Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.     

Efficient detection and tracking of moving objects in geo-coordinates

We present a system to detect and track moving objects from an airborne platform. Given a global map, such as a satellite image, our approach can locate and track the targets in geo-coordinates, namely longitude and latitude obtained from geo-registration. A motion model in geo-coordinates is more physically meaningful than the one in image coordinates. We propose to use a two-step geo-registration approach to stitch images acquired by satellite and UAV cameras. Mutual information is used to find correspondences between these two very different modalities. After motion segmentation and geo-registration, tracking is performed in a hierarchical manner: at the temporally local level, moving image blobs extracted by motion segmentation are associated into tracklets; at the global level, tracklets are linked by their appearance and spatio-temporal consistency on the global map. To achieve efficient time performance, graphics processing unit techniques are applied in the geo-registration and motion detection modules, which are the bottleneck of the whole system. Experiments show that our method can efficiently deal with long term occlusion and segmented tracks even when targets fall out the field of view.     

Exchange rate prediction with non-numerical information

Exchange rate prediction is an important yet challenging problem in financial time series analysis. Although the historical exchange rates can provide valuable information, other factors will also affect the prediction significantly. These factors could be numerical or non-numerical ones, which are related to politics, economics, military, or even market psychology. Previous automatic exchange rate prediction merely considers numerical data (or simply the historical rates) for predicting the next day value. In this paper, we show how to utilize and combine many related factors, both numerical and non-numerical factors, for exchange rate prediction. With an example on forecasting exchange rate between US dollar and Japanese yen, we investigate how to exploit the information from non-numerical factors. We then engage a novel integrated approach which successfully combines information obtained from both numerical and non-numerical factors. We show how to quantify the non-numerical fundamental information, provide details steps on how to construct single predictors on different kinds of information separately, and finally describe how to integrate these separate predictors. Experimental results showed that our method can achieve the Directional Symmetry (DS) accuracy of 86.96%, which is much higher than only exploiting numerical information.     

一种面向大规模P2P环境的成员管理机制

基于P2P的系统都严重依赖于底层节点之间形成稳定的具有某种特征的拓扑结构.成员管理通过为节点维护可以直接访问的邻居视图,能够实现根据上层应用的语义组织节点拓扑.然而大规模P2P环境下节点的动态加入/退出、网络状况的动态波动、节点上资源的动态更新以及庞大的节点规模对成员管理的抗扰动性和可伸缩性提出了更高的要求.许多典型的成员管理技术要么忽略了系统的扰动问题,要么忽略了系统的伸缩问题,因而无法在大规模P2P环境下获得良好的效果.提出了一种新的成员管理机制,综合考虑多种系统资源动态变化的因素,通过抗扰动的成员发现协议以及可伸缩的成员维护方法,保证即使在恶劣环境下仍能够为上层应用维护稳定正确的节点拓扑结构.最后通过实验验证了所提出的成员管理机制在大规模P2P环境下的有效性,通过对实验数据的分析得出的结论为进一步的研究工作奠定了基础.     

基于独立成分分析的反卷积新方法

基于独立成分分析方法进行了反卷积研究。独立成分分析算法中要求混合的观测信号不少于从这些观测信号中分离出的独立成分数。针对反卷积中混合的观测信号路数不满足上述条件的问题,提出了一种新的基于独立成分分析的反卷积方法,该方法通过对输入信号进行变换构造出新的观测信号,并对卷积模型进行非线性变换,采用独立成分分析算法实现解卷积混叠。仿真实验结果表明,该方法具有较好的性能,并能实现对信道瞬态响应信号的提取。     

基于图像的流体型风格画仿真研究

为了模拟艺术家的流体型风格绘画,提出了一种新的流体型风格化图像绘制算法。提取图像中的流体信息,通过随机扰动生成纹理参考图,再运用小波技术的融合方法,使得输出图像具有流体效果,最后采用色彩转换的方法,将颜色参考图中的色彩特征转换到绘制图像上。实验结果表明,该算法自动化程度高,复杂度低,渲染效果更接近于流体型风格绘画。     

无位置传感器的永磁同步电动机反演控制

设计了一种基于无位置传感器的永磁同步电动机(PMSM)反演控制器.该控制器包括基于定子电流检测的速度观测器与反演速度控制器.速度观测器代替位置传感器实现转速的在线估计;反演控制器的设计确保速度控制系统具有快速的转速跟踪与转矩响应.通过设计全局Lyapunov函数保证了控制系统的稳定性.仿真和试验结果表明:基于速度观测器...     

一种新型电静液作动飞机刹车系统

多电飞机已成为现代飞机的发展趋势,其刹车系统不再采用传统的液压刹车.提出一种用于多电飞机刹车系统的泵控和阀控相结合的电静液刹车系统.首先分析了电静液作动器的工作原理和数学模型,对电静液机构进行了单独的控制仿真;接着对飞机刹车系统进行数学建模,并将这种电静液作动飞机刹车系统进行控制仿真.仿真结果表明,基于电静液作动器的刹...     

An FFT Performance Model for Optimizing General-Purpose Processor Architecture

General-purpose processor (GPP) is an important platform for fast Fourier transform (FFT),due to its flexibility,reliability and practicality.FFT is a representative application intensive in both computation and memory access,optimizing the FFT performance of a GPP also benefits the performances of many other applications.To facilitate the analysis of FFT,this paper proposes a theoretical model of the FFT processing.The model gives out a tight lower bound of the runtime of FFT on a GPP,and guides the architecture optimization for GPP as well.Based on the model,two theorems on optimization of architecture parameters are deduced,which refer to the lower bounds of register number and memory bandwidth.Experimental results on different processor architectures (including Intel Core i7 and Godson-3B) validate the performance model.The above investigations were adopted in the development of Godson-3B,which is an industrial GPP.The optimization techniques deduced from our performance model improve the FFT performance by about 40%,while incurring only 0.8% additional area cost.Consequently,Godson-3B solves the 1024-point single-precision complex FFT in 0.368 μs with about 40 Watt power consumption,and has the highest performance-per-watt in complex FFT among processors as far as we know.This work could benefit optimization of other GPPs as well.     

基于行为监控的自适应动态信任度测模型

大规模分布式系统中的动态信任关系模型本质上是最复杂的社会关系之一,涉及假设、期望、行为和环境等多种因子,很难准确的定量表示和预测.将粗糙集理论和信息熵理论结合起来,应用于开放环境下动态构建基于行为数据监控与分析的信任关系度测(度量与预测)模型.该方法直接从分析传感器监测到的动态数据入手,针对影响信任的多个度测指标进行自适应的数据挖掘与知识发现,从而改变了传统的信任关系建模思路,跳出了传统信任关系建模过程中各种主观假设的束缚,并克服了传统模型对多维数据处理能力不足的问题.实验结果表明,与已有模型相比,新模型能够快速准确地实现开放分布式环境下实体的可信性判别,而且具有良好的行为数据规模的扩展能力.