一种新的空间多维关联规则模型与算法

空间对象具有自相关、连续性、多尺度等特点,导致空间关联规则挖掘与传统的统计关联规则挖掘不同,不存在统计的“事务”,挖掘更加复杂。本文用基于空间相关的影响域来创建“空间事务”,以代替传统关联规则挖掘中的事务,建立了一种新的应用于挖掘空间多维数据的空间多维关联规则模型(Spatial multidimensional association rules model,SMARM)。设计并实现了一种新的挖掘算法SMARBIA,用基于影响域、空间支持度等剪枝技巧,克服了空间多维关联规则挖掘过程中候选项目集庞大的困难。实验表明,该算法能有效地减少候选项目集而获得较好的性能。     

空间谱估计经典算法性能比较

空间谱估计是阵列信号处理的一个重要研究方向。空间谱估计理论与技术已日趋成熟,近几十年的经典谱估计技术包括：常规波束形成（CBF）、Capon谱估计、多重信号分类（MUSIC）、旋转不变子空间算法（ESPRIT）、最大似然（ML）、子空间拟合（SF）,及这些算法的扩展和变形。上述算法在各个分散的文章中均有具体深入的理论分析和研究,亦有类似的2种或3种算法的性能比较,但是针对这些所有算法的性能比较,就笔者所知尚无公开报道,而使工程实现时对算法的选择没有依据。本文对这些经典算法做了简介,列出各个算法的优缺点,并对性能进行仿真比较,能直观地得到各个算法的性能对比,给工程实现算法选择提供理论依据。     

在轨任务规划的新型开发方法

作为未来航天器智能化的发展方向,在轨任务规划的实现是一项涉及多个领域知识的复杂软件工程问题,需要与之匹配的先进开发方法。本文基于动态语言和嵌入式操作系统,提出了一种在轨任务规划的开发方法;从需求出发,调研了一组适用于在轨任务规划应用的开源基础库,涉及航天器任务规划、地理信息数据库、地图投影、坐标变换、几何拓扑关系求解、线性规划等多个方面;将这些领域的典型开源库作为中间件移植到高性能星载计算机和嵌入式操作系统上,并进行了功能测试。本文的工作能给后续在轨任务规划系统的设计和实现提供启发和帮助。     

数字波束形成技术在雷达中的应用

由于电磁工作环境的恶化和大量射频干扰的存在,在极低的信干噪比(SINR)条件下进行目标检测和信息提取十分困难.对于阵列系统,往往采用自适应数字波束形成(ADBF)技术,来抑制强干扰和方向性干扰对有用信号的影响.给出了数字波束形成器的基本原理及其工程实现.     

基于实测数据的海杂波时空相关性分析

海杂波的相关特性分为时间相关性和空间相关性,二者均与雷达系统参数、环境条件等多种因素有关。文章利用X波段雷达实测海杂波数据,重点研究了不同极化条件下海杂波的时间相关性、高低典型海况和不同入射余角条件下的海杂波距离向和方位向空间相关性。经大量实测数据验证表明,极化方式对海杂波强时间相关性影响较小,海杂波距离向空间相关性受海况影响较大、方位向相邻分辨单元空间相关性较弱,这些结论对于海杂波中目标检测方法优化设计具有重要意义。     

The Space within Fisherman's Folly: Playing with a Puzzle in Mereotopology

Abstract

In this paper we propose a spatial ontology for reasoning about holes, rigid objects and a string, taking a classical puzzle as a motivating example. In this ontology the domain is composed of spatial regions whereby a theory about holes is defined over a mereotopological basis. Within this theory we define a data structure, named chain, that facilitates a clear and efficient representation of the puzzle states and its solution.     

Spatio-Temporal Ontologies and Attention

ABSTRACT

This paper presents a new proposal for the design of spatio-temporal ontologies which has its origin in cognitively motivated spatial semantics. It is shown that selective attention not only plays a central role in the characterization of spatial relations but that the representation of attentional aspects also leads to the possibility of defining an ontological upper structure which systematically covers both the spatial and the temporal domain.     

Expert Algorithmic and Imagistic Problem Solving Strategies in Advanced Chemistry

Abstract

Visualization and imagistic reasoning appear central to expert practice in science; however, expert use of these strategies on authentic tasks has not been examined in detail. This study documents how science experts use both algorithms and imagistic reasoning to solve problems. Using protocol analysis, we report expert chemists' preferential use of algorithms for solving spatial problems and imagistic reasoning for deducing spatial transformations. We observed experts employ algorithms to solve the majority of spatial tasks while reserving imagistic strategies to solve a class of tasks that required translating between representations. Strategy used varied widely among experts and tasks.     

Interface Familiarity Restores Active Advantage in a Virtual Exploration and Reconstruction Task in Children

Abstract

Active exploration is reportedly better than passive observation of spatial displacements in real environments, for the acquisition of relational spatial information, especially by children. However, a previous study using a virtual environment (VE) showed that children in a passive observation condition performed better than actives when asked to reconstruct in reality the environment explored virtually. Active children were unpractised in using the input device, which may have detracted from any active advantage, since input device operation may be regarded as a concurrent task, increasing cognitive load and spatial working memory demands. To examine this possibility, 7–8-year-old children in the present study were given 5 minutes of training with the joystick input device. When compared with passive participants for spatial learning, active participants gave a better performance than passives, placing objects significantly more accurately. The importance of interface training when using VEs for assessment and training was discussed.