不完备区间值信息系统中邻域粗集模型

目前对未知区间值的研究还处于起步阶段。以包含复杂的遗漏型未知区间值不完备信息系统为研究对象,提出了一种基于灰格运算和Hausdorff距离的新的邻域关系。在此基础上,依次提出了邻域关系、最大相容类和邻域系统3种灰色粗集模型。进一步讨论了3种灰色粗集模型之间的上、下近似空间,以提高近似空间的精确度,并用实例进行了分析及验证。     

Extending metric multidimensional scaling with Bregman divergences

Sum of weighted square distance errors has been a popular way of defining stress function for metric multidimensional scaling (MMDS) like the Sammon mapping. In this paper we generalise this popular MMDS with Bregman divergences, as an example we show that the Sammon mapping can be thought of as a truncated Bregman MMDS (BMMDS) and we show that the full BMMDS improves upon the Sammon mapping on some standard data sets and investigate the reasons underlying this improvement. We then extend a well known family of MMDS, that deploy a strategy of focusing on small distances, with BMMDS and investigate limitations of the strategy empirically. Then an opposite strategy is introduced to create another family of BMMDS that gives increasing mapping quality. A data preprocessing method and a distance matrix preprocessing are introduced.     

Metric concepts and implications in describing compositional changes for world river's water chemistry

The paper is designed to give the reader an outline that is useful for understanding the importance of distance, as a metric concept, and its implications when compositional (geochemical) data are managed from a statistical point of view in a given sample space. Application examples are shown by considering the construction of confidence regions and mixing models. The analyzed data are related to the chemistry of the most important rivers of the world as referring to the GEMS/WATER Global Register of River Inputs when each sample (river) is represented as a composition. A compositional vector of d parts, x=[x₁,x₂,…,x_d], is defined as a vector in which the only relevant information is contained in the ratios between its components. All the components of the vector are assumed positive and are called parts (variables), while the whole compositional vector, with the sum of the parts equal to a constant, represents the composition. In this case data are not represented by variables free to vary from −∞ to +∞ within a Euclidean space but occupy a restricted part of it called the simplex. The d-part simplex, S^d, is a subset of a d-dimensional real space. In this context the metric of the R space, with the definition of basic algebraic operations and of inner product, norm and distance, thus giving an Euclidean vector space structure, cannot be applied since the scale is relative and not absolute.     

Comparing and aggregating partially resolved trees

Partially-resolved-that is, non-binary-trees arise frequently in the analysis of species evolution. Non-binary nodes, also called multifurcations, must be treated carefully, since they can be interpreted as reflecting either lack of information or actual evolutionary history. While several distance measures exist for comparing trees, none of them deal explicitly with this dichotomy. Here we introduce two kinds of distance measures between rooted and unrooted partially-resolved phylogenetic trees over the same set of species; the measures address multifurcations directly. For rooted trees, the measures are based on the topologies the input trees induce on triplets; that is, on three-element subsets of the set of species. For unrooted trees, the measures are based on quartets (four-element subsets). The first class of measures are parametric distances, where there is a parameter that weighs the difference between an unresolved triplet/quartet topology and a resolved one. The second class of measures are based on the Hausdorff distance, where each tree is viewed as a set of all possible ways in which the tree can be refined to eliminate unresolved nodes. We give efficient algorithms for computing parametric distances and give conditions under which Hausdorff distances can be calculated approximately in polynomial time. Additionally, we (i) derive the expected value of the parametric distance between two random trees, (ii) characterize the conditions under which parametric distances are near-metrics or metrics, (iii) study the computational and algorithmic properties of consensus tree methods based on the measures, and (iv) analyze the interrelationships among Hausdorff and parametric distances.     

基于无线传感器网络的DV-Hop定位算法的改进

在无线传感器网络的定位算法中,距离向量-跳段(DV-Hop)是典型的无需测距算法之一,但其定位精度不高。为提高算法的定位精度,通过对DV-Hop算法的理论分析,找出该算法产生误差的主要原因。针对该算法存在的缺陷,新算法巧妙利用无线信号在同种介质中传播速度的不变性,用计数器来测量锚节点间的传送时间以及锚节点与未知节点间的传送时间,并利用该时间比例来修正未知节点的估计距离。通过仿真实验表明:新算法减少了定位误差,提高了定位精度。     

基于PIC单片机的超声波测距系统

利用超声波测量距离是一种有效的非接触式测距方法,本文介绍了PIC单片机控制的超声波测距系统的原理,给出系统的硬件构成和软件控制流程,并在数据处理中采用了温度补偿修正。此系统具有易控制、工作可靠,测距精度高的优点。     

基于Hausdorff度量的快速图象匹配算法

借助于计算机形态学的膨胀运算,文章提出了一种基于Hausdorff距离的快速图象匹配算法.Hausdorff距离相似性度量简化为膨胀和累加运算两个步骤,与传统的Hausdorff距离计算方法相比,具有简单、快速的特点.仿真结果验证了所提出算法的有效性.     

基于最佳圆弧样条逼近的快速距离曲面计算

距离曲面是一种常用的隐式曲面,它在几何造型和计算机动画中具有重要的应用价值,但以往往在对距离曲面进行多边形化时速较慢,为了提高点到曲线最近距离计算的效率,提出了一种基于最佳圆弧样条逼近的快速线骨架距离曲面计算方法,该算法对于一条任意的二维NURBS曲线,在用户给定的误差范围内,先用最少量的圆弧样条来逼近给定的曲线,从而把点到NURBS曲线最近距离的计算问题转化为点到圆弧样条最近距离的计算问题,由于在对曲面进行多边形化时,需要大量的点到曲线最近距离的计算,而该处可以将点到圆弧样条最近距离很少的计算量来解析求得,故该算法效率很高,该实验表明,算法简单实用,具有很大的应用价值。     

Using narratives in conferences to improve the CMC learning environment

Abstract This paper reports on the use of short stories in Internet discussions to promote student learning. It describes off-campus teacher education students CMC discussions of short stories concerning issues in human development. The content of students' discussions is analysed, as is their perceptions of the value of the discussion stories. The results indicate that the use of narratives can improve the social environment of online conferences and contribute to collaborative student learning.     

A model-based hand gesture recognition system

This paper introduces a model-based hand gesture recognition system, which consists of three phases: feature extraction, training, and recognition. In the feature extraction phase, a hybrid technique combines the spatial (edge) and the temporal (motion) information of each frame to extract the feature images. Then, in the training phase, we use the principal component analysis (PCA) to characterize spatial shape variations and the hidden Markov models (HMM) to describe the temporal shape variations. A modified Hausdorff distance measurement is also applied to measure the similarity between the feature images and the pre-stored PCA models. The similarity measures are referred to as the possible observations for each frame. Finally, in recognition phase, with the pre-trained PCA models and HMM, we can generate the observation patterns from the input sequences, and then apply the Viterbi algorithm to identify the gesture. In the experiments, we prove that our method can recognize 18 different continuous gestures effectively. Received: 19 May 1999 / Accepted: 4 September 2000