Fault diagnosis on large-scale and complex networks is a challenging task, as it requires efficient and accurate inference from huge data volumes. Active probing is a cost-efficient tool for fault diagnosis. However a...Fault diagnosis on large-scale and complex networks is a challenging task, as it requires efficient and accurate inference from huge data volumes. Active probing is a cost-efficient tool for fault diagnosis. However almost all existing probing-based techniques face the following problems: 1) performing inaccurately in noisy networks; 2) generating additional traffic to the network; 3) high cost computation. To address these problems, we propose an efficient probe selection algorithm for fault diagnosis based on Bayesian network. Moreover, two approaches which could significantly reduce the computational complexity of the probe selection process are provided. Finally, we implement the new proposed algorithm and a former representative probing-based algorithm (BPEA algorithm) on different settings of networks. The results show that, the new algorithm performs much faster than BPEA does without sacrificing the diagnostic quality, especially in large, noisy and multiple-fault networks.展开更多
It will show the feasibility of a Wireless Sensor Network (WSN) devoted to monitoring water basin, river, lake, and sea both on the surface and in depth. The swarm of floating probes can be programmed to periodically ...It will show the feasibility of a Wireless Sensor Network (WSN) devoted to monitoring water basin, river, lake, and sea both on the surface and in depth. The swarm of floating probes can be programmed to periodically sink some tens of meters below the surface, collecting data, characterizing water properties and then coming to the surface again. The life span of the probes may be assured by an on-board power supply or through batteries recharged by solar cells. The basic idea of the WSN is reported together with a detailed analysis of the operational constraints, the energy requirements, and the electronic and mechanical discussion.展开更多
提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得...提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得到系统响应传递函数。选取深海半潜浮式平台及系泊系统为研究对象,计算平台及其系泊系统在不同波浪工况作用下的时域耦合响应,以波高和系泊缆张力时程作为数据集,利用NARX神经网络结合谐波探测法辨识此系泊系统的响应传递函数。采用识别的传递函数预测系泊缆在不同海况下的张力响应,并与数值计算结果进行对比,证明NARX神经网络结合谐波探测法可较好地识别系泊浮体系统的非线性响应传递函数,并能够对系泊系统的张力响应进行准确预测。展开更多
可用带宽测量对于网络行为分析、网络服务质量(quality of service,简称QoS)的验证等有很重要的作用.现有可用带宽测量工作主要集中在端到端路径可用带宽测量,仅提供路径上承压链路(tight link)的信息,而不能提供其他关键链路的信息.为...可用带宽测量对于网络行为分析、网络服务质量(quality of service,简称QoS)的验证等有很重要的作用.现有可用带宽测量工作主要集中在端到端路径可用带宽测量,仅提供路径上承压链路(tight link)的信息,而不能提供其他关键链路的信息.为此,提出一种新颖的链路可用带宽测量算法LinkPPQ(trains of pairs of packet-quartets used to measure available bandwidth of arbitrary links),它采用由四探测分组结构对构成的探测序列,能够测量网络中任意链路的可用带宽,并跟踪该链路上背景流的变化.在仿真环境和实际网络环境下研究了LinkPPQ的性能.仿真结果表明,在几种不同背景流场景下,对于具有单狭窄链路的路径和具有多狭窄链路的路径,LinkPPQ都能够对各个链路的可用带宽进行有效的测量.绝大多数情况下测量误差小于30%,且具有较好的测量平稳性.实验网的实验结果也表明,LinkPPQ可以准确测量以下几种情况下的链路的可用带宽:a)从容量为10Mbps的链路准确地测量一条100Mbps链路的可用带宽;b)准确测量容量10倍于紧邻其后狭窄链路的容量的链路的可用带宽;c)准确测量具有多狭窄链路的路径上各狭窄链路的可用带宽.展开更多
基金supported by National Key Basic Research Program of China (973 program) under Grant No.2007CB310703Funds for Creative Research Groups of China under Grant No.60821001+1 种基金National Natural Science Foundation of China under Grant No. 60973108National S&T Major Project under Grant No.2011ZX03005-004-02
文摘Fault diagnosis on large-scale and complex networks is a challenging task, as it requires efficient and accurate inference from huge data volumes. Active probing is a cost-efficient tool for fault diagnosis. However almost all existing probing-based techniques face the following problems: 1) performing inaccurately in noisy networks; 2) generating additional traffic to the network; 3) high cost computation. To address these problems, we propose an efficient probe selection algorithm for fault diagnosis based on Bayesian network. Moreover, two approaches which could significantly reduce the computational complexity of the probe selection process are provided. Finally, we implement the new proposed algorithm and a former representative probing-based algorithm (BPEA algorithm) on different settings of networks. The results show that, the new algorithm performs much faster than BPEA does without sacrificing the diagnostic quality, especially in large, noisy and multiple-fault networks.
文摘It will show the feasibility of a Wireless Sensor Network (WSN) devoted to monitoring water basin, river, lake, and sea both on the surface and in depth. The swarm of floating probes can be programmed to periodically sink some tens of meters below the surface, collecting data, characterizing water properties and then coming to the surface again. The life span of the probes may be assured by an on-board power supply or through batteries recharged by solar cells. The basic idea of the WSN is reported together with a detailed analysis of the operational constraints, the energy requirements, and the electronic and mechanical discussion.
文摘提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得到系统响应传递函数。选取深海半潜浮式平台及系泊系统为研究对象,计算平台及其系泊系统在不同波浪工况作用下的时域耦合响应,以波高和系泊缆张力时程作为数据集,利用NARX神经网络结合谐波探测法辨识此系泊系统的响应传递函数。采用识别的传递函数预测系泊缆在不同海况下的张力响应,并与数值计算结果进行对比,证明NARX神经网络结合谐波探测法可较好地识别系泊浮体系统的非线性响应传递函数,并能够对系泊系统的张力响应进行准确预测。
文摘可用带宽测量对于网络行为分析、网络服务质量(quality of service,简称QoS)的验证等有很重要的作用.现有可用带宽测量工作主要集中在端到端路径可用带宽测量,仅提供路径上承压链路(tight link)的信息,而不能提供其他关键链路的信息.为此,提出一种新颖的链路可用带宽测量算法LinkPPQ(trains of pairs of packet-quartets used to measure available bandwidth of arbitrary links),它采用由四探测分组结构对构成的探测序列,能够测量网络中任意链路的可用带宽,并跟踪该链路上背景流的变化.在仿真环境和实际网络环境下研究了LinkPPQ的性能.仿真结果表明,在几种不同背景流场景下,对于具有单狭窄链路的路径和具有多狭窄链路的路径,LinkPPQ都能够对各个链路的可用带宽进行有效的测量.绝大多数情况下测量误差小于30%,且具有较好的测量平稳性.实验网的实验结果也表明,LinkPPQ可以准确测量以下几种情况下的链路的可用带宽:a)从容量为10Mbps的链路准确地测量一条100Mbps链路的可用带宽;b)准确测量容量10倍于紧邻其后狭窄链路的容量的链路的可用带宽;c)准确测量具有多狭窄链路的路径上各狭窄链路的可用带宽.
