Development of a Sensor Fusion Strategy for Robotic Application Based on Geometric Optimization

Fusion of multi-sensor information is an important technology, which is growing exponentially due to its tremendous application potential in many areas. Effective fusion of data from sensors is very critical in increasing an intelligent system's capability to accomplish complex tasks. Appropriate fusion technologies are needed to be developed specially when a system requires redundant sensors to be used. More the redundancy in sensors, more is the computational complexity for controlling the system and more is its intelligence level. This research presents a strategy developed for multiple sensor fusion, based on geometric optimization. Each sensor's uncertainty has been modeled using classical Lagrangian optimization techniques. However, the uniqueness and effectiveness of the present technique lies on the fact that starting from the optimized value as initial estimate the accuracy of the sensory information has further been improved up to any pre defined bounded range, by developing two architectures – FFA (fission–fusion architecture) and FDD (fusion in differential domain). Sufficient evidences and analyses have been provided in the paper to show its effectiveness in various applications.     

用于一维图像识别的支撑矢量机方法

研究了支撑矢量机的分类机理,并利用支撑矢量机对雷达目标一维像进行了识别.识别的结果表明了该方法的优越性,并显示它可以对残缺不全的样本进行识别.     

基于支持向量机的岩爆识别模型

岩爆的发生受多种因素的影响,这些因素关系错综复杂,以不同的特征和组合对岩爆产生综合影响,为科学、充分地利用现有的有限工程资料,本文从地下洞室围岩最大切向应力、岩石的单轴抗压强度和抗拉强度以及岩石冲击倾向指数四个主要因素出发,提出了基于支持向量机的岩爆识别模型,较好地解决小样本、非线性、高维数、局部极小点等的实际问题。     

基于移动端多天线系统的无线衰落信道模型

根据当前无线移动通信对多天线移动接收端性能仿真的要求，讨论并建立了适于空时信号处理的移动端矢量信道模型。通过分析无线移动信道特点。研究了影响接收信号幅度相位的诸多因素，建立了相应的数学模型。并对随机参数选取进行了探讨。其中考虑到移动端接收信号到达角扩散的特点，采用GBSB椭圆模型产生了适于移动端接收信号仿真的随机角度，改进了一种瑞利衰落生成算法。最后信道的数值仿真结果符合理论和经验。同时，通过TD-SCDMA系统链路级仿真，也证明了该信道模型的有效性。     

支持向量机的发展与应用

基于统计学习理论的支持向量机（SVM）是一种新型的机器学习方法，描述了SVM在模式识别和回归估计中的基本思想。在大训练样本情况下，用传统的方法求解SVM问题计算复杂，针对该问题探讨了一系列的SVM训练算法，并对其进行了比较。SVM由于其良好的泛化能力和全局最优性能．在模式识别、数据挖掘、非线性系统建模和控制等领域中展现出广泛的应用前景。     

Ultrahigh-performance FFTs for the CRAY-2 and CRAY Y-MP supercomputers

In this paper a set of techniques for improving the performance of the fast Fourier transform (FFT) algorithm on modern vector-oriented supercomputers is presented. Single-processor FFT implementations based on these techniques are developed for the CRAY-2 and the CRAY Y-MP, and it is shown that they achieve higher performance than previously measured on these machines. The techniques include (1) using gather/scatter operations to maintain optimum length vectors throughout all stages of small-to medium-sized FFTs, (2) using efficient radix-8 and radix-16 inner loops, which allow a large number of vector loads/stores to be overlapped, and (3) prefetching twiddle factors as vectors so that on the CRAY-2 they can later be fetched from local memory in parallel with common memory accesses. Performance results for Fortran implementations using these techniques demonstrate that they are faster than Cray's library FFT routine CFFT2. The actual speedups obtained, which depend on the size of the FFT being computed and the supercomputer being used, range from about 5 to over 300%.     

鱼雷质心配重纠偏技术的实现

为了快速找到最佳配重组合，使鱼雷在完成配重后的实际质心尽可能地接受理想位置，提出了实际质心矢量和配重矢量的概念，从而将静力学问题转化为相对容易处理的几何学问题。设计了配重矢量表，通过查表法可以快速地寻求最佳配重组合，并提出了最大配重误差的评价方法。     

基于LVQ网络在轴承座松动故障诊断中的应用

把汽轮机轴承座松动的相关频段上不同频率谱的谱峰能量值作为故障信号的训练样本输入到LVQ网络中去，并由LVQ网络产生训练后不同的权重向量。由于竞争层神经元的权重分布不同，则权重分布图也不同，可根据不同的权重分布图来确认和诊断汽轮机轴承座松动故障的不同程度。     

基于径向矢量的不变性形状分类方法

介绍一种利用径向矢量提取形状特征的方法，着重于分析二维图形的不变性，提出一种带有方向因子的径向矢量描述，该矢量以图形边界弧长为自变量，完整地刻画了图形特点，克服了非凸图形识别中存在的多义性，以归一化的特征矢量的输入，采用三层神经网络为分类器，在字符及军事目标不变性识别中取得了令人满意的结果。