关技术人员的青睐。利用ZedGraph图表控件,实现对地应力监测数据的动态曲线分析,结合Google Maps来实现对监测点的直观呈现,极大地方便了研究人员对地震形成的检测和分析研究。     

基于ARM的手机位置采集及跟踪研究

针对特殊群体的实时位置跟踪需求,研究了GPS位置数据获取算法和数据采集时间间隔更新算法,设计实现了基于ARM和Google Maps的手机位置跟踪系统。用户只需随身携带GPS手机,监护人即可通过嵌入式移动终端在地图上实时查看其位置信息,并在用户接近和进入危险区域时报警,可应用于老人、小孩、残疾人等特殊群体的保护。基于ARM的嵌入式位置信息数据库保存了用户出行数据,为智能手机定位的大数据研究提供支持。通过对GPS数据重复性、位置信息数据库以及ARM位置跟踪系统的性能测试,验证了算法的有效性及设计思路的可行性。     

基于矿山GIS的煤矿矿图维护与管理

在煤矿生产中，矿山地学信息时刻都处于时间和空间的动态变化之中。煤矿生产中使用的矿图是为了满足煤矿生产的需要，因此矿图的内容几乎天天都在变动，矿图的及时维护与更新十分重要。针对矿山生产的实际情况，并基于矿山GIS(MGIS)，论述了矿山空间数据的GIS管理、矿图的绘制与更新。