Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

Optimal Trajectory Planning for Wheeled Mobile Robots Based on Kinematics Singularity

This research introduces a new optimality criterion for motion planning of wheeled mobile robots based on a cost index that assesses the nearness to singularity of forward and inverse kinematic models. Slip motions, infinite estimation error and impossible control actions are avoided escaping from singularities. In addition, high amplification of wheel velocity errors and high wheel velocity values are also avoided by moving far from the singularity. The proposed cost index can be used directly to complement path-planning and motion-planning techniques (e.g. tree graphs, roadmaps, etc.) in order to select the optimal collision-free path or trajectory among several possible solutions. To illustrate the applications of the proposed approach, an industrial forklift, equivalent to a tricycle-like mobile robot, is considered in a simulated environment. In particular, several results are validated for the proposed optimality criterion, which are extensively compared to those obtained with other classical optimality criteria, such as shortest-path, time-optimal and minimum-energy.     

Improvements of nonlinear dynamic modeling of hot-film MAF sensor

The operating procedure of engine requires that the mass air flow (MAF) sensor can measure the pulsation air flow accurately. Therefore the MAF sensor must possess fast response speed. It is necessary to study the dynamic characteristic of the MAF sensor. Both the experimental equipments and method used by authors previously are improved to obtain the sensor response data accurately in this paper. The static and dynamic separable modeling method is adopted to build the uniform nonlinear dynamic model of the hot-film MAF sensor in the Hammerstein and Wiener forms, which can describe the large/small flow-rate and positive/negative step responses. The performance indexes are calculated by the actual and model responses.     

基于社区分析的网络分割

在并行计算中,网络分割的质量会极大地影响并行计算的效率,然而在网络分割时并没有一个统一的质量评价指标,因此网络分割评价指标的定义以及最佳网络分割方法的构造,成为大规模网络中并行计算的一个核心问题.根据对影响并行计算效率的各种因素的分析,给出了一个网络分割评价指数的定义,并利用并行最短路径的计算验证了该评价指数的定义的正确性;并且基于社区分析,给出了一个全新的网络分割方法,理论分析表明:该网络分割方法是最优的.     

浅谈高等学校财务与科研信息系统平台整合建设

整合高等学校财务核算系统与科研信息系统,建立统一的信息平台,可以解决高校科研项目管理、经费核算与指标控制相脱离问题。将科技人员信息系统、科技项目信息系统、科技成果信息系统及财务信息系统等信息孤岛相互整合,形成科研项目申报、立项、管理及财务核算、监督、分析、控制的一体化平台,提高科研项目管理与财务核算信息化水平。     

Web-based visualization of spatial objects in 3DGIS

Adaptive rendering large and complex spatial data has become an important research issue in a 3DGIS application. In order to transmit the data to the client efficiently,this paper proposes a node-layer data model to manage the 3D scene. Because the large spatial data and limited network bandwidth are the main bottlenecks of web-based 3DGIS,a client/server architecture including progressive transmission methods and multiresolution representations,together with the spatial index,are developed to improve the p...     

游戏程序设计中若干问题的探讨

游戏编程是当今程序设计的一个热点,开发过程会遇到各种各样的问题。通过限制构造函数和拷贝函数,设计一个单体全局管理类,可以避免不合适的调用。采用分块技术加栽地图,可以在时空性能上优化游戏。阐述了D3D的空间坐标变换。分析了万向节死锁问题发生的原因,并提出了解决办法。     

关于高校双语教学课堂质量评价体系的研究

该文根据现代教育评价理论建立了高校双语教学课堂质量评价体系的。结合因素分解法和特尔斐法对评价指标进行了确定。采用了专家排序法对评价体系各指标的权重进行了计算,并对所建立的评价体系的使用方法及结果的分析进行了详细的阐述．揭示了各项结果的现实意义。     

最小海明距离分类器的字符识别方法研究

应用最小海明距离(Hamming Distance,缩写HD)分类器,研制了一种新型的基于最小海明分类器的字符识别方法。讨论了该字符识别方法实现的技术路线,较好解决了理论的工程化应用问题,对500帧连续图像的时间字符进行实验,时间字符识别准确率为99.8%,达到了字符比较准确识别的目的。     

基于运动估算的多分辨率地形分块调度方法

针对常用的基于视点位置进行地形分块调度方法存在可视窗口固定、调度范围和时机不能根据视点运动特点进行调整而导致内存利用率低的问题,提出一种基于运动估算的地形分块调度方法.以数据密度为标准进行多分辨率地形划分,结合四叉树和二维矩阵对地形分块建立空间索引,重点研究以视点位置、视线方向、视点运动向量和地形分块数据量为参数的调度方法;通过计算预加载区域和卸载区域的地形分块,实时更新其调度范围和调度时机.实验结果表明,与基于视点位置的方法相比,该方法平均能提高3～4倍左右的内存有效数据比例和绘制帧速.