动态小波在变压器励磁涌流识别中的应用研究

电力变压器的励磁涌流识别一直是变压器保护的一个重要问题。动态小波是小波函数和傅里叶函数的组合,固此与常规小波变换相比。检测故障突变信号的能力更强。文中将动态小波运用于励磁涌流识别中,通过仿真实验证明了该方法提高了勋磁涌流鉴别的可靠性。     

浅议各类数据的备份与归档管理

本文从各类数据在计算机中存在的状态以及在存储过程中的安全问题,对数据的安全性和保护性进行了论证。     

基于UML模型的场景测试技术

UML代表着软件建模的发展趋势,对基于UML模型的测试技术研究具有现实意义。本文采用基于UML模型的场景测试技术生成测试用例。     

飞机防滑刹车检测系统设计

防滑刹车系统是飞机的重要部分之一,它控制着飞机降落、着陆和减速的全过程;为了提高飞机防滑刹车系统检测的自动化程度、效率和检测精度,使用专用的检测设备是十分必要的;该系统采用独特的设计方法完成了对整个复杂系统的检测,以计算机为核心的方案,使得检测系统的智能化程度和灵活性更好;应用结果表明,该检测系统运行稳定可靠,检测精度高,能满足现有的检测需求。     

基于ATmega 128单片机的数字化地下管线定位仪

地下管线被视为城市的生命线。分析了地下管线定位仪的基本原理,并在此基础上详细介绍了基于ATmega 128单片机的数字化地下管线定位仪的硬件实现和软件实现,重点分析了基于磁保持继电器的分时供电电源电路。ATmega 128单片机实现了地下管线定位仪的数字化,使得定位仪能快速、准确地对地下管线进行水平定位和垂直定深。     

A piecewise analysis method to stability analysis of linear continuous/discrete systems with time‐varying delay

The delay‐dependent stability problem of linear continuous/discrete systems with time‐varying delay is investigated based on a piecewise analysis method (PAM). In the method, the variation interval of the time delay is firstly divided into several subintervals. By checking the variation of the Lyapunov functional in every subinterval, some new delay‐dependent stability criteria are derived. Several numerical examples show that our method can lead to much less conservative results than those in the existing references. Moreover, when the number of the divided subintervals increases, the corresponding criteria can provide an improvement on the results. Copyright © 2008 John Wiley & Sons, Ltd.     

Design of a Scalable Multicast Scheme With an Application-Network Cross-Layer Approach

This paper develops an efficient and scalable multicast scheme for high-quality multimedia distribution. The traditional IP multicast, a pure network-layer solution, is bandwidth efficient in data delivery but not scalable in managing the multicast tree. The more recent overlay multicast establishes the data-dissemination structure at the application layer; however, it induces redundant traffic at the network layer. We propose an application-oriented multicast (AOM) protocol, which exploits the application-network cross-layer design. With AOM, each packet carries explicit destinations information, instead of an implicit group address, to facilitate the multicast data delivery; each router leverages the unicast IP routing table to determine necessary multicast copies and next-hop interfaces. In our design, all the multicast membership and addressing information traversing the network is encoded with bloom filters for low storage and bandwidth overhead. We theoretically prove that the AOM service model is loop-free and incurs no redundant traffic. The false positive performance of the bloom filter implementation is also analyzed. Moreover, we show that the AOM protocol is a generic design, applicable for both intra-domain and inter-domain scenarios with either symmetric or asymmetric routing.     

Large-scale network intrusion detection based on distributed learning algorithm

As network traffic bandwidth is increasing at an exponential rate, it’s impossible to keep up with the speed of networks by just increasing the speed of processors. Besides, increasingly complex intrusion detection methods only add further to the pressure on network intrusion detection (NIDS) platforms, so the continuous increasing speed and throughput of network poses new challenges to NIDS. To make NIDS usable in Gigabit Ethernet, the ideal policy is using a load balancer to split the traffic data and forward those to different detection sensors, which can analyze the splitting data in parallel. In order to make each slice contains all the evidence necessary to detect a specific attack, the load balancer design must be complicated and it becomes a new bottleneck of NIDS. To simplify the load balancer this paper put forward a distributed neural network learning algorithm (DNNL). Using DNNL a large data set can be split randomly and each slice of data is presented to an independent neural network; these networks can be trained in distribution and each one in parallel. Completeness analysis shows that DNNL’s learning algorithm is equivalent to training by one neural network which uses the technique of regularization. The experiments to check the completeness and efficiency of DNNL are performed on the KDD’99 Data Set which is a standard intrusion detection benchmark. Compared with other approaches on the same benchmark, DNNL achieves a high detection rate and low false alarm rate.     

嵌入式操作系统的时钟机制的研究

在实时操作系统中,无论是实时响应并处理来自各个被控对象的实时信息,还是对任务执行时间的管理、资源的限时等待等,都需要时钟的参与。因此,准确而又具有足够精度的时钟对于实时系统是必不可少的。根据普通嵌入式系统内核中的时钟机制的特点,分析了实时操作系统中的有关时钟机制改进的具体方法与特点,并提出了三种多粒度时钟机制：UTIME机制、RFRTOS TIMER机制、RTOS KERNEL TIMER机制,并且分析了与时钟机制密切相关的中断服务程序。     

曼彻斯特编码技术在测井数据传输中的应用研究

基于曼彻斯特码编码技术文章提出一种适用于石油测井下行数据传输链路的设计方案。采用Verilog HDL设计的曼彻斯特编、译码器,在ALTERA公司的QuartusⅡ6．0软件平台上完成了仿真,并在FPGA器件上实现了硬件测试．最后下行数据传输链路的发送端和接收端在6km的电缆模拟器上完成了系统测试。通过计算机仿真和工程实现验证了该设计方法的正确性和实用性。