Local Exact Particle Tracing on Unstructured Grids

For analyzing and interpreting results of flow simulations, particle tracing is a well established visualization method. In addition, it is a preliminary step for more advanced techniques such as line integral convolution. For interactive exploration of large data sets, a very efficient and reliable particle tracing method is needed. For wind channel experiments or flight simulations, large unstructured computational grids have become common practice. Traditional approachs, based on numerical integration methods of ordinary differential equations however fail to deliver sufficiently accurate path calculation at the speed required for interactive use. In this paper we extend the local exact approach of Nielson and Jung in such a way that it can be used for interactive particle tracing in large data sets of steady flow simulation experiments. This will be achieved by sophisticated preprocessing using additional memory. For further visual enhancement of the streamline we construct an implicitly defined smooth Bézier curve that is used for ray tracing. This allows us to visualize additional scalar values of the simulation as attributes to the trajectory and enables the display of high‐quality smooth curves without creating any visualization geometry and providing a good impression of the spatial situation at the same time. ACM CSS: I.3.3 Computer Graphics—Line and curve generation; I .3.7 Computer Graphics—Raytracing; G.1.2 Numerical Analysis—Spline and piecewise polynomial approximation     

What do we learn from binding features? Evidence for multilevel feature integration.

Four experiments were conducted to investigate the relationship between the binding of visual features (as measured by their aftereffects on subsequent binding) and the learning of feature-conjunction probabilities. Both binding and learning effects were obtained, but they did not interact. Interestingly, (shape-color) binding effects disappeared with increasing practice, presumably because of the fact that only 1 of the features involved was relevant to the task. However, this instability was only observed for arbitrary, not highly overlearned combinations of simple geometric features and not for real objects (colored pictures of a banana and strawberry), where binding effects were strong and resistant to practice. These findings suggest that learning has no direct impact on the strength or resistance of bindings or on speed with which features are bound; however, learning does affect the amount of attention particular feature dimensions attract, which again can influence which features are considered in binding. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Alleviating convergence problems in Group Support Systems

Not all Group Support Systems are identical, as is demonstrated by their software implementations. We discuss two existing implementations of group support tools and the process models underlying them. We demonstrate that fundamental to both processes is the merging or integration of individual data. Based on this and other empirical research, the Shared Context Model (SCM) of cooperative work is adopted and we show that it supports existing processes and others. We expect that groups will find merging their work easier with the SCM. This model is presented and embedded in the architecture and implementation of four group tools. Because these tools are destined to be used by dispersed groups, synchronously or asynchronously, an object-based communication and control mechanism is incorporated. Finally, as graphics and multi-tasking have been shown to be increasingly important, the tools are implemented in Microsoft Windows for personal computers attached to local area networks.     

企业柔性:基于集成的观点

从集成的思想出发分析了柔性的内涵，认为企业柔性的目标是提高企业在动态环境条件下的整体竞争优势，柔性的基础是在更大范围内的协调优化与有效集成；从系统的角度将企业柔性分为市场柔性，研发柔性，制造柔性与组织柔性四个维度，对每个维度所包括的具体内容进行了分析；建立了企业柔性的概念模型，从整体的角度出发对企业的柔性的形式与作用机制进行了描述。     

基于ODMG—93标准的面向对象视图集成机制

模式集成多数据库系统中需要解决的关键问题之一，己存在的大多数面向多数据库系统都采用了面向对象视图机制来建立集成模式。为了满足基于ＣＯＲＢＡ的ＣＩＭＳ信息集成平台呆订成的需要，本文提出了一种基于数据库管理组ＯＤＭＧ的ＯＤＭＧ－９３标准的视图集成机制并通过一个完整的例子说明了这种视图机制在模式集成中的应用。     

中国民航信息系统现状及发展展望

介绍了中国民航信息系统的现状和与实际业务需求的差距,接着从系统设计理念、核心系统、新系统新功能和信息系统整合等多个方面详细阐述了民航信息系统的发展思路,最后强调了建立新一代民航信息系统的紧迫性,具备很强的指导意义。     

应用型机械本科专业教学体系改革研究与实践

根据当前高等教育改革与发展的指导思想，紧紧围绕国家实施制造业信息化建设工程为背景，积极配合打造浙江先进制造业基地建设，结合现代机械专业结构调整，本着交叉、复合、应用型人才的培养目标，塑造具有宽口径、厚基础和强创新意识的人才培养理念，以数字化设计制造为核心，以机械技术、电子技术、信息技术和管理学科结合为主线，突出知识综合与创新能力，强调专业实践与专业知识的有效结合，有的放矢实施机械专业教学改革与实践。     

智能化系统在新火车站的应用——福州火车站智能化系统设计总结

福州火车站智能化系统包括到发通告、旅客引导显示、客运广播、建筑设备自动控制等7个子系统和系统总集成。     

信号时域积分性质的一个推广应用

对信号进行运算（卷积运算，傅氏变换，拉纸变换）时，应用时域积分性质，得出由函数导数的运算式求取原函数的相应运算结果的一般性计算公式，使时域积分性质的应用得到推广。     

Merging uncertain information with semantic heterogeneity in XML

Semistructured information can be merged in a logic-based framework [6, 7]. This framework has been extended to deal with uncertainty, in the form of probability values, degrees of beliefs, or necessity measures, associated with leaves (i.e. textentries) in the XML documents [3]. In this paper we further extend this approach to modelling and merging uncertain information that is defined at different levels of granularity of XML textentries, and to modelling and reasoning with XML documents that contain semantically heterogeneous uncertain information on more complex elements in XML subtrees. We present the formal definitions for modelling, propagating and merging semantically heterogeneous uncertain information and explain how they can be handled using logic-based fusion techniques. Anthony Hunter received a B.Sc. (1984) from the University of Bristol and an M.Sc. (1987) and Ph.D. (1992) from Imperial College, London. He is currently a reader in the Department of Computer Science at University College London. His main research interests are: Knowledge representation and reasoning, Analysing inconsistency, Argumentation, Default reasoning and Knowledge Fusion. Weiru Liu is a senior lecturer at the School of Computer Science, Queen's University Belfast. She received her B.Sc. and M.Sc. degrees in Computer Science from Jilin University, P.R China, and her Ph.D. degree in Artificial Intelligence from the University of Edinburgh. Her main research interests include reasoning under uncertainty, knowledge representation and reasoning, uncertain knowledge and information fusion, and knowledge discovery in databases. She has published over 50 journal and conference papers in these areas.