光学测量传感器的设计与分析

在非接触式检测技术中,基于主动视觉检测技术的光学测量传感器因能快速给出准确的结果而被广泛应用。这里设计的是一种基于激光三角法原理的光学测量传感器。该传感器由CCD摄像机和激光器组成,主要用于测量路面面型的整体情况。实验结果表明,此传感器在竖直方向的测量范围可达到1100mm,横向测量范围可达到1300mm;横向平均绝对误差约为0．05mm,最大绝对误差约为0．09mm;纵向平均绝对误差约为0．08mm,最大绝对误差约为0-2mm。     

基于Delaunay三角剖分的半径补偿新算法

合理的半径补偿算法能有效提高逆向工程的最终精度.在分析了现有半径补偿算法及其相应优缺点的基础上,针对三角网格法,通过Delaunay三角剖分思想的引入,提出了一种基于Delaunay三角剖分的半径补偿新算法,并对其中三角剖分的优化准则、边界点的处理等关键技术进行了详细的阐述,最后以增压器叶轮为例,实现了叶轮叶面测量数据的半径补偿.     

应急仿真中的碰撞检测算法研究

以应急仿真场景为背景,针对其中大量物体碰撞检测过程的需求,提出了层次包围盒与空间划分方法结合的实时碰撞检测算法,可以在完全不降低碰撞检测精度的同时,大大改进了仿真环境碰撞检测的效率,减轻了计算负担,提高了系统的实时性。     

基于Delaunay三角网的空间离群挖掘

空间离群是指非空间属性与其空间邻居显著不同的空间对象。空间数据的特殊性决定了空间离群挖掘需要充分考虑空间数据的特点,才能挖掘出有现实意义的离群。本文对现有主要的空间数据离群挖掘算法进行了研究分析,针对k-邻域法确定空间邻域的缺点,基于Delaunay三角网在表达空间邻近关系的有效性,通过构建Delaunay三角网确定空间邻域并生成空间权重矩阵,据此提出了基于Delaunay三角网的空间离群挖掘算法DT_SOF,并以实际生态地球化学数据进行实验检验。结果表明,算法具有较低的用户依赖性,能准确挖掘空间离群。     

一种改进的启发式离散化算法及应用

Nguyen S.H提出的布尔逻辑和粗糙集理论相结合的离散化算法是粗糙集理论中的离散化算法在思想上的重大突破。通过定义分界点来区分Nguyen S.H离散化算法中定义的断点对决策系统的分辨关系是否有贡献,并仅取分界点集作为初始断点集,使得初始断点数目较大幅度地降低,提出了一种改进的启发式离散化算法并应用于一个实际的决策系统的连续属性离散化。应用实例表明改进算法较大程度地减小了算法空间复杂性和时间复杂性,具有正确性和实用性。     

寻找电气故障行之有效的方法—“两分法”和“多分法”

本文介绍了“两分法”和“多分法”寻找电气故障的全部过程,掌握规律,集中目标,安全、排除故障,提出了设备的运转率。     

基于Delaunay图的反向最近邻查询

将查询点作为Delaunay图的一个生成点,利用Delaunay图的生成点与其邻接生成点之间的关系,在查询点的邻接生成点集(元素个数小于等于6)中计算数据集中给定点的反向最近邻。把伴随Delaunay图增量生成过程产生的Delaunay树作为查询索引结构,该结构能存储Delaunay图,在数据点插入和删除时维护Delaunay图的拓扑结构。     

求旅行商问题近似解的碰撞算法

提出通过寻找精确解的边获得旅行商问题(TSP)近似解的思想,并以该思想为指导,设计一种新的碰撞算法。对国际通用的TSPLIB中不同城市规模的数据进行测试表明,该算法可以得到与目前已知最优解或相同或相近的结果。该算法不仅可以计算小规模的TSP,而且同样适用较大规模的TSP。     

On the number of higher order Delaunay triangulations

Higher order Delaunay triangulations are a generalization of the Delaunay triangulation that provides a class of well-shaped triangulations, over which extra criteria can be optimized. A triangulation is order-k Delaunay if the circumcircle of each triangle of the triangulation contains at most k points. In this paper we study lower and upper bounds on the number of higher order Delaunay triangulations, as well as their expected number for randomly distributed points. We show that arbitrarily large point sets can have a single higher order Delaunay triangulation, even for large orders, whereas for first order Delaunay triangulations, the maximum number is 2ⁿ⁻³. Next we show that uniformly distributed points have an expected number of at least 2^{ρ₁n(1+o(1))} first order Delaunay triangulations, where ρ₁ is an analytically defined constant (ρ₁≈0.525785), and for k>1, the expected number of order-k Delaunay triangulations (which are not order-i for any i<k) is at least 2^{ρ_kn(1+o(1))}, where ρ_k can be calculated numerically.     

Distributed Video Coding using Turbo Trellis Coded Modulation

Distributed Video Coding (DVC) has been proposed for increasingly new application domains. This rise is apparently motivated by the very attractive features of its flexibility for building very low cost video encoders and the very high built-in error resilience when applied over noisy communication channels. Yet, the compression efficiency of DVC is notably lagging behind the state-of-the-art in video coding and compression, H.264/AVC in particular. In this context, a novel coding solution for DVC is presented in this paper, which promises to improve its rate-distortion (RD) performance towards the state-of-the-art. Here, Turbo Trellis Coded Modulation (TTCM), with its attractive coding gain in channel coding, is utilized and its resultant impact in both pixel domain and transform domain DVC framework is discussed herein. Simulations have shown a significant gain in the RD performance when compared with the state-of-the-art Turbo coding based DVC implementations.