Multi-scale gist feature manifold for building recognition

Multi-scale gist (MS-gist) feature manifold for building recognition is presented in the paper. It is described as a two-stage model. In the first stage, we extract the multi-scale gist features that represent the structural information of the building images. Since the MS-gist features are extrinsically high dimensional and intrinsically low dimensional, in the second stage, an enhanced fuzzy local maximal marginal embedding (EFLMME) algorithm is proposed to project MS-gist feature manifold to low-dimensional subspace. EFLMME aims to preserve local intra-class geometry and maximize local interclass margin separability of MS-gist feature manifold of different classes at the same time. To evaluate the performance of our proposed model, experiments were carried out on the Sheffield buildings database, compared with the existing works: (a) the visual gist based building recognition model (VGBR) and (b) the hierarchical building recognition model (HBR). Moreover, EFLMME is evaluated on Sheffield buildings database compared with some linear dimensionality reduction methods. The results show that the proposed model is superior to other models in practice of building recognition and can handle the building recognition problem caused by rotations, variant lighting conditions and occlusions very well.     

基于Petri网的数字家庭无线射频协议研究

对数字家庭无线射频通信协议进行分解,给出协议各部分的流程。构造基于Petri网的通信协议模型,并在模型的基础上运用可达图方法分析协议Petri网模型的动态性质,从而验证协议的正确性,为进一步研究和应用该协议提供一种新的思路。     

Deadline division-based heuristic for cost optimization in workflow scheduling

Cost optimization for workflow applications described by Directed Acyclic Graph (DAG) with deadline constraints is a fundamental and intractable problem on Grids. In this paper, an effective and efficient heuristic called DET (Deadline Early Tree) is proposed. An early feasible schedule for a workflow application is defined as an Early Tree. According to the Early Tree, all tasks are grouped and the Critical Path is given. For critical activities, the optimal cost solution under the deadline constraint can be obtained by a dynamic programming strategy, and the whole deadline is segmented into time windows according to the slack time float. For non-critical activities, an iterative procedure is proposed to maximize time windows while maintaining the precedence constraints among activities. In terms of the time window allocations, a local optimization method is developed to minimize execution costs. The two local cost optimization methods can lead to a global near-optimal solution. Experimental results show that DET outperforms two other recent leveling algorithms. Moreover, the deadline division strategy adopted by DET can be applied to all feasible deadlines.     

A stochastic graph grammar for compositional object representation and recognition

This paper illustrates a hierarchical generative model for representing and recognizing compositional object categories with large intra-category variance. In this model, objects are broken into their constituent parts and the variability of configurations and relationships between these parts are modeled by stochastic attribute graph grammars, which are embedded in an And-Or graph for each compositional object category. It combines the power of a stochastic context free grammar (SCFG) to express the variability of part configurations, and a Markov random field (MRF) to represent the pictorial spatial relationships between these parts. As a generative model, different object instances of a category can be realized as a traversal through the And-Or graph to arrive at a valid configuration (like a valid sentence in language, by analogy). The inference/recognition procedure is intimately tied to the structure of the model and follows a probabilistic formulation consisting of bottom-up detection steps for the parts, which in turn recursively activate the grammar rules for top-down verification and searches for missing parts. We present experiments comparing our results to state of art methods and demonstrate the potential of our proposed framework on compositional objects with cluttered backgrounds using training and testing data from the public Lotus Hill and Caltech datasets.     

有向无环图最小度生成树问题的一种近似算法

计算具有较小度的生成树是算法与复杂性研究的一个基本问题,同时在网络设计等领域具有重要应用.给定具有n个顶点的有向无环图G=(V,E)和根顶点r∈ V,最小度生成树问题欲求一棵以r为根的生成树T,使得在G的所有以r为根的生成树中T的最大度最小.给出该问题的一种迭代的多项式时间近似算法.该算法所求树的度不超过△*+1,其中△*为某一最优树的度.算法的时间复杂度为O(n2logn),其中n为顶点数目.算法没有运用过多的枚举,其实际运行时间要快得多.     

面向攻击图构建的网络连通性分析

针对目前网络攻击图构建系统的需求,设计网络连通性分析算法。通过对网络拓扑及防火墙规则进行离线分析,可以判断网络中由若干台过滤设备分隔的任意2台主机间的连通性。引入关键实体集的概念,结合经典的Apriori算法提出一种快速有效的获取关键实体集的方法。分析对比表明,关键实体集可以在连通性分析过程中为网络中各节点的重要性评估提供有力依据。     

基于可疑行为识别的PE病毒检测方法

针对当前PE病毒难以防范及查杀的现象,对PE病毒关键技术进行分析,提取病毒典型特征的可疑行为,在此基础上提出一种Windows平台下的静态检测方法。该方法在对程序反编译处理的基础上,以指令序列与控制流图的分析为行为识别依据,完成基于可疑行为识别的病毒检测方法的设计。实验结果证明,该检测方法能有效检测混淆变换病毒。     

Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.     

面向三维动画自动生成的路径规划

全过程计算机辅助自动生成动画技术由中科院陆汝钤院士于上世纪90年代提出,这一技术的目标是将适当的故事以受限自然语言的方式输入计算机,由开始一直到动画的生成,每一步都是在计算机的辅助下完成.在动画生成过程中,如何规划运动对象路径是影响动画效果的重要因素.为此,提出基于动画场景规划信息的路径规划方法,该方法通过预定义的路径定性规划语言PADL描述规划需求,然后通过利用扩展的A*算法规划路径并生成路径定量规划语言PCAL描述的路径,最后采用运动图方法合成路径运动动画.实验表明,该方法能有效地自动生成符合指定要求的路径动画.