家谱关系的元图表示

针对已有家谱图展示形式中,边的数量随节点数的增加而快速增长导致表现力下降的问题,提出以元图的形式对家谱进行可视化展示。家谱的元图表示形式中,产生集表示家族中所有人员组成的集合,每条边仅表示“父母〖CD*2〗子女”关系,不必存在表示夫妻关系的边。其中,边为产生集的两个子集的有序对,分别为具有夫妻关系的两个节点组成的入点集和单个的孩子节点组成的出点集。实验结果表明,在数据相同的情况下,家谱的元图可视化形式中边的数量约为通常的展示形式中边数量的一半,使得展示结果清晰易懂。同时,对家谱的数学建模、可视化研究以及家谱信息系统的改造具有一定指导作用。     

元图的存储结构及其搜索算法

给出了元图在计算机中的实现, 提出元图的关联矩阵和邻接表存储结构, 并给出了元图对应两种存储结构的广度优先搜索算法（BFS）和深度优先搜索算法（DFS）。最后, 对给出的元图的存储结构和搜索算法进行了分析, 验证了提出的存储结构和搜索算法的可行性。     

IT系统结构关系可视化系统

IT系统结构关系可视化系统是一个利用三维可视化技术构建业务至IT设施的可视化管理模型,以立体、仿真、实时互动的方式,呈现系统业务、系统、流程以及支撑的物理设备之间关系的Web系统[1].可以直观的展示不同层次应用节点的业务关系、应用与组件之间所属关系、逻辑组件与物理实体的对应关系、应用业务流向关系、系统监控信息和按条件显示拓扑等,并且支持针对某一节点发生故障的定位及影响分析、故障回溯功能.可视化系统数据来源于配置管理模块、后端使用Struct2+Hibernate4+Spring3技术,前端可视化模块使用Unity3D引擎.整个可视化系统具有使用方便、扩展性好、面向服务、支持配置化、数据呈现直观等特性.     

基于元图的工作流建模分析方法

工作流可以有效的规范业务流程、监控流程的执行，而且能够根据客户的需求创建出新流程模型。工作流的建模过程存在一定的复杂性，它要求创建的新模型具有可视化和可操作的特点，同时模型的有效性（即正确性与合理性）也是相当重要的。将元图运用于工作流建模和分析过程，以具体的业务流程为背景，逐步分析隐含在流程中的深层次问题，找出信息元素、活动和资源之间的相互关系，从而确保工作流模型的有效性。     

家谱文本中实体关系提取方法研究

实现家谱资源的高效的组织和利用,需要从非结构化的家谱文本中提取实体及关系,进行结构化的表示。实体和关系的提取通常被作为序列标注任务来解决,输入的句子被映射到标签序列。针对家谱文本中实体和关系高度密集、关系重叠很常见的特点,该文构建了相应的概念模型来指导整个提取过程。在序列标注部分,该文在真实数据上检验了常用的深度学习模型的表现。实验结果显示,BERT-BiLSTM-CRF模型的精确率、召回率和F₁值均优于所对比的其他模型,该文所提出的方法能够有效地解决家谱文本中的实体关系提取问题。     

成员系统模型及其归约

本文提出了一种基于有限关联团块观点的成员系统模型，主要针对不确定性情况给出了其归约操作的形式化描述，讨论了它与产生式系统的关系，并根据该模型及其归约过程，成功地设计实现了一种并发成员系统程序计算语言，对研究支持多种ＡＩ问题求解的模型及其语言做了有益的尝试。     

面向入侵检测的元图神经网络构建与分析

网络入侵样本数据特征间存在未知的非欧氏空间图结构关系, 深入挖掘并利用该关系可有效提升网络入侵检测方法的检测效能. 对此, 设计一种元图神经网络(Meta graph neural network, MGNN), MGNN能够对样本数据特征内部隐藏的图结构关系进行挖掘与利用, 在应对入侵检测问题时优势明显. 首先, 设计元图网络层(Meta graph network layer, MGNL), 挖掘出样本数据特征内部隐藏的图结构关系, 并利用该关系对样本数据的原始特征进行更新; 然后, 针对MGNN存在的图信息传播过程中父代信息湮灭现象提出反信息湮灭策略, 并设计了注意力损失函数, 简化MGNN中实现注意力机制的运算过程. KDD-NSL、UNSW-NB15、CICDoS2019数据集上的实验表明, 与经典深度学习算法深度神经网络 (Deep neural network, DNN)、卷积神经网络(Convolutional neural network, CNN)、循环神经网络(Recurrent neural network, RNN)、长短期记忆(Long short-term memory, LSTM)和传统机器学习算法支持向量机(Support vector machine, SVM)、决策树(Decision tree, DT)、随机森林(Random forest, RF)、K-最近邻(K-nearest neighbor, KNN)、逻辑回归(Logistic regression, LR)相比, MGNN在准确率、F1值、精确率、召回率评价指标上均具有良好效果.     

基于Web的信息可视化系统的设计与实现

可视化技术的发展能够帮助我们加深对大规模复杂数据和信息间相互关系的理解,因此人们针对不同领域开发出各种功能的可视化系统。以高等级生物安全实验室协同工作平台CBL项目为背景,根据生物安全实验室环境监控和生物安全知识库的可视化需求,设计并开发了CBL信息可视化系统。该系统采用基于Web的B/S三层架构,利用MVC设计模式,根据需求分别采用合适的可视化模型开发可视化组件,并根据可视化界面与用户交互的不同方式采用相应的Web客户端技术,方便用户更加高效地获取和理解信息。     

基于二叉树的电子家谱设计方法

家谱用于记录家族成员情况与关系,具有重要的学术研究价值和实用价值。本文结合生活中实际情况,设计一种基于二叉树的电子家谱管理系统。实现了对家族所有资料的存储分析,具备家谱存储、更新、查询、统计等功能。     

基于编译技术的可视化计算系统设计

针对编译技术中的词法分析与语法分析,利用Visual Studio2008开发工具设计出一套根据算符优先关系进行优先级比较的可视化计算系统。在这套系统中,可对输入的数据进行词法分析、语法分析,语义分析并计算出结果。     

Multilevel neural net adaptive models using the metagraph approach

The paper considers adaptive models enabling real-time processing of data flows. The drawbacks of current algorithms are examined. A method that combines advantages of deep learning, self-organizing neural nets and the metagraph approach is offered for designing adaptive models. A part of the method is realized, data clustering experiments are carried out and experimental results are analyzed.     

Visualization of trends using RadViz

Data mining is sometimes treating data consisting of items representing measurements of a single property taken in different time points. In this case data can be understood as a time series of one feature. It is no exception when the clue for evaluation of such data is related to their development trends as observed in several successive time points. From the qualitative point of view one can distinguish three basic types of behaviour between two neighbouring time points: the value of the feature is stable (remains the same), it grows or it falls. This paper is concerned with identification of typical qualitative development patterns as they appear in the windows of given length in the considered time-stamped data and their utilization for specification of interesting subgroups.     

利用OpenGL实现连铸模拟的可视化

连铸技术是借助有效的冷却控制,使钢水连续并逐渐转变为钢坯的工艺过程,连铸坯内钢液的流动和传热直接影响着铸坯的表面质量及内部质量。从可视化角度研究了连铸结晶器内温度场和流场的显示原理及方法,利用Visual C＋＋和OpenGL混合编程技术加以实现,并将屏幕上显示的图像保存为位图文件。     

Visualization and analysis using virtual reality

Current virtual reality technologies have not yet crossed the threshold of usability. Not surprisingly, VR has so far shown more promise than practical applications. Yet the promise looks bright for fields such as data visualization and analysis. For such problems, VR offers a natural interface between human and computer that will simplify complicated manipulations of the data. It also provides an opportunity to rely on the interplay of combined senses rather than on a single or even dominant sense. Still, we cannot yet say whether VR is better than other visualization and analysis approaches for certain classes of data and, if so, by how much. The payoff will come not for those applications or tasks for which VR is merely better, even if significantly, but for those applications or tasks for which it offers some unique advantage unavailable otherwise. To answer these questions, we embarked on a multipronged program involving the Graphics, Visualization. and Usability (GVU) Center, the Office of Information Technology Scientific Visualization Lab. and other research groups at Georgia Tech. Integration is mandatory since these questions involve basic considerations: how immersive environments affect user interfaces and human-computer interactions; the ranges and capabilities of sensors; computer graphics and the VR optical system; and applications' needs. We describe some of our results     

Visualization of geologic stress perturbations using Mohr diagrams

Huge salt formations, trapping large untapped oil and gas reservoirs, lie in the deepwater region of the Gulf of Mexico. Drilling in this region is high-risk and drilling failures have led to well abandonments, with each costing tens of millions of dollars. Salt tectonics plays a central role in these failures. To explore the geomechanical interactions between salt and the surrounding sand and shale formations, scientists have simulated the stresses in and around salt diapirs in the Gulf of Mexico using nonlinear finite element geomechanical modeling. In this paper, we describe novel techniques developed to visualize the simulated subsurface stress field. We present an adaptation of the Mohr diagram, a traditional paper-and-pencil graphical method long used by the material mechanics community for estimating coordinate transformations for stress tensors, as a new tensor glyph for dynamically exploring tensor variables within three-dimensional finite element models. This interactive glyph can be used as either a probe or a filter through brushing and linking.     

Visualization of scientific video data using KL decomposition

Fast methods are developed for visualizing and classifying certain types of scientific video data. These techniques, which are based on Karhunen-Loe`ve (KL) decomposition, find a best coordinate system for a data set. When the data set represents a temporally ordered collection of images, the best coordinate system leads to approximations that are separable in time and space. Practical methods for computing this best coordinate system are discussed, and physically significant visualizations for experimental video data are developed. The visualization techniques are applied to two experimental systems-one from combustion and the other from neurobiology-to show how relevant information can be quickly extracted from video data. These techniques can be integrated into the video acquisition process to provide real-time feedback to the experimentalist during the operation of an experiment     

Visualization of A-Train vertical profiles using Google Earth

Online tools, such as those pioneered by Google Earth (GE), are changing the way in which scientists and the general public interact with three-dimensional geospatial data in a virtual environment. However, while GE provides a number of features to facilitate geospatial data visualization, there is currently no readily available method for rendering vertical geospatial data derived from Earth—viewing remote sensing satellites as an orbit curtain seen from above. Here, a solution (one of many possible) is demonstrated to render vertical profiles of atmospheric data from the A-Train satellite formation in GE, using as a proof-of-concept data from one of the instruments—the NASA CloudSat satellite. CloudSat carries a nadir-viewing Cloud Profiling Radar that produces data revealing the vertical distribution of cloud characteristics along the satellite track. These data are first rendered into a long vertical image for a user-selected spatial range through the NASA Goddard Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) system (http://giovanni.gsfc.nasa.gov/). The vertical image is then chopped into small slices representing 15 s of satellite time (～103 km long ground distance). Each small piece, as a texture, is fed into a generalized COLLAborative Design Activity (COLLADA) three-dimensional (3-D) model. Using the satellite orbit coordinates, the repeated 15 s “3-D model slices” are spliced together to form a vertical “curtain” image in Keyhole Markup Language (KML) format. Each model slice is geolocated along the CloudSat orbit path based on its size, scale and angle with the longitude line that are precisely calculated on the fly. The resulting vertical cloud data can be viewed in GE, either transparently or opaquely, superimposed above the Earth's surface with an exaggerated vertical scale. Since CloudSat is just a part of the A-Train formation, the full utility of this tool can be explored within the context of the A-Train Data Depot (ATDD, http://disc.gsfc.nasa.gov/atdd/) and the corresponding Giovanni instance (http://disc1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=atrain). The latter portal allows scientists and the general public to access and visualize complex A-Train datasets without having to delve into data formats specific to a given mission.