实时刚体破碎特效仿真研究

刚体仿真是图形学研究的热点之一,但是单纯依据物理规律进行仿真达不到实时性要求.依据攻击武器的运动轨迹在目标的八叉树结构上抽取空间连续的子块,设计了一种摧毁区域锁定算法;提出一种刚体预破碎模式,采用线性约束连接子块和块内元素,并用统一的算法检测由攻击武器的碰撞引起的约束动态受损情况.线性约束的不同程度破损或导致子块的分裂,或导致块内基础元素的分裂,将形成不同程度的破碎特效.最后设计了实时刚体破碎特效实验方案并构建仿真系统.实验表明,该种模式不仅较好地体现了刚体破碎特效,也简化了刚体破碎仿真的复杂性,相应的破碎特效能够拓展到具有实时性需求的虚拟现实应用环境中.     

BeehiveZ:一个开放的业务过程模型管理框架

随着面向服务架构、业务过程管理技术的推广应用,企业中用建模语言描述的业务过程模型越来越多,这些模型描述了企业核心的业务逻辑,是企业最宝贵的知识资产.为了更好地促进企业的发展,提升竞争力,需要将这些业务过程模型作为数据进行有效地管理、建立企业过程模型库,并支持对过程模型的存储、浏览、相似性度量、检索等操作和应用开发.提出了一个开放的业务过程模型管理框架--BeeHiveZ,阐述了BeeHiveZ的体系结构、关键技术及系统功能与操作等内容.BeeHiveZ现已在开源软件社区SourceForge. net上发布.     

InfoSigs:一种面向Web对象的细粒度聚类算法

面向Web对象的细粒度聚类已经成为学术界研究的热点.然而现有大多数聚类模型只关注如何对文本内容或文章主题进行聚类,聚类结果粒度较粗,无法满足大规模网络信息检索的质量要求.针对上述挑战,充分挖掘Web文档中词汇间的树状概率层次关系,提出一种以词汇信息分布作为特征标志的聚类算法InfoSigs,实现对Web对象的细粒度聚类.算法构建一个信息传递有向无环图,根据词汇在图中信息分布的集中度赋予其合理的权重,产生更具代表性的特征向量;同时算法提出了一个自适应的记录合并模型,有效提高记录簇中记录间的相似度,减少噪音对合并过程的影响.实验结果表明,InfoSigs算法比传统聚类算法—I-Match和Shingling—在F-Measure值上平均约有21.3%的提高,可以有效地运用到多领域Web对象的聚类问题.     

一种信息-物理融合系统体系结构

信息-物理融合系统(cyber-physical system, CPS)是当今最前沿的交叉研究领域之一,它被普遍认为是计算机信息处理技术史上的下一次革命,将会改变人与现实物理世界之间的交互方式,具有广泛的应用前景.CPS是一种分布式且深度嵌入式的实时系统,实时性是CPS的一个极重要特征和性能要求.研究了CPS体系结构,提出了一种CPS系统模型,并且从实时任务调度和实时网络两个方面,提出了解决系统实时性保证问题的初步方案,为进一步的研究工作奠定了基础.     

基于ASP.NET与Amzi! Prolog混合编程开发专家系统的研究

针对ASP.NET网站开发中可能遇到的逻辑推理问题,提出并实现了用Prolog语言与ASP.NET混合编程方法开发专家系统框架的方案,为开发基于Web的专家系统提供了一条方便快捷和高效的途径。此方案用ASP.NET2.0(C#)实现专家系统的Web用户界面,用Prolog语言实现推理过程,介绍了该专家系统的实现原理,给出了关键程序连接语句用来论述其结合技术,从而提高了软件开发的效率。     

Model Transduction for Triangle Meshes

This paper proposes a novel method, called model transduction, to directly transfer pose between different meshes, without the need of building the skeleton configurations for meshes. Different from previous retargetting methods, such as deformation transfer, model transduction does not require a reference source mesh to obtain the source deformation, thus effectively avoids unsatisfying results when the source and target have different reference poses. Moreover, we show other two applications of the model ...     

Mesh Snapping: Robust Interactive Mesh Cutting Using Fast Geodesic Curvature Flow

This paper considers the problem of interactively finding the cutting contour to extract components from a given mesh. Some existing methods support cuts of arbitrary shape but require careful and tedious input from the user. Others need little user input however they are sensitive to user input and need a postprocessing step to smooth the generated jaggy cutting contours. The popular geometric snake can be used to optimize the cutting contour, but it cannot deal with the topology change. In this paper, we propose a geodesic curvature flow based framework to overcome all these problems. Since in many cases the meaningful cutting contour on a 3D mesh is locally shortest in the sense of some weighted curve length, the geodesic curvature flow is an ideal tool for our problem. It evolves the cutting contour to the nearby local minimum. We should mention that the previous numerical scheme, discretized geodesic curvature flow (dGCF) is too slow and has not been applied to mesh segmentation. With a careful observation to dGCF, we devise here a fast computation scheme called fast geodesic curvature flow (FGCF), which only needs to solve a smaller and easier problem. The initial cutting contour is generated by a variant of random walks algorithm, which is very fast and gives reasonable cutting result with little user input. Experiment results on the benchmark mesh segmentation data set show that our proposed framework is robust to user input and capable of producing good results reflecting geometric features and human shape perception.     

Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.     

An overview of dynamic parameter identification of robots

Due to the importance to model-based control, dynamic parameter identification has attracted much attention. However, until now, there is still much work for the identification of dynamic parameters to be done. In this paper, an overview is given of the existing work on dynamic parameter identification of serial and parallel robots. The methods for estimating the dynamic parameters are summarized, and the advantages and disadvantages of each method are discussed. The model to be identified and the trajectory optimization are reviewed. Further, the methods for validating the estimated model are summarized and the application of dynamic parameter identification is mentioned. The results of this review are useful for manufacturers of robots in selecting proper identification method and also for researchers in determining further research areas.     

Distributed bandwidth allocation based on alternating evolution algorithm

In a network, end nodes have to compete for bandwidth through some distributed congestion control algorithms. It is a great challenge to ensure the efficiency and fairness of the distributed control algorithms. TCP congestion control algorithms do not perform well in terms of their efficiency and fairness in high speed networks. In this paper, we propose a novel asymptotic evolution algorithm based on the Logistic Model to allocate limited bandwidth resource. The algorithm introduces an explicit bandwidth pre-allocation factor. The factor is carried by the packet and is computed in routers based on the information of the router capacity, the aggregate load, and the instantaneous queue length; therefore the algorithm does not require the routers to keep the per-flow state. According to this pre-allocation bandwidth factor, the senders asymptotically adjust their sending rate and the bandwidth factor changes asymptotically along with the variation of the aggregate load and the queue length in the routers; therefore the sending rate and the pre-allocation bandwidth factor form alternating evolution and eventually reach a steady state.