A split least-squares characteristic mixed finite element method for Sobolev equations with convection term

In this paper, a split least-squares characteristic mixed finite element method for a kind of Sobolev equation with convection term is proposed, in which the characteristic method is based on the approximation of the material derivative term, that is, the time derivative term plus the convection term. The resulting least-squares procedure can be split into two independent symmetric positive definite sub-schemes and does not need to solve a coupled system of equations. Theory analysis shows that the method yields the approximate solutions with optimal accuracy in L²(Ω) norm for the primal unknown and in H(div;Ω) norm for the unknown flux, respectively. Numerical examples in one dimension, which are consistent with the theoretical results, are provided to demonstrate the characteristic behavior of this approach.     

<Emphasis Type="Italic">N</Emphasis>-person credibilistic strategic game

This paper enlarges the scope of fuzzy-payoff game to n-person form from the previous two-person form. Based on credibility theory, three credibilistic approaches are introduced to model the behaviors of players in different decision situations. Accordingly, three new definitions of Nash equilibrium are proposed for n-person credibilistic strategic game. Moreover, existence theorems are proved for further research into credibilistic equilibrium strategies. Finally, two numerical examples are given to illustrate the significance of credibilistic equilibria in practical strategic games.     

Two-Dimensional Multilabel Active Learning with an Efficient Online Adaptation Model for Image Classification

Conventional active learning dynamically constructs the training set only along the sample dimension. While this is the right strategy in binary classification, it is suboptimal for multilabel image classification. We argue that for each selected sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multilabel Bayesian classification error bound. We call it two-dimensional active learning because it considers both the sample dimension and the label dimension. Furthermore, as the number of training samples increases rapidly over time due to active learning, it becomes intractable for the offline learner to retrain a new model on the whole training set. So we develop an efficient online learner to adapt the existing model with the new one by minimizing their model distance under a set of multilabel constraints. The effectiveness and efficiency of the proposed method are evaluated on two benchmark data sets and a realistic image collection from a real-world image sharing Web site—Corbis.     

Fast,Sub‐pixel Antialiased Shadow Maps

Solving aliasing artifacts is an essential problem in shadow mapping approaches. Many works have been proposed, however, most of them focused on removing the texel‐level aliasing that results from the limited resolution of shadow maps. Little work has been done to solve the pixel‐level shadow aliasing that is produced by the rasterization on the screen plane. In this paper, we propose a fast, sub‐pixel antialiased shadowing algorithm to solve the pixel aliasing problem. Our work is based on the alias‐free shadow maps, which is capable of computing accurate per‐pixel shadow, and only incurs little cost to extend to sub‐pixel accuracy. Instead of direct supersampling the screen space, we take facets to approximate pixels in shadow testing. The shadowed area of one facet is rapidly evaluated by projecting blocker geometry onto a supersampled 2D occlusion mask with bitmasks fusion. It provides a sub‐pixel occlusion sampling so as to capture fine shadow details and features. Furthermore, we introduce the silhouette mask map that limits visibility evaluation to pixels only on the silhouette, which greatly reduces the computation cost. Our algorithm runs entirely on the GPU, achieving real‐time performance and is an order of magnitude faster than the brute‐force supersampling method to produce comparable 32× antialiased shadows.     

工作流资源分配中约束规则的建模研究

传统工作流系统在对任务节点进行资源分配时,只进行了组织、角色、人员的分配,而较少对不同任务节点上执行人间的潜在关系进行建模.根据企业的实际应用需求,将这种潜在的约束关系提取出来进行建模,并以规则的形式进行定义.最后采用强有力的推理工具--规则引擎--来完成对定义的规则冲突检测以及规则的实现.     

基于高斯混合模型的无线局域网定位算法

目标定位是无线局域网提供位置服务的基础。该文围绕无线局域网目标定位中的定位精度和实时性2个QoS指标,对基于RSSI的定位技术进行研究,提出一种基于高斯混合模型的无线局域网定位算法。该算法由离线训练和在线定位2个阶段组成,采用GMM对RSSI进行建模,降低了系统定位误差,并减少了定位阶段的运算量,提高了定位的实时性。仿真实验结果表明,该算法具有较好的定位精度和实时性。     

基于过滤驱动的透明加密文件系统模型

传统加密软件在实际应用中一般要求用户在使用前手动解密文件,使用结束后再手动加密文件,整个过程十分繁琐,且文件在操作过程中以明文形式存储在硬盘上,存在安全隐患。该文针对传统加密软件的不足,采用过滤驱动技术,设计一个透明加密的文件系统模型,并予以实现。该模型无需用户改变使用习惯,全程实时透明加密数据,有效保障了数据的安全性,提高了工作效率,改善了用户体验。     

钻杆热处理温度监测管理系统设计

介绍一种用Delphi开发的上位机温度采集显示系统。该系统弥补了传统的钻杆热处理控制系统只能显示实时温度,无法保存温度变化过程的缺点。为进一步分析钻杆热处理过程提供详尽的信息,保证了钻杆的质量。该文对钻杆热处理过程进行阐述,并就热处理过程状态转换、无效启动、温度采集、温度信息保存的方法以及查询方法等关键问题进行介绍。     

基于改进灰色理论的威胁指标权重技术

针对各种网络指标之间的相互影响,同时考虑主客观2个方面的因素,提出用灰色理论中的灰关联度和传统层次分析法相结合的算法进行网络威胁指标权重的确定。实验证明,该方法数学理论严谨,具有良好的实用价值,使网络威胁评估方法在可信度方面有一定的 提高。