基于Java3D的空间关联规则可视化原理与实现

空间数据挖掘可视化是空间数据挖掘研究的一个重要方面。可视化技术充分利用了图形和图像的表达能力以及人对于色彩和空间的敏锐的感知能力,使人机有机地融合在一起。本文在简述空间关联规则可视化基本需求的基础上,提出了空间关联规则可视化的一般方法,并进行用Java3D技术开发空间关联规则可视化工具的应用研究。重点在于分析利用Java3D技术进行空间关联规则可视化的基本原理和方法,并给出核心部分流程。最后将该技术应用于两个具体实例中,并给出实现的空间关联规则可视化界面。     

面向语义的精简化多关系频繁模式发现方法

多关系频繁模式发现能够直接从复杂结构化数据中发现涉及多个关系的复杂频繁模式,避免了传统方法的局限。有别于主流基于归纳逻辑程序设计技术的方法,提出了基于合取查询包含关系的面向语义的精简化多关系频繁模式发现方法,具有理论与技术基础的新颖性,解决了两种语义冗余问题。实验表明,该方法在可理解性、功能、效率以及可扩展性方面具有优势。     

Modeling Skewness in Vulnerability Discovery

A vulnerability discovery model attempts to model the rate at which the vulnerabilities are discovered in a software product. Recent studies have shown that the S‐shaped Alhazmi–Malaiya Logistic (AML) vulnerability discovery model often fits better than other models and demonstrates superior prediction capabilities for several major software systems. However, the AML model is based on the logistic distribution, which assumes a symmetrical discovery process with a peak in the center. Hence, it can be expected that when the discovery process does not follow a symmetrical pattern, an asymmetrical distribution based discovery model might perform better. Here, the relationship between performance of S‐shaped vulnerability discovery models and the skewness in target vulnerability datasets is examined. To study the possible dependence on the skew, alternative S‐shaped models based on the Weibull, Beta, Gamma and Normal distributions are introduced and evaluated. The models are fitted to data from eight major software systems. The applicability of the models is examined using two separate approaches: goodness of fit test to see how well the models track the data, and prediction capability using average error and average bias measures. It is observed that an excellent goodness of fit does not necessarily result in a superior prediction capability. The results show that when the prediction capability is considered, all the right skewed datasets are represented better with the Gamma distribution‐based model. The symmetrical models tend to predict better for left skewed datasets; the AML model is found to be the best among them. Copyright © 2013 John Wiley & Sons, Ltd.     

疫情可视化设计中的数据分析与表达方法研究——以“重庆市新型冠状病毒肺炎疫情数据可视化分析”为例

围绕疫情数据可视化这一主题,研究如何针对突发事件的海量数据进行数据分析和可视化表达。在数据量大、内容繁杂的背景下,基于可视化设计的方法论,探讨数据可视化在突发公共卫生事件数据报道中的优势,并以“重庆市新型冠状病毒肺炎疫情数据可视化分析”设计为案例,具体分析数据可视化从设计方法到结论的过程。分析、整理适用于突发公共卫生事件下的疫情可视化设计数据分析和表达方法,为突发公共事件的设计介入、数据可视化的设计方法提供一定的补充。     

基于虚拟现实技术的测量数据可视化方法

在从数据可视化的角度对测量数据进行分类的基础上,对两种重要类型的测量数据给出了三维建模方法。然后利用CAVE技术和3D技术,结合以映射、绘制为主线的数据可视化基本流程,提出了一种具有沉浸感和立体感的通用型测量数据可视化方案。技术方案以建立三维模型为基础,以对视图的控制为核心,以场景设置为辅助,实现并验证了测量数据可视化。     

基于扫描数据可视化的超声换能器参数测量

超声探头是超声无损检测中的关键部件,其参数直接影响到对超声检测系统性能的评价与校准。在自主建立的水浸超声声场自动扫查系统基础上,以标准ASTM E1065-99为依据,利用水听器法对聚焦超声换能器声场进行扫描,并将数据可视化。在数据可视化的基础上,对声场中若干重要参数进行了测量。实验结果表明,测量结果与理论计算结果吻合程度良好,所实现的声场自动扫查系统能够真实反应出换能器空间的声场分布,能够为超声换能器应用特性的准确评价提供可信的数据。     

无网格方法数值结果的可视化方法与实现

科学计算可视化是科学计算中不可缺少的一个组成部分,其主要任务是将数值模拟产生的大量复杂的数据信息通过计算机技术转换成图形、图像信息。无网格方法是一种基于点的数值计算方法,各离散点之间没有联结信息,其数值结果的可视化后处理是一件很困难的事情,尤其当离散点随机分布时,更是如此。Delaunay 三角化是十分理想的散乱数据的可视化工具,它可以根据一组随机分布的离散点数据生成唯一的近似等边三角形。首先介绍了 Voronoi 图与 Delaunay 三角化的基本原理,然后介绍了实现 Delaunay 三角剖分的算法及无网格方法数值结果可视化的实现方法,最后给出了无网格方法可视化的若干应用实例。     

从数据属性和设计逻辑再审视数据可视化

目的 提倡关注数据的客观属性和数据情感,以数据思维和设计逻辑为基础重新建立数据可视化设计模型,在此基础上,对设计环节中的内容进行问题的提出与延展,企图寻找有价值、值得被关注的问题,避免设计模型成为一成不变的方程式,同时将设计思维与数据科学更好地结合起来,为未来数据可视化的设计实践提供新的视角。方法 再审视包含对“数据”的再审视以及对“设计逻辑”的再审视,在“数据”与“数据思维”内涵的辨析与自定义的推动下,以数据可视化案例的演绎为依据,从数据可视化设计逻辑的角度,观察可视化设计的关键环节。结论 数据呈现方式具备多样化的形式,且设计形式与数据属性密切相关。建立以“认识、创建、评估”3个阶段为基础的数据可视化设计模型,避免忽视评估阶段,同时在实际设计中,设计师需要同步参与这3个阶段。     

A survey of temporal data mining

Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining. We mainly concentrate on algorithms for pattern discovery in sequential data streams. We also describe some recent results regarding statistical analysis of pattern discovery methods.     

Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods. To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality. We contributed to meeting the challenges of big data visualization using the embedded method based “Select from model (SFM)” method by using “Random forest Importance algorithm (RFI)” and comparing it with the filter method by using “Select percentile (SP)” method based chi square “Chi2” tool for selecting the most important features, which are then fed into a classification process using the logistic regression (LR) algorithm and the k-nearest neighbor (KNN) algorithm. Thus, the classification accuracy (AC) performance of LR is also compared to the KNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied. Consequently, the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal. After making several comparisons, the study suggests (SFMLR) using SFM based on RFI algorithm for feature selection, with LR algorithm for data classify. The proposal proved its efficacy by comparing its results with recent literature.     

An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment, which leverages new applications and services. Since the trajectory streams is rapidly evolving, continuously created and cannot be stored indefinitely in memory, the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams. This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models. By processing the trajectory data in current window, the mining algorithm can capture the trend and evolution of moving object clusters pattern. Firstly, the density peaks clustering algorithm is exploited to identify clusters of different snapshots. The stable relationship between relatively few moving objects is used to improve the clustering efficiency. Then, by intersecting clusters from different snapshots, the gradual moving object clusters pattern is updated. The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process. Finally, experiment results on two real datasets demonstrate that our algorithm is effective and efficient.     

基于VTK的激光跟踪测量可视化软件设计

测量数据的三维可视化是激光跟踪测量数据处理的关键步骤之一。结合大型激光跟踪测量软件系统的开发实践,对VTK技术特点和体系结构进行深入研究,设计实现了对测量数据进行处理和显示的类和接口。在此基础上,基于Microsoft Visual Studio 2010平台,利用C++和VTK技术开发了一款三维测量数据处理及可视化软件,实现了测量点云拟合、三维图形显示以及交互功能,并通过实例验证了软件的有效性和可用性,证明了VTK在工业测量可视化领域具有很大的应用价值。     

Virtual reality prototyping of bio-molecules

Modelling, sharing and transmission of three-dimensional (3D) graphics data of biomolecules are essential in many bio visualization tasks from collaborative research and education to molecular simulation and drug discovery. In the current paper, modelling and representing of bio-molecular structure for virtual and physical rapid prototyping is presented. Our aim is to devise a uniform solution for visualizing, browsing, interacting and prototyping of bio-molecules in various environments including internet, immersive virtual reality (VR), and rapid manufacturing. To do so, we use non uniform rational B-spline surfaces (NURBS) to represent protein secondary structure and surface structure. NURBS protein structures are then tessellated to form bio-molecular graphics models. Their triangular mesh representation is next extracted from their scene graph. A geometric optimization process is followed to make data compatible for their formatting in compact and consistent VR standard to support protein internet browsing, protein VR visualization, protein 3D rapid prototyping and crystal sub-surface laser engraving.     

Virtual reality prototyping of bio-molecules

Modelling, sharing and transmission of three-dimensional (3D) graphics data of biomolecules are essential in many bio visualization tasks from collaborative research and education to molecular simulation and drug discovery. In the current paper, modelling and representing of bio-molecular structure for virtual and physical rapid prototyping is presented. Our aim is to devise a uniform solution for visualizing, browsing, interacting and prototyping of bio-molecules in various environments including internet, immersive virtual reality (VR), and rapid manufacturing. To do so, we use non uniform rational B-spline surfaces (NURBS) to represent protein secondary structure and surface structure. NURBS protein structures are then tessellated to form bio-molecular graphics models. Their triangular mesh representation is next extracted from their scene graph. A geometric optimization process is followed to make data compatible for their formatting in compact and consistent VR standard to support protein internet browsing, protein VR visualization, protein 3D rapid prototyping and crystal sub-surface laser engraving.     

An Automated Penetration Semantic Knowledge Mining Algorithm Based on Bayesian Inference

Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration testing. Associative rule mining, a data mining technique, has been studied and explored for a long time. However, few studies have focused on knowledge discovery in the penetration testing area. The experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent pattern. To address this problem, a Bayesian inference based penetration semantic knowledge mining algorithm is proposed. First, a directed bipartite graph model, a kind of Bayesian network, is constructed to formalize penetration testing data. Then, we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution efficiency. Finally, irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the model. The experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.     

基于条码技术的离散产品生产跟踪方法研究

针对面向订单生产的离散型制造企业,提出了基于条码技术的离散产品生产数据采集与跟踪方法,并把工作流管理思想引入车间工序控制中,实现工序状态的可视化.定义了产品不同级别的加工状态,建立相应的状态判定算法,并利用采集数据对产品的加工指标进行计算.设计开发了原型系统,并在某企业得到成功应用.应用结果表明,条码运用于离散产品的车间生产跟踪是切实可行的.     

Accelerating Scientific Discovery Through Computation and Visualization III. Tight-Binding Wave Functions for Quantum Dots

This is the third in a series of articles that describe, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing, visualization, and machine learning to accelerate scientific discovery. In this article we focus on the use of high performance computing and visualization for simulations of nanotechnology.     

从需求出发的信息可视化设计方法研究

目的在数据来源繁杂、数据量巨大的的情况下,从信息设计的角度探索提高信息可视化设计质量的方法。方法分析笔者在执行几个信息可视化设计项目过程中应用到的用户研究以及交互设计相关理论,以及具体应用这些理论的方法。对基于用户需求的可视化设计流程和执行方法进行总结。结果提出关注信息传达本身、根据应用场景来设计心理模型与信息架构、对数据进行观点挖掘这几个方面来优化设计策略。结论信息可视化应逐渐从关注技术转变到关注使用者的需求本身,这个需求才是数据成为有价值的信息的关键。     

水平管降膜式蒸发器管间流型转换试验研究

为了分析水平管降膜式蒸发器管间流型的转换规律,设计一种多层布液器,采用可视化方法研究开孔规格、布液高度和管间距对管间流型转换的影响。试验结果表明,在一定流量下,流型转换的临界雷诺数与布液孔径、布液孔间距、布液高度、管间距、流动介质有关。根据试验数据拟合得到临界雷诺数Re与伽利略数Ga的关系,发现柱状流到柱-膜状流的过渡区域比较大。     

Visual-area coding technique (VACT): optical parallel implementation of fuzzy logic and its visualization with the digital-halftoning process

A novel technique, the visual-area coding technique (VACT), for the optical implementation of fuzzy logic with the capability of visualization of the results is presented. This technique is based on the microfont method and is considered to be an instance of digitized analog optical computing. Huge amounts of data an be processed in fuzzy logic with the VACT. In addition, real-time visualization of the processed result can be accomplished.