高层建筑结构CAD中剪力墙节点几何归并的点阵表示法和矢量表示法

对高层建筑结构CAD系统中剪力墙节点的归并进行了研究,给出了剪力墙节点的数据结构和生成算法,提出了剪力墙节点归并的点阵表示法和矢量表示法.该文建议的方法准确、高效,能够很好地实现体型复杂的高层三维空间结构剪力墙节点的归并.     

分布式结构化数据分页查询中的动态归并算法

本设计实现一种动态归并算法,主要应用在对于分布式结构化数据的跨节点跨表实时分页查询的业务场景中.分布式数据库中数据表都会被拆分为若干子表并存储于若干数据节点中,在对数据进行单表查询和多表查询时都需要进行数据的归并,本算法被设计用来处理中间数据的归并问题,在归并策略上采用了二路归并,从而保证了较高的节点并发度,使得归并的计算负载能够均衡地分配在各计算节点上;采用动态的归并过程而不是在任务一开始就确定节点之间的归并配对关系,确保算法的自适应性,避免了预先制定归并策略而可能导致的数据等待.实验结果表明随着参与归并的节点数量的提高,该算法执行效率明显优于单节点归并以及预先设定归并策略的多节点归并.     

一种非归并不确定XML小枝模式查询算法

针对目前不确定XML小枝模式查询需要存储大量中间结果和归并中间结果的情况,提出一种非归并不确定XML小枝模式查询算法ProTwigList。该算法查询之前通过Tag+Level流进行剪枝,以减少待处理节点的数目;并扩展了区间编码来对剪枝后剩余的普通节点进行编码,用一定规则对分布节点进行标识;查询时采用公共分布节点路径的方法处理分布结点,最后结合最低公共祖先节点的概率计算查询结果的概率值。理论分析和实验结果证明了ProTwigList算法的查询效率。     

Delaunay三角网生成算法的研究与实现

Delaunay三角网作为一种主要的数字地形模型表示法,经过二十多年来的研究,它的生成算法已趋于成熟。本文在简单回顾和评价了分割—归并法、逐点插入法、三角网生长法等三类主流算法的基础上,介绍并实现了一个融以上算法优点于一体,兼顾空间与时间性能的合成算法。     

一种高效的连续不确定XML小枝模式匹配算法

针对目前不确定XML小枝模式匹配算法均基于归并,易造成很大的空间和时间浪费问题,提出基于P-文档模型的连续不确定XML的非归并的小枝模式匹配算法.算法在节点入队列和出队列时分别进行过滤剪枝操作,减少待处理节点的个数,匹配过程使用相互关联的链表存储中间结果,不需要归并.理论分析与实验结果表明,该算法是一种高效的连续不确定XML查询算法.     

基于节点的线状图骨架提取算法研究

文中提出一种重视保留节点形位信息的线状图骨架提取算法,首先提出了截面序列的概念,光栅线索两侧的两个与线素走向近似垂直的轮廓点连线构成了一个截面,一组连续斋构成了一个截面序列,由不属于截面序列的轮廓点可计算出节点位置及拓扑结构关系;最后将通过同一节点并满足光滑性条件的截面序列归并为同源线索,并对其提取中心线得到的线状图骨架。     

一种新的基于流过滤的Twig模式匹配算法

针对传统XML文档小枝模式查询算法中,与模式树中标签名相同的节点均入内存,易造成很大的空间浪费问题,提出了一种新的算法—StreamFWM(StreamFilter Without Merging)。StreamFWM采用区间编码方式,依据节点间的结构关系过滤标签流中无用的中间节点,且不用归并,只用简单的栈和列表实现。实验结果证明,算法StreamFWM相比TwigStack在查询处理的性能上有所提高。     

基于traceroute的层析成像方法*

针对传统的网络拓扑识别方法（如traceroute）无法完成包含不协作节点的拓扑识别以及基于网络层析成像技术的拓扑识别方法的复杂性和不确定性问题,提出一种基于traceroute的层析成像技术的拓扑识别方法。该方法可通过提出的最小相似度聚类算法和匿名节点构造归并算法,将网络层析成像获得的拓扑信息与trace-route探测结果融合,构成最终的拓扑结构。NS2的仿真表明,该方法不仅可识别包含不协作节点的网络拓扑,且所使用的探测包的数量也大大减少。     

基于Hadoop分布式文件系统的单点问题的研究

从Hadoop分布式文件系统的架构出发,对Name Node节点存在的单点问题进行了分析与研究。在这个前提下,针对单点内存瓶颈问题,提出了一个小文件归并算法。此算法以Hadoop为基础,利用Hadoop分布式文件系统的特点,将归并后生成的大文件序列化到Hadoop分布式文件系统,很好地解决了小文件过多时Name Node单点内存瓶颈问题,并提高了系统的性能和可靠性。     

sTwig——一种基于流的XML小枝匹配算法

结构连接是处理XML查询的主要方法,目前已经有一系列有效的结构连接算法被提出.但是已经存在的结构连接算法,在处理仅含有祖先后裔边的小枝模式查询方面,会访问不必要的元素节点,提出了一种新的小枝匹配算法sTwig.sTwig算法基于"流"的思想,实现简单,不需要归并操作,且避免了对不必要元素节点的访问,TwigNME算法是目前在处理仅含有祖先后裔边的小枝查询方面表现最优的结构连接算法,通过实验,将sTwig算法与TwigNME算法进行了比较,实验结果表明sTwig算法在时间和空间上都存在优势.     

高层建筑结构CAD系统的数据模式

用面向对象的分析方法对高层建筑结构CAD系统进行了分析,通过对基本对象及其之间关系,作用的抽象,运用面向对象的设计方法建立了高层建筑 基本对象模型,采用面向对象技术和关系数据库相结合的方法,构造和初步建立了高层建筑结构的工程数据库,能够较好地解决高层建筑结构集成化CAD系统的数据组织和管理问题。     

Application of ADINA to nonlinear analysis of reinforced concrete shear walls with openings

This paper is concerned with application of ADINA to elasto-plastic analysis of the shear walls with openings. The authors analyzed the types of structures. One is the shear wall with many openings (the model of a secondary shield wall in nuclear power plant), on which scale model experiments were made. The other is the shear wall with openings in concrete rigid frame (the model of a shear wall in a building), on which parametric study was made.

In both cases, concrete is modeled using 8 nodes isoparametric 2 dimensional plane stress elements, reinforcing steels are modeled as truss elements. Concrete and elasto-plastic models are adopted for non-linear material model of concrete and reinforcing steel, respectively. The total numbers of nodes are 248–308, and that of 2D elements are 66–80.

Both analytical results are satisfactory from the view point of structural design. Close agreement to experimental results in the cracking load, crack extension, elasto-plastic stiffness and total strength was verified.     

基于神经网络的交互式自动获取知识的N－R方法

本文为解决知识系统构造过程中的瓶颈问题──知识获取，提出了一种基于神经网络ＮＮ（ｎｅｕｒａｌｎｅｔｗｏｒｋ）的自动获取多级推理产生式规则的Ｎ－Ｒ方法，该方法采用了特有的ＮＮ结构模型和相应的学习算法，使得ＮＮ在学习过程中动态确定隐层节点数的同时，也产生了样例集中没有定义的新概念，学习后的ＮＮ能用本文提出的转换算法转换成推理网络，最终方便地得到产生式规则集.     

垂直壁面行走机器人系统研制

本文对能在高层全封闭玻璃结构外墙上行走的垂直壁面行走机器人系统的总体组成，基本功能，实施方案和技术指标等作一介绍，并对系统中的关键技术进行了分析。     

全维观测器的新设计方法

本文结合直流电气传动系统,提出了全维状态及外扰观测器的新设计方法。它具有积木式的结构,并且易于调整。在理论上对降维观测器和全维观测器做了某种沟通。     

Intelligent design of shear wall layout based on graph neural networks

Structural scheme design of shear wall structures is important because it is the first stage that guides the project along its entire structural design process and significantly impacts the subsequent design stages. Design methods for shear wall layouts based on deep generative algorithms have been proposed and achieved some success. However, current generative algorithms rely on pixel images to design shear wall layouts, which have many model parameters and require intensive calculations. Moreover, it is challenging to use pixel image-based methods to reflect the topological characteristics of structures and connect them with the subsequent design stages. The above defects can be effectively solved by representing a shear wall structure in graph data form and adopting graph neural networks (GNNs), which have a robust topological-characteristic-extraction capability. However, there is no existing research using GNN methods in the design of shear wall structures owing to the lack of graph representation methods and high-quality structural graph data for shear walls. Therefore, this study develops an intelligent design method for shear wall layouts based on GNNs. Two graph representation methods for a shear wall structure—graph edge representation and graph node representation—are examined. A data augmentation method for shear wall structures in graph data form is established to enhance the universality of the GNN performance. An evaluation method for both graph representation methods is developed. Case studies show that the shear wall layout designed using the established GNN method is highly similar to the design by experienced engineers.     

Computer program for the analysis of asymmetric frame-shear wall structures

A computer program for the analysis of tall buildings comprising frames and shear walls coupled together is presented. Both static and free vibration analyses of the buildings of both uniform and nonuniform sections on either rigid or flexible foundation are considered. The governing equations are formulated through the continuum approach treating the structures as shear-flexure cantilevers. Both polynomial and transcendental displacement functions are employed to approximate the true displacement field. The method is shown to be simple yet powerful.     

Measurements of wall shear stress with the lattice Boltzmann method and staircase approximation of boundaries

We analyze the accuracy of wall shear stress measurements in lattice Boltzmann simulations that are based on a voxel representation of the geometry and staircase approximation of boundaries. Such approximations are commonly used in the context of lattice Boltzmann simulations, because they favor the use of simple and highly efficient data structures. We show on several two- and three-dimensional simulations that this low-order approximation of the boundary affects the accuracy of wall shear stress measurements in areas directly adjacent to the wall. A few lattice nodes apart from the wall, the accuracy is however largely improved, and can be considered to be compatible with the overall accuracy of a simulation at a given coarseness level of the grid. This result is interpreted as a justification for the use of walls with staircase shape, even in simulations with high expectations regarding the level of accuracy. Furthermore, we propose a novel method for establishing the direction of the wall normal, a quantity which is required for the computation of the wall shear stress. With this method, the wall normal is computed from local data that is extracted from the results of the fluid flow simulation. Owing to the nature of the flow dynamics, which tends to smooth out the asperities of the wall, the information on the wall orientation obtained in this way is observed to be of high quality.     

有界曲面剖分的边界递归法

本文针对有界曲面剖分提出了边界递归方法，引进了一种紧凑且富有方法特色的数据结构。在此基础上，外接曲面拟圆准则结合单侧性准则的使用，使得剖分优化过程加快，剖分总体速度得以提高，剖分效果也很好。剖分的输出数据又能方便地被有限元其它环节所处理，是一种合理可行的剖分方法。