基于GL Studio的无人机地面站天线控件设计与实现

为了实现良好的人机交互,并对无人机宽带数据链进行实时直观地监控,搭建了基于GL Studio的无人机地面站天线控件,并详细介绍了其设计开发原理和方法.GL Studio生成的地面站控件不仅具有良好的可移植性,而且还可以与多种软件开发平台实现很好的融合.     

Efficient integration of fine-grained access control and resource brokering in grid

In this paper, we present a novel resource brokering service for grid systems which considers authorization policies of the grid nodes in the process of selecting the resources to be assigned to a request. We argue such an integration is needed to avoid scheduling requests onto resources the policies of which do not authorize their execution. Our service, implemented in Globus as a part of Monitoring and Discovery Service (MDS), is based on the concept of fine-grained access control (FGAC) which enables participating grid nodes to specify fine-grained policies concerning the conditions under which grid clients can access their resources. Since the process of evaluating authorization policies, in addition to checking the resource requirements, can be a potential bottleneck for a large scale grid, we also analyze the problem of the efficient evaluation of FGAC policies. In this context, we present GroupByRule, a novel method for policy organization and compare its performance with other strategies.

网格环境下基于P2P的数据集成方法

针对旧有数据集成方法已不适应网格环境下高动态数据集成的现状,提出基于P2P的数据集成方法,给出一个异构的XML数据源的集成框架。通过在各Peer点直接建立模式映射,利用给出的分解再组成算法,有效地完成对各网格节点上动态XML数据源的集成。     

基于网格的散乱点曲面重构技术

提出一种利用网格技术实现散乱点曲面重构的方法,设计适合网格并行的松耦合分层重构算法,在此基础上研究基于Agent技术的智能任务分解机制和基于Condor-G技术的任务分配与调度策略,并搭建网格实验平台进行测试。对重构结果的分析表明,该技术可提高基于散乱点的曲面重构速度,降低应用成本。     

网格环境下的分布仿真事件调度研究

针对网格仿真中的事件调度问题,提出一种新的时序管理方法。该方法同时考虑网格仿真成员的按需模型服务调用与成员间的交互,对仿真中具体存在的事件进行分类,建立成员的相关局部事件触发矩阵和时序关系矩阵,并应用Mutli-AWS算法求解每个事件的开始时间。实验结果证明,该方法可以确保成员之间事件的时序关系,有效解决成员按需模型服务调用问题。     

基于密度可达的多密度聚类算法

为对多密度数据集聚类,提出一种基于密度可达的多密度聚类算法。使用网格划分技术来提高计算每个点密度值的效率,每次聚类都是从最高密度点开始,根据密度可达的概念和广度优先的策略逐步向外扩展进行聚类。实验表明,该算法能够有效地对任意形状、大小的均匀数据集和多密度数据集进行聚类,并能较好地识别出孤立点和噪声,其精度和效率优于SNN算法。     

基于感知网格的无线传感器网络动态采样策略

在分析无线传感器网络时空相关性模型的基础上,提出一种基于感知网格的无线传感器网络动态采样策略.将监测区域划分为多个感知网格,感知网格内只有簇头节点保持活跃状态,当出现异常数据后再激活感知网格内其他节点来获得更详细的信息.该策略通过减少无线传感器节点之间相同的或相近的采样数据上传来降低冗余信息的传输.仿真结果表明:该策略显著提高了无线传感器网络能量效率.     

闪电快电场信号时频特性的小波分析

应用小波变换方法将具有短时非平稳特点的闪电快电场时域波形信号转换到二维的时频平面,从时间和频率上同时描述闪电快电场信号的电场强度或相对能量分布,揭示其所包含的不同频率分量及其随时间变化的特性.分析结果表明,基于小波变换的闪电快电场信号时频特性分析可以挖掘闪电电场更深层次的变化规律和特征;也为闪电放电类型的识别研究提供了新的思路.     

基于角度方差的多层次高维数据异常检测算法

异常检测一直是数据挖掘领域的重要工作之一。基于欧式距离的异常检测算法在应用于高维数据时存在检测精度无法保证和运行时间过长的问题。在基于角度方差的异常检测算法基础上提出了一种多层次的高维数据异常检测算法（Hybrid outlier detection algorithm based on angle variance for High-dimensional data, HODA）。算法结合了粗糙集理论,分析属性之间的相互作用以排除影响较小的属性;通过分析各维度上的数据分布,对数据进行网格划分,寻找可能存在异常点的网格;最后对可能存在异常点的网格计算角度方差异常因子,筛选异常数据。实验结果表明,与ABOD, FastVOA和经典LOF算法相比,HODA算法在保证精测精度的前提下,运行时间显著缩短且可扩展性强。     

Nonlinear aircraft sensor fault reconstruction in the presence of disturbances validated by real flight data

This paper proposes an approach for Inertial Measurement Unit sensor fault reconstruction by exploiting a ground speed-based kinematic model of the aircraft flying in a rotating earth reference system. Two strategies for the validation of sensor fault reconstruction are presented: closed-loop validation and open-loop validation. Both strategies use the same kinematic model and a newly-developed Adaptive Two-Stage Extended Kalman Filter to estimate the states and faults of the aircraft. Simulation results demonstrate the effectiveness of the proposed approach compared to an approach using an airspeed-based kinematic model. Furthermore, the major contribution is that the proposed approach is validated using real flight test data including the presence of external disturbances such as turbulence. Three flight scenarios are selected to test the performance of the proposed approach. It is shown that the proposed approach is robust to model uncertainties, unmodeled dynamics and disturbances such as time-varying wind and turbulence. Therefore, the proposed approach can be incorporated into aircraft Fault Detection and Isolation systems to enhance the performance of the aircraft.