产品概念创新设计系统--CIDS

介绍开发的概念创新设计原型系统CIDS的知识库设计以及知识推理部分的算法．知识库利用框架结构进行面向对象的建模,描述实例知识功能特征和结构特征;知识推理部分将实例推理和规则推理结合使用以提高推理能力．提出一种改进的计算相似度的算法．介绍了使用CIDS进行手机产品概念设计的主要步骤．     

基于物元理论的任意功能树相似扩展方法

功能模型是概念设计的核心处理对象,功能树是一种典型的、应用广泛的 功能模型。使用现有相似度计算方法计算任意功能树的相似度存在困难。因此,基于布尔代 数提出了析取范式树的概念,以及两种求解析取范式树的方法,并描述了任意功能树的物元 相似度计算方法。拓展了功能树相似扩展方法的应用范围,扩大了设计解空间,增加了获得 创新解的可能性,最后给出实例验证了方法的有效性。     

基于案例推理的应急辅助决策方法研究*

将案例推理的方法应用到应急决策中,为应急决策提供了一种实用的科学辅助方法。在分析了应急案例特征的基础上设计了一种基于概念树—突发事件本体模型—事件元模型三层架构的应急案例通用的案例描述与组织方法;根据应急案例属性复杂及属性值缺失的问题设计了基于结构相似度和属性相似度双层结构的案例全局相似度计算算法,避免了传统最近相邻算法中的属性值缺失问题;最后通过基于案例推理的应急辅助决策原型系统的开发使设计方法得以实现,证明了该方法具有较好的实用性。     

基于知识的可视化图像处理集成开发环境模型

提供优秀的开发环境是提高研究效率的有效途径.本文提出的模型是基于知识的专家系统.建立了图像处理研究过程模型,设计了“知识体·对象块”两层知识表达模型和“框架推理+对象块推理”二级推理模式;提出了算法的试验设计、调用和计算结果三阶段可视化的方法,实现了算法、界面和数据可视化工具的自动重用.试验表明,基于该模型的开发可加快新算法的建模,提高研究效率;本文思想也可用于其它智能系统的建模.     

一种基于桶树的自动推理问题求解算法

桶消元和连接树推理算法是处理自动推理问题的两种常用的推理算法。针对连接树推理算法中消息传播效率问题,提出了一种能有效进行消息传播的连接树推理算法JTR。针对桶消元推理算法BE处理多任务的自动推理问题效率低下的问题,采用连接树结构和连接树推理算法JTR的消息传播方式对桶消元算法BE进行改进和扩展,提出了一种桶树推理算法BJTR。通过对算法BE、BTE和BJTR的时空性能分析发现:与同类算法BTE相比,算法BJTR在空间略有下降的情况下提高了时间性能;针对多任务的自动推理问题,与桶消元推理算法BE相比,BJTR算法的空间略有下降,时间性能得到明显提高;并通过实例和实验进一步验证了算法BJTR针对多任务的自动推理任务具有良好的时间性能。     

VRML可视化编辑器的研究与开发

对虚拟现实建模语言VRML可视化编辑器进行了研究,设计并实现了VRML可视化编辑器VisualVRMLEditor.构造了一个节点树视图,描述VRML文件的内部数据结构;构造了一个三维场景视图,显示了VRML文件所描述的三维场景;构造了一个路由视图,将路由这种无形的机制进行可视化编辑.实现了将节点树视图和三维场景视图结合起来,方便用户操作.此外,将路由这种无形的信息可视化,使用户能够进行可视化编辑路由,不用记住繁琐的VRML语法规则,提高了编辑VRML文件的效率.     

应急指挥系统的结构化和可视化预案研究

对结构化预案和可视化预案的实现技术进行研究,结合规则推理与案例推理,对其进行智能化推理及分析,根据突发事件信息主动推送有针对性的智能应急方案。设计并实现基于结构化和可视化预案的应急指挥系统,以满足应急指挥过程中智能应急方案的生成需要,提高预案在事件处置过程中的针对性与时效性。     

基于Cube的数据展现结构的研究

为了为用户提供合适的OLAP可视化工具,研究了OLAP可视化技术的范围和概念,并研究了OLAP可视化技术的研究现状。重点研究了分解树技术的内容以及拓展,根据分解树的优点和缺点提出了新的OLAP可视化技术:多角度增强分解树。该技术可从两个方面对OLAP数据进行钻取,其中提出的多角度钻取,通过不同的角度来对比观察数据,提高了模式发现能力。针对该技术,设计了系统架构,确定了系统流程,完成了系统的开发。最后对OLAP可视化研究的前景进行了总结。     

WDRR:一种带权重的多维案例推理算法

案例推理技术已经成为故障诊断、管理辅助决策、专家系统等实现的重要手段.现有的案例推理算法针对海量案例集时,普遍存在检索效率不高问题.设计了一种带权重的多维案例推理算法(Weighted DimensionReduction and R-tree,WDRR),该算法结合案例的多维特征权重,将多维案例降维成二维案例点,并在此基础上建立R树空间索引;案例检索时首先借助R树索引,确定案例的二维点所在,再结合二次权重和K近邻(KNN)算法进行精确过滤,根据相似度阈值输出案例推理的结果,并完成案例学习和索引修正.实验证明该方法针对海量案例集的检索效率和准确率都有较大的提升.     

基于协同侦测技术的移动主体模型验证策略研究

移动主体模型可以实现协同侦测网络攻击和区域预警的功能;它利用元组空间(TUPLE—SPACE)构建移动主体服务集群(RMAS—CMAS)网络结构及侦测ARP攻击的主体．为了证明该模型逻辑的有效性,验证策略设计了可以分析ARP攻击数据包从初始组态到终结组态的动态空间树;利用空间树嵌套演算推理协同侦测系统的组态迁移过程,分析模块的动态移动重组以及消息的并行传递机制．对模型验证策略的研究可以消除在模型设计阶段出现的冗余组态,提高移动系统的设计水平．     

Design sketches and sketch design tools

In early stages of design architects often use sketching and diagramming to perform formal and functional reasoning. Design sketches are an external representation aid for visualization and evaluation of the spatial arrangements of artifacts. Symbols and configurations are used in design sketches to define context and object arrangements. This paper argues the need to study design drawing, reports the findings from empirical studies of design drawings, and describes the software systems implemented to support intention inference and automated activation of knowledge-based design tools to support design.     

Video Visualization for Snooker Skill Training

We present a feasibility study on using video visualization to aid snooker skill training. By involving the coaches and players in the loop of intelligent reasoning, our approach addresses the difficulties of automated semantic reasoning, while benefiting from mature video processing techniques. This work was conducted in conjunction with a snooker club and a sports scientist. In particular, we utilized the principal design of the VideoPerpetuoGram (VPG) to convey spatiotemporal information to the viewers through static visualization, removing the burden of repeated video viewing. We extended the VPG design to accommodate the need for depicting multiple video streams and respective temporal attribute fields, including silhouette extrusion, spatial attributes, and non‐spatial attributes. Our results and evaluation have shown that video visualization can provide snooker coaching with visually quantifiable and comparable summary records, and is thus a cost‐effective means for assessing skill levels and monitoring progress objectively and consistently.     

Reasoning about pictures and similarity retrieval for image information systems based on SK-set knowledge representation

Spatial reasoning and similarity retrieval are two important functions of any image information system. Good spatial knowledge representation for images is necessary to adequately support these two functions. In this paper, we propose a new spatial knowledge representation, called the SK-set based on morphological skeleton theories. Spatial reasoning algorithms which achieve more accurate results by directly analysing skeletons are described. SK-set facilitates browsing and progressive visualization. We also define four new types of similarity measures and propose a similarity retrieval algorithm for performing image retrieval. Moreover, using SK-set as a spatial knowledge representation will reduce the storage space required by an image database significantly.     

Similarity-based approximate reasoning: methodology and application

This correspondence elucidates the importance of similarity, as modeled by a measure of similarity, in approximate reasoning. A set of axioms is proposed to compute a reasonable measure of similarity between two imprecise concepts represented as fuzzy sets. For approximate reasoning, a similarity index between the fact and the antecedent of a rule is computed and is used in the reasoning mechanism. Accordingly, the existing reasoning mechanism is modified. A new similarity-based approximate reasoning methodology is proposed. As an illustration of its effectiveness, the proposed mechanism is used to develop a rule-based pattern classifier.     

ConceptGraph: A Formal Model for Interpretation and Reasoning During Visual Analysis

In order to discuss the kinds of reasoning a visualization supports and the conclusions that can be drawn within the analysis context, a theoretical framework is needed that enables a formal treatment of the reasoning process. Such a model needs to encompass three stages of the visualization pipeline: encoding, decoding and interpretation. The encoding details how data are transformed into a visualization and what can be seen in the visualization. The decoding explains how humans construct graphical contexts inside the depicted visualization and how they interpret them assigning meaning to displayed structures according to a formal reasoning strategy. In the presented model, we adapt and combine theories for the different steps into a unified formal framework such that the analysis process is modelled as an assignment of meaning to displayed structures according to a formal reasoning strategy. Additionally, we propose the ConceptGraph, a combined graph-based representation of the finite-state transducers resulting from the three stages, that can be used to formalize and understand the reasoning process. We apply the new model to several visualization types and investigate reasoning strategies for various tasks.     

层次信息可视化技术研究综述

随着掌控的层次信息在数量和规模上的急剧增长,用户迫切需要高效的可视化工具在理解信息和制定决策过程中对认知行为进行辅助.首先对层次结构进行介绍,给出层次可视化的设计目标;然后讨论了层次可视化技术的分类问题,并尝试提出一种较为合理且便于阐述的表现-维度分类框架;随后基于上述分类框架,重点分析现有主要层次可视化技术的基本原理和特征;最后比较总结各类技术,并对层次可视化可能的研究方向及面临的挑战做出展望.     

SimilarityExplorer: A Visual Inter‐Comparison Tool for Multifaceted Climate Data

Inter‐comparison and similarity analysis to gauge consensus among multiple simulation models is a critical visualization problem for understanding climate change patterns. Climate models, specifically, Terrestrial Biosphere Models (TBM) represent time and space variable ecosystem processes, like, simulations of photosynthesis and respiration, using algorithms and driving variables such as climate and land use. While it is widely accepted that interactive visualization can enable scientists to better explore model similarity from different perspectives and different granularity of space and time, currently there is a lack of such visualization tools. In this paper we present three main contributions. First, we propose a domain characterization for the TBM community by systematically defining the domain‐specific intents for analyzing model similarity and characterizing the different facets of the data. Second, we define a classification scheme for combining visualization tasks and multiple facets of climate model data in one integrated framework, which can be leveraged for translating the tasks into the visualization design. Finally, we present SimilarityExplorer, an exploratory visualization tool that facilitates similarity comparison tasks across both space and time through a set of coordinated multiple views. We present two case studies from three climate scientists, who used our tool for a month for gaining scientific insights into model similarity. Their experience and results validate the effectiveness of our tool.     

一种自动化软件设计改进方法

面向对象的软件设计改进是增强系统的可扩展性、使之适应可能的需求变化的一种有效手段.提出一种基于CBR(case based reasoning)和微体系结构反模式的设计改进方法.该方法形式化地定义了在微体系结构层中不灵活的设计结构和相应的重构方案的描述方法,研究了它们在事例库中的组织和索引机制.根据基于事例的推理技术的4R过程,提出了类图、顺序图、质量要素和语义约束的相似性度量方法,描述了在给定设计中识别反模式及其实例,并在此基础上,用高质量的设计方案进行替代的几种算法.以该方法为依据,进一步介绍了设计改进支撑系统--CBDIT(CBR based design improving tool)的体系结构的设计.     

Isosurface Similarity Maps

In this paper, we introduce the concept of isosurface similarity maps for the visualization of volume data. Iso‐surface similarity maps present structural information of a volume data set by depicting similarities between individual isosurfaces quantified by a robust information‐theoretic measure. Unlike conventional histograms, they are not based on the frequency of isovalues and/or derivatives and therefore provide complementary information. We demonstrate that this new representation can be used to guide transfer function design and visualization parameter specification. Furthermore, we use isosurface similarity to develop an automatic parameter‐free method for identifying representative isovalues. Using real‐world data sets, we show that isosurface similarity maps can be a useful addition to conventional classification techniques.     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.