一种数据网格存储模型与并行下载调度算法

针对在数据网格中创建多副本虽可有效提升下载速度、降低网络流量,但多副本创建会带来大量存储开销和网络流量开销,以及基于GridFTP协议的各种并行下载算法虽可进一步提升下载速度,但仍不能解决多副本对存储空间和网络流量的影响的问题,提出了一个能保证数据的完整性、存储的可靠性和降低存储空间的数据网格存储模型,并基于该存储模型和GridFTP协议,提出了一个并行下载调度算法。实验表明,该算法只需要较少的冗余便可达到现有的针对全副本的并行下载算法可达到的理想下载速度,取得较好的效果,实现并行快速传输、节约存储空间和降低网络流量的目标。     

基于ZigBee的无线动态应变测量

针对传统应变测量系统不便于安装、设备体积大和需要大量布线等缺点,采用ZigBee无线传感器网络和零漂移数字可编程增益仪表放大器等技术设计并实现了一种无线动态应变测量系统.根据电阻应变片测量原理,重点讨论了应变测量的硬件实现以及zigBee精简协议栈应用层软件编程.系统由精密直流激励源、可编程仪表放大电路、DAC调零电路、射频电路组成.实际应用表明,该设计达到了技术方案要求并已成功应用于桥梁和机械应变测试系统中.     

虚拟仪器高温应变测量系统

高温环境下进行应变测量时,温度会对应变片的灵敏度、热输出、零漂和蠕变等参数特性产生影响,需要采取有效措施来保证测量结果的准确度。该文针对高温对应变片参数特性的影响,采用标定数据跟踪及自动处理的方法对应变测量数据进行修正补偿,并开发配套的高温应变测量系统。该测试系统可对应变测试数据进行自动补偿修正,从而得到满足测量准确度要求的应变数据,此外该系统还可实现测试数据的实时显示、存储、回放和打印输出等功能。试验验证表明:该测试系统对高温应变测量具有较好的适用性,能有效地提高高温应变测量的准确度。     

硬质合金磁饱和测量的高精度数据处理方法

设计并验证了硬质合金磁饱和测量的一种高精度的数据处理方法,包括数字滤波、数字积分、数据拟合、系统调零和补偿等。滤波采用基于窗函数设计的有限长脉冲响应数字滤波器,积分采用复化Newton-Cotes积分算法,数据拟合采用改进的最小二乘法,系统校准采用首次调零和负值补偿方法。与传统硬质合金磁饱和测量的数据处理方法相比,该方法具有处理结果精度高、数据重复性好等特点。     

网络流量分析与应用

利用Sniffer分析当前校园网中网络流量的类型,对不同流量测量的方法进行介绍和对比,提出网络流量测量的必要性,对进行流量测量时使用的Sniffer及相关实现技术和标准进行探讨。     

入侵检测系统中用户级报文传输机制研究

在分析了骨干网入侵检测系统性能瓶颈的基础上，提出并实现了一种面向入侵检测系统的用户级报文传输机制——ULMM(User-Levd Messaging Mechanism)。该通讯机制采用零拷贝技术，能够将传统TCP／IP协议栈从操作系统中旁路掉，利用异步DMA的方式实现用户层报文传输，从而有效地降低了入侵检测系统的通讯开销。试验证实，采用该通讯机制，大流量网络环境下的入侵检测系统可以获得很高的报文处理速率和CPU空闲率。     

网络测量的研究和测量仪的设计

研究现代网络性能就要对网络进行测量,通过网络测量的数据对网络性能进行全面的分析,针对网络测量的常用的方法和测量指标进行研究,并对网络流量测量仪进行设计。     

基于GPRS的手机网络流量计量技术研究

基于GPRS技术和当前手机网络流量计费原理的现状,提出了基于GPRS的手机网络流量计量标准,给出了满足用户的知情权和证明通信运营服务商流量记录的公平公正性的测量方案,并引入了软计量器具的概念.     

数字存储示波器垂直系统测量结果不确定度评定方法探讨

文章主要对数字存储示波器误差来源进行了分析,对测量方法及测量原理进行了阐述,探讨性的给出了数字存储示波器垂直系统关键技术指标测量结果不确定度评定的方法,并就整个不确定度的评定过程进行分析给出结论。     

气动力作用下飞行体的冲击加速度测量

介绍存储测试系统的研制情况,阐述存储测试系统的工作原理和安装过程中的冲击隔离方法,并将其用于空气炮的内弹道加速度测量．采用碰靶时的接电技术测出了飞行体以300m／s的速度侵彻混凝土靶板时的加速度,并用存储测试系统对飞行体以600m／s的速度侵彻混凝土靶板时的加速度进行了探索性的测量,给出了初步测试结果．     

城市道路网运行实时动态评价理论和技术研究

从道路网运行的基本特性入手,着重分析了路网运行的随机波动性、递延传导效应和周期规律性。在此基础上,剖析了迄今国内外一直沿用的道路"负荷度"路网评价理论的局限性,提出一套适用于路网整体实时动态评价的理论和技术方法,解决了无盲区实时数据采集与处理、路网运行时空动态分析、评价指标阈值标定等关键技术难题,为交通战略规划、实时动态路网功况诊断等提供了全新的技术手段,北京市的实证研究初步证明了所提出的理论和技术体系科学、有效、实用。     

Combined methodology for measurement of available bandwidth and link capacity in wired packet networks

Accurate measurement of network parameters such as available bandwidth (ABW), link capacity, delay, packet loss and jitter are used to support and monitor several network functions, for example traffic engineering, quality-of-service (QoS) routing, end-to-end transport performance optimisation and link capacity planning. However, proactive network measurement schemes can impact both the data traffic and the measurement process itself, affecting the accuracy of the estimation if a significant amount of probe traffic is injected into the network. In this work, the authors propose two measurement schemes, one for measuring ABW and the other for measuring link capacity, both of them use a combination of data probe packets and Internet control messaging protocol (ICMP) packets. Our schemes perform ABW and link-capacity measurements in a short time and with a small amount of probe traffic. The authors show a performance study of our measurement schemes and compare their accuracy to those of other existing measurement schemes and also show that the proposed schemes achieve shorter convergence time than other existing schemes and high accuracy.     

Measurement and Control of Packet Loss Probability for MPLS VPN Services

Multiprotocol-label-switch virtual private network (VPN) service has emerged as a value-added cost-effective VPN- based service, and its market opportunity is tremendous for network service providers (NSPs). Quality of service (QoS) control is one of the critical issues to be addressed, for NSPs to maximize their profits. This paper proposes a QoS control system that combines the service admission control (SAC) and the rate feedback control together to maintain the preset QoS parameters (the packet loss probability case) in the provider's backbone network. First, one measurement module employs distributed intelligent agents to measure the VPN traffic from all of the heterogenous remote network nodes. The packet loss probability is then estimated on line by applying the large-deviation theory. SAC strategy decides the amount of permitted VPN services, while the feedback controller dynamically throttles ingress traffic rates of instantiated VPN services to maintain the preset packet loss targets. The performance is evaluated in the live network of the National Capital Institute of Telecommunications (NCIT$ast$net2). Numeric results show that, under appropriately selected control parameters, it is possible to maintain the network operation within a prescribed loss limit.     

一种结合网络编码的路径代价衡量方法

针对无线路由协议中的路径代价衡量问题,结合网络编码改善无线节点信息互换的思想,提出了一种结合网络编码的路径代价衡量方法--RMNC,其核心思想是利用流量参数反映信息流的网络编码"搭乘"程度和逐节点计算路径的代价.通过将传输流流量参数和路径中节点左右链路信息流流量参数进行运算,获得路径上的各个节点的传输代价;网络中某一条路径的代价等于组成这条路径的节点传输代价之和,通过比较不同路径的逐节点计算代价值,获得最短路径.分析和模拟测试结果表明,RMNC可以有效地获得结合网络编码的最短路径,达到提高传输性能的目的.尽管传输延时有所增加,但可以接受,方法可行.     

AI设计下的智能驾驶场景文本识别技术

目的 交通标志识别作为智能驾驶、交通系统研究中的一项重要内容,具有较大的理论价值和应用前景.尤其是文本型交通标志,其含有丰富的高层语义信息,能够提供极其丰富的道路信息.因此通过设计并实现一套新的端到端交通标志文本识别系统,达到有效缓解交通拥堵、提高道路安全的目的.方法 系统主要包括文本区域检测和文字识别两个视觉任务,并基于卷积神经网络的深度学习技术实现.首先以ResNet-50为骨干网络提取特征,并采用类FPN结构进行多层特征融合,将融合后的特征作为文本检测和识别的共享特征.文本检测定位文本区域并输出候选文本框的坐标,文字识别输出词条对应的文本字符串.结果 通过实验验证,系统在Traffic Guide Panel Dataset上取得了令人满意的结果,行识别准确率为71.08％.结论 端到端交通标志文本识别非常具有现实意义.通过卷积神经网络的深度学习技术,提出了一套端到端交通标志文本识别系统,并在开源的Traffic Guide Panel Dataset上证明了该系统的优越性.     

Multicommodity Network Expansion under Elastic Demands

We analyze some issues of network design and bandwidth allocation in telecommunication systems with congestible resources. The work is closely related to network monitoring and traffic measurement functions that must be carried out on line, in order to overcome congestion caused by an unfavorable traffic pattern or by a failure. In addition to the traditional use of routing controls, our approach achieves network efficiency with capacity assignment and also imposing variable prices for the consumers. A generalized Benders decomposition method is applied to a mixed integer nonlinear programming formulation of the integrated problem of network design and operation. The method exploits the nature of the continuous subproblem, that is a large-scale convex network flow problem with demands sensitive to commodity prices. Some numerical experience suggests that the method is usefull to address both questions of global optimality and competitive pricing in such systems.     

An expert system for network management control

Abstract

Due to the varying time and the complexity of the commercial telecommunication network configuration, routing of the communication traffic becomes very important for telecommunication systems. No one can keep in mind all the complex routing plans used in networks, so it is hard to quickly take proper actions while the switching node is being blocked.

In this paper, we propose an expert system which can collect traffic data, monitor network status, reason and take appropriate actions for extreme traffic congestion on a network just as a network management expert can do. It would certainly streamline the whole network control procedure, and. provide dynamic routing functions based on the original static routing method adopted in Taiwan. It does improve both network efficiency and network reliability.     

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques. The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic, respectively. Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks, with high accuracy. Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis (PCA). The proposed model incorporates stacked ensemble model extra boosting forest (EBF) which is a combination of tree-based models such as extra tree classifier, gradient boosting classifier, and random forest using a stacked ensemble approach. Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes, respectively.     

A Fused Machine Learning Approach for Intrusion Detection System

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnectivity of networks has brought various complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities. An intrusion detection system controls the flow of network traffic with the help of computer systems. Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic. For this purpose, when the network traffic encounters known or unknown intrusions in the network, a machine-learning framework is needed to identify and/or verify network intrusion. The Intrusion detection scheme empowered with a fused machine learning technique (IDS-FMLT) is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks. The proposed IDS-FMLT system model obtained 95.18% validation accuracy and a 4.82% miss rate in intrusion detection.     

一种道路交通安全神经网络评价模型的构建

在综合分析国内外交通安全评价研究的基础上,针对中国道路交通安全现状,提出了基于一个城市行政分区的道路交通安全评价指标和评价模型,并针对我国城市道路交通安全的现状及存在问题,提出了采用基础数据、交通安全管理、公众安全评价3个方面共17项指标组成的面向城市道路交通安全可持续发展的评价指标集;构建了基于BP神经网络的评价模型;并结合算例详细分析了其计算方法。