Computational Problem Solving in Spatial Substrates -- A Cognitive Systems Engineering Approach

The ability to perform spatial tasks is crucial for everyday life and of great importance to cognitive agents such as humans, animals, and autonomous robots. A common artificial intelligence approach to accomplish spatial tasks is to represent spatial configurations and tasks in form of detailed knowledge about various aspects of space and time. Suitable algorithms then use the representations to compute solutions to spatial problems. In comparison, natural embodied and situated agents often solve spatial tasks without detailed knowledge about geometric, topological, or mechanical laws; they directly relate actions to effects that are due to spatio-temporal affordances in their bodies and environments. Accordingly, we propose a paradigm that makes the spatio-temporal substrate an integral part of the engine that drives spatial problem solving. We argue that spatial and temporal structures in body and environment can substantially support (and even replace) reasoning effort in computational processes: physical manipulation and perception in spatial environments substitute formal computation. While the approach is well known – for example, we employ diagrams as spatial substrate for geometric problem solving and maps for wayfinding – the underlying principle has not been systematically investigated or formally analyzed as a paradigm of cognitive processing. Topology, distance, and orientation constraints are all integrated and interdependent in truly 2- or 3-dimensional space. Exploiting this fact may not only help overcome the need for acquiring detailed knowledge about the interrelationships between different aspects of space; it also can point to a way of avoiding exploding computational complexity that occurs when we deal with these aspects of space in complex real-world scenarios. Our approach employs affordance-based object-level problem solving to complement knowledge-level formal approaches. We will assess strengths and weaknesses of the new cognitive systems paradigm.     

基于随机词汇迭代模型的POI分类检索

从特征选择、局部区域划分和词汇语义相似性计算入手,利用随机词汇迭代模型(random terms iterativemodel,RTIM)进行海量兴趣点(point of interest,POI)文本分类.通过词汇频度、集中度和离散度方法筛选出特征词汇;依据文本与各POI类别间的相似度进行局部区域划分;在每个局部区域内基于词汇在文本中的排列顺序构建词频向量,基于词频向量中词频的随机删除和重构,获取特征映射矩阵;通过特征映射矩阵将文本转为特征向量,并采用SVM分类器进行POI文本分类.实验证明,该方法有效提升了POI文本分类准确性和覆盖率.     

三次样条构造的伪逆解法

为了提高三次样条构造的可行性, 基于矩阵的伪逆方法, 提出一种不依赖额外约束条件的三次样条构造的伪逆解法。该解法通过求解出三次样条二阶导数的最小范数解, 从而较好地构造出三次样条函数。理论分析及数值实验结果表明该三次样条构造的伪逆解法具有简单、有效等特点。综合分析各种构造解法的性质, 对各种三次样条构造解法进行归类比较, 为在实际工程计算应用中选择合适的三次样条构造解法提供了指导方向。     

基于各向异性滤波和空间FCM的MRI图像分割方法

针对具复杂目标和边界模糊的MRI图像中多感兴趣区域的分割中分割MRI图像软组织难的问题, 提出了一种基于各向异性滤波和空间模糊C-均值聚类(SFCM)的MRI图像分割方法; 用新型各向异性滤波对图像进行预处理, 解决去噪平滑的同时弱化图像细节的问题; 用邻域空间信息设计空间函数, 改进传统FCM的目标函数; 用图像的空间信息实现图像各目标准确分类、有效解决孤立区域的正确归类问题, 进而使分割区域完整; 用直方图拟合曲线初始化分类数和初始聚类中心, 加快算法迭代到最优解, 进而减少运行时间。通过实验证实了各向异性滤波和空间FCM的MRI图像分割方法的综合应用显著提高了分割灰度重叠、目标不连续和目标边界模糊的MRI图像的分割效果。     

空间资源索引与top-k查询研究

以空间资源索引结构R-tree为基础, 考虑人们对空间资源能力指标的要求, 利用道路网络模型进行空间距离的计算, 提出了一种包含能力维度信息的空间资源索引结构和top-k查询算法, 并与传统遍历算法进行对比实验, 验证了查询算法的有效性。     

基于SOPC的高精度超声波温度计设计

根据在介质中超声波的传播速度随温度变化而变化的特点为设计原理,以基于Nios II处理器软核的可编程系统级芯片(SOPC)为控制核心,设计了高精度超声波温度计。在SOPC上同时实现了高频信号发生器模块、高速信号电路控制模块、信号自动采集控制模块以及Nios II软核处理器模块,缩小了体积,并降低了成本。传播时间的精确测量采用软件细分插补算法,经过理论分析和实验验证,该方法能够达到ns级超声波传播时间的测量,使设计的超声波温度计能够实现分辨率优于0.001℃的温度测量。     

Efficient Data Access for Location-Dependent Spatial Queries

When the mobile environment consists of light-weight devices, the loss of network connectivity and scarce resources, e.g., low battery power and limited memory, become primary issues of concern in order to efficiently support portable wireless devices. In this paper, we propose an index-based peer-to-peer data access method that uses a new Hierarchical Location-Based Sequential （HLBS） index. We then propose a novel distributed Nearest First Broadcast （NFB） algorithm. Both HLBS and NFB are specifically designed for mobile peer-to-peer service in wireless broadcast environments. The system has a lower response time, because the client only contacts a qualified service provider by accessing the HLBS and quickly retrieves the data to answer the query by using NFB. HLBS and NFB design the index for spatial objects according to the positions of individual clients and transfer the index in the order arranged so that the spatial query can be processed even after the user tunes the partial index. Hence, this design can support rapid and energy-efficient service. A performance evaluation is conducted to compare the proposed algorithms with algorithms based on R-tree and Hilbert-curve air indexes. The results show that the proposed data dissemination algorithm with the HLBS index is scalable and energy efficient in both range queries and nearest neighbor queries.     

基于包围盒和空间分解的碰撞检测算法

文中提出一种基于包围盒和空间分解的碰撞检测算法,用以解决软体的碰撞检测。算法使用AABB包围盒做初步检测,确定可能发生碰撞的物体。再根据包围盒的重叠情况缩小可能发生碰撞的区域,利用哈希表作为数据储存结构进行空间分解,将物体包围盒重叠区域的基本几何元素的空间网格映射到哈希表中,将碰撞区域缩小到基本几何元素,最后用基元碰撞检测找出具体碰撞点。由于前期AABB包围盒的处理减少了空间分解阶段需要映射的基本几何元素数量,该算法具有较高的运算速度。     

基于业务驱动的“一张图”数据动态更新机制研究

在分析国土资源管理业务模型及"一张图"数据库关系的基础上,重点阐述基于业务驱动的"一张图"数据动态更新机制的原理、实现技术,并在成都市开展应用示范。本文提出基于业务并借助数据捕获、ETL工具实现"一张图"数据动态更新的新思路,为国土资源动态监管、分析核查与决策、防灾减灾提供数据支撑和信息服务。     

考虑土体强度参数深度依赖性的非平稳随机场边坡滑动深度和体积分析

基于随机有限元方法（RFEM）和Monte-Carlo模拟,建立了能够模拟具有深度依赖性的土体强度参数边坡或具有软弱夹层边坡的非平稳随机场模型。通过辐射扫描方法获取最危险滑动面,得到非平稳随机场边坡滑动深度和滑动体积。将该方法与均质边坡和平稳随机场边坡获得的滑动深度和滑动体积进行对比,研究土体强度参数深度依赖性的非平稳随机场边坡的不同坡度和各向异性对滑动深度和滑动体积的影响。结果表明：基于RFEM的非平稳随机场滑动深度和滑动体积结果为地质灾害风险及后果评估提供了一个参考分析的新角度;参数的深度依赖性程度越高(变化率b越大),则边坡平均滑动深度和滑动体积越小;在大坡度滑坡风险后果评估中,可以基于均质边坡分析结果进行初步预估;在非平稳随机场中,随着各向异性的增大,所有坡度边坡的滑动体积变异系数增大;不同坡度下,水平方向的相关程度越大,则土坡内软弱夹层的不确定性越强,滑动体积的变异系数也不断增大。