互联网视听节目监测从业人员上网行为审计系统设计与实现

文章根据互联网视听节目监测从业人员的工作内容,提出上网行为审计系统的设计并加以实现,系统满足加强互联网信息安全的需求,同时规范上网行为、提高工作效率。系统通过用户准入、上网行为记录、流量控制、终端管理等技术手段,部署了一套完整的上网行为审计系统。本系统旨在解决互联网视听节目监测从业人员访问互联网所衍生的信息安全隐患、工作效率低等问题。     

A Complete Solution to Spectrum Problem for Five‐Vertex Graphs with Application to Traffic Grooming in Optical Networks

A G‐design of order n is a decomposition of the complete graph on n vertices into edge‐disjoint subgraphs isomorphic to G. Grooming uniform all‐to‐all traffic in optical ring networks with grooming ratio C requires the determination of graph decompositions of the complete graph on n vertices into subgraphs each having at most C edges. The drop cost of such a grooming is the total number of vertices of nonzero degree in these subgraphs, and the grooming is optimal when the drop cost is minimum. The existence spectrum problem of G‐designs for five‐vertex graphs is a long standing problem posed by Bermond, Huang, Rosa and Sotteau in 1980, which is closely related to traffic groomings in optical networks. Although considerable progress has been made over the past 30 years, the existence problems for such G‐designs and their related traffic groomings in optical networks are far from complete. In this paper, we first give a complete solution to this spectrum problem for five‐vertex graphs by eliminating all the undetermined possible exceptions. Then, we determine almost completely the minimum drop cost of 8‐groomings for all orders n by reducing the 37 possible exceptions to 8. Finally, we show the minimum possible drop cost of 9‐groomings for all orders n is realizable with 14 exceptions and 12 possible exceptions.     

SOME TRAFFIC ISSUES IN THE DESIGN OF VIRTUAL PRIVATE NETWORKS OVER ATM

The emergence of broadband technologies such as ATM gives rise to new developments in the domain of virtual private networks (VPNs). In this paper, a target broadband VPN (B-VPN) service is proposed, which combines the cost savings of classic VPN services and the powerful features of private corporate networks (service integration, statistical multiplexing, etc.) enlarged by the use of ATM. To reach this B-VPN service, some architectural issues related to the network supporting the B-VPN service are discussed. The traffic implications of the possible architectural choices are then examined, and from this discussion some basic principles for a VPN architecture are drawn. Finally, target VPN architectures are proposed.     

用支持向量机网络实现VBR视频通信量的预测

VBR(Variable Bit Rate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测是提高信息传输速度和提高网络带宽资源利用效率的重要手段.针对以上问题,本文提出了一种用于VBR视频通信量预测的差分输入支持向量机(SVM:Support Vector Machine)网络模型.该网络模型采用结构风险最小化准则,在最小化经验风险的同时,尽量缩小模型预测误差的上界,从而使网络模型具有更好的推广能力.实验结果表明:支持向量机网络模型的预测误差为0.0018,而梯度径向基函数(Gradient Radial Basis Function:GRBF)神经网络模型的预测误差为0.0029.可以看出,支持向量机网络模型的预测精度要比GRBF网络模型的预测精度高出大约40%.     

自相似业务下共享通道保护WDM网络性能分析

该文利用随机中点置换-分形高斯噪声(RMD-FGN)方法生成自相似业务,并且利用分层图方法来记录WDM网络状态,提出了一种链路状态描述模型。对自相似业务下55 Mesh_Torus共享通道保护WDM网络性能进行了仿真。结果表明:当业务的自相似系数或方差变大时,也就是说业务的突发程度变大时,阻塞率变大,网络的性能下降。增加单纤波长数,可以降低阻塞率,提高网络性能。     

光突发交换网络中最长队列优先调度组装机制

提出了光突发交换网络中的最长队列优先调度（LQF-SA）机制,将边缘节点的突发组装和突发调度两种技术紧密结合在一起.在现实网络中,业务通常是非均匀分布的,即在某些源宿对之间存在更大的业务流量.仿真结果表明,LQF-SA机制可以很好地完成非均匀分布业务流的组装.即使在均匀分布业务流输入下,LQF-SA机制在突发大小分布、组装效率和突发丢失率等方面也优于已提出的轮询调度组装（RR-SA）机制.     

WDM光网络中静态业务的P-cycle保护

P-cycle是一种预先配置的保护方式,在WDM网络中,P-cycle可以提供快速保护倒换和很高的资源利用率,具有很好的应用前景和研究价值.文章研究了P-cycle对于WDM网络中静态业务的保护情况,得出了基本的P-cycle配置策略下的仿真数据,并进行了分析.     

基于加权流量介数中心性的路网脆弱性分析——以无锡市为例

针对当前路网脆弱性研究中缺乏对真实交通状况考量的问题,在复杂网络理论的基础上,结合交通流量信息,提出了基于加权流量介数中心性的路网脆弱性分析方法。首先计算路网拓扑抽象图中各节点的最短路径介数中心性,然后使用流量数据对相应区域最短路径介数中心性加权,综合得到最终的脆弱性指标结果。以无锡市为例,对其实际交通路网脆弱性进行了计算,结果表明,该方法能综合反映静态全局路网结构与动态局部通行信息和现实交通情景下的路网脆弱性。     

基于显示连通贝叶斯网络的机场塔台地震易损性分析

将基于性能的多维易损性分析方法,结合显示连通贝叶斯网络,应用于机场塔台的多维易损性分析。考虑地震激励的不确定性,通过非线性时程分析获得结构响应数据;将塔台结构分为三个层次,每个层次按包含的层数分为相应的子层次。根据功能特性确定子层次的评价指标和极限状态,建立服从多元对数正态分布的概率地震需求模型;考虑各种极限状态之间的相关性,建立极限状态方程,确定失效域,通过蒙特卡洛法求得构件的超越概率;建立塔台结构的显示连通贝叶斯网络模型,利用层次分析法获得中间节点的条件概率表,利用MATLAB进行贝叶斯网络的推理计算,实现从单一层次的易损性到整体易损性的推理。     

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.