Hierarchical Implicit Shape Modeling

In this paper, a new hierarchical approach for object detection is proposed. Object detection methods based on Implicit Shape Model (ISM) efficiently handle deformable objects, occlusions and clutters. The structure of each object in ISM is defined by a spring like graph. We introduce hierarchical ISM in which structure of each object is defined by a hierarchical star graph. Hierarchical ISM has two layers. In the first layer, a set of local ISMs are used to model object parts. In the second layer, structure of parts with respect to the object center is modeled by global ISM. In the proposed approach, the obtained parts for each object category have high discriminative ability. Therefore, our approach does not require a verification stage. We applied the proposed approach to some datasets and compared the performance of our algorithm to comparable methods. The results show that our method has a superior performance.     

基于攻击期望的双模攻击图生成算法

针对网络中心战中已有的攻击图生成算法不考虑对攻击效果的期望,不能适应多种网络脆弱性分析目标的不足,提出了双模攻击图(Two-Mode Attack Graph,TMAG)的概念.该方法以攻击者在网络中所有主机上具有的权限作为状态,以网络弱点和连接情况为辅助,设定网络攻击的单调性,能自动生成精简状态空间的完备攻击图和关键...     

光网络中基于预定组播业务的资源优化

文章利用遗传算法安排光网络中预定组播业务的计算顺序,提出了优化业务顺序的分层图算法和点着色算法,并与按时间顺序安排业务的算法进行了比较.仿真表明,优化排序后,资源优化效果明显.在波长数的优化上,分层图算法比点着色算法性能更好.在分层图算法中,业务放置顺序的优化对组播树总链路数的减少也有一定帮助.     

电信大规模社交关系网络图数据挖掘研究

电信技术的发展使得通话网络几乎覆盖了整个社会,对电信运营商来说,这个庞大的社会网络蕴藏着巨大的商机.尤其在日益激烈的市场竞争中,客户关系的维护、客户潜在价值的挖掘以及有针对性地对客户进行营销等对电信图数据的挖掘,受到了强烈关注.通过对比,研究了当前针对类似电信网络的大规模社交关系网络图数据进行挖掘的分布式和单机图计算工具和框架,并对单机图计算工具Graphchi的单机处理数据能力进行了可行性和可用性测试,同时采用单机Graphchi对电信社交关系网络的数据进行了图挖掘研究和应用.     

基于图切割的人体运动检测

研究利用图切割对人体进行有效检测的方法.首先在色相、饱和度和亮度(HSV)颜色空间建立自适应的背景混合模型快速提取背景;然后计算差分并消除阴影;最后构造8连通网络图,使用最小切割完成目标的分割.通过实验,对单模型与混合模型背景、4连通与8连通邻域以及基于数学形态学与基于图切割的分割进行了比较.结果表明,在实际环境下,采用本方法可快速、有效和鲁棒地对人体运动进行检测,并获得干净、光滑的分割结果.     

k-Diameter of Circulant Graph with Degree 3

Parameters k-distance and k-diameter are extension of the distance and the diameter in graph theory. In this paper, the k-distance dk （x,y） between the any vertices x and y is first obtained in a connected circulant graph G with order n （n is even） and degree 3 by removing some vertices from the neighbour set of the x. Then, the k-diameters of the connected circulant graphs with order n and degree 3 are given by using the k-diameter dk （x,y）.     

Multiscale spatial temporal attention graph convolution network for skeleton-based anomaly behavior detection

Anomaly behavior detection plays a significant role in emergencies such as robbery. Although a lot of works have been proposed to deal with this problem, the performance in real applications is still relatively low. Here, to detect abnormal human behavior in videos, we propose a multiscale spatial temporal attention graph convolution network (MSTA-GCN) to capture and cluster the features of the human skeleton. First, based on the human skeleton graph, a multiscale spatial temporal attention graph convolution block (MSTA-GCB) is built which contains multiscale graphs in temporal and spatial dimensions. MSTA-GCB can simulate the motion relations of human body components at different scales where each scale corresponds to different granularity of annotation levels on the human skeleton. Then, static, globally-learned and attention-based adjacency matrices in the graph convolution module are proposed to capture hierarchical representation. Finally, extensive experiments are carried out on the ShanghaiTech Campus and CUHK Avenue datasets, the final results of the frame-level AUC/EER are 0.759/0.311 and 0.876/0.192, respectively. Moreover, the frame-level AUC is 0.768 for the human-related ShanghaiTech subset. These results show that our MSTA-GCN outperforms most of methods in video anomaly detection and we have obtained a new state-of-the-art performance in skeleton-based anomaly behavior detection.     

Sensor fault detection algorithm based on credibility and neighbor-cooperation

To effectively detect whether a node was failure,one of the effective sensor fault detection methods was to compare historical data of suspicious node with its neighbors.The key points of this method were identification of suspicious nodes,as well as decision problem of sending timing for fault diagnosis messages.To this end,a sensor fault detection algorithm was presented based on credibility and neighbor-cooperation.Firstly,a credibility model to determine whether nodes are suspicious was established.Then suspicious nodes send fault diagnosis requests to neighbor nodes based on neighbor-cooperation.The sending timing was determined by equal probability time window.Finally,it could finish classification of fault condition and state judgment based on diagnosis responds of neighbor nodes.Simulation experiments show that this algorithm can achieve higher fault detection rate with less fault diagnosis times and low data congestion probability.     

基于Mathcad系统对解决统计教学问题中的相关应用

针对Mathcad 系统的特点,阐述了应用Mathcad 系统对解决统计学教学问题中所进行的数据计算,图形绘制和统计决断等这些问题提出思路与方法,不仅可以轻松地解决统计学中繁杂的数值处理和计算问题,而且能够用Mathcad系统绘制准确生动的统计学图表和通过编程让Mathcad系统自动做出科学的统计判断。