基于LS-SVM的电力通信网性能劣化评估与预测模型研究

随着电力通信网络规模越来越大,运行维护人员对通信网运行状态的实时有效监控,对设备故障的快速准确判断越来越困难,需要对通信网运行的健康状态进行科学评估,以及对网络性能的劣化进行趋势预测,从而可以提前预知网络可能存在的隐患。论文充分利用通信网现有的海量状态监测数据,提出基于健康状态数据的电力通信网性能劣化评估模型,在此基础上利用最小二乘支持向量机(LS-SVM)性能劣化时间序列进行预测,预警通信网的异常状态,提高通信网运行维护的水平,减少故障导致的断网损失。     

通信动力环境下电网运行状态在线自动监测技术研究

为了提高在监测电网运行状态过程中识别状态异常数据的能力，降低能量开销，延长电网的运行生命周期，提出在通信动力环境下基于分层协同的电网运行状态在线自动监测技术。首先根据测量数据在采集终端的特征分布，构建测量数据的二维特征分布集，使用高维结构数据重组，得到电力大数据分布参数集。在发电、输电、变电、配电、用电及调度等环节中进行电网运行状态特征估计，完成电网运行状态数据特征误差补偿。根据收集的大量电网运行状态数据，采用分层协同调度方法实现电网运行状态特征的多维度检测和参数提取。根据电力系统收集、传输的测量数据的状态特征提取结果，采用主成分分析与小波变化结合的方法实现对电网运行状态特征预测和自动监测。实验结果得知，采用该方法进行通信动力环境下电网运行状态检测，识别状态异常数据特征强，且能量开销低，在4 kJ左右，通信动力环境下电网运行生命周期较长，达到158 000 h以上，电力数据传输和调度能力较强，对于用电特征及高频波动分量监测的泛化能力较强。     

基于仿真的地域通信网实时规划与管理系统设计

本文提出了一种基于仿真的实时规划与管理方式,将仿真系统与地域通信网中的一些节点构成半实物仿真,根据网络的当前状态,快速仿真网络运行,预测网络未来的性能,并持续地对网络进行优化,以此来提高地域通信网性能,改善服务质量。     

OPNET在ATM通信网QoS和性能评估上的应用

网络仿真是一种全新的通信网络规划、设计与分析技术,它能够验证分析实际网络建设方案的有效性和可行性,并可为通信网络规划与设计提供定量依据.本文在介绍OPNET软件的层次化通信网建模方法基础上,给出应用OPNET进行通信网服务质量(QoS)和性能仿真分析的具体实现方法,从网络技术机制、网络性能以及QoS等方面对网络设计方案进行综合评估.并以ATM网为例,应用该方法进行了QoS和性能仿真分析,分析了ATM网络可用比特率ABR和恒定比特率CBR两种服务类型的性能以及服务质量,仿真结果显示,与实际网络运行结果一致,表明该方法的有效性.     

基于多波动尺度的两层包级自适应抽样

在高速Internet链路的流量测量中,包抽样技术能有效减少被测数据量以节省各种可用资源。文章关注如何提高流量突发和波动周期的抽样精度,提出了一种基于多波动尺度的两层包级自适应抽样方法。该方法能够根据当前流量的突发频度和波动性动态调节每包抽样概率,调节粒度精细。通过在真实流量跟踪下与静态随机抽样方法的对比,两层自适应抽样方法显示了较强的实时捕获流量速率的波动变化和改善抽样精度的能力。     

一种基于动态反馈的三级网状态测量机制

随着三级网发展为集数据、语音、视频于一体的综合信息网,只对某个或某几个性能参数的测量已经不能满足分析复杂的网络状态及其产生原因的需要。该文提出了一种基于动态反馈的三级网状态测量机制。该机制通过守护测量获取网络的基本状态,然后根据获取的测量结果判定是否需要对网络状态进行进一步的测量,若需要,则自动选择待测参数和测量手段,发起反馈测量,获取进一步的网络状态信息以分析现有状态出现的原因。该测量机制能够反映网络故障等产生的深层次原因,有利于及时准确的故障设备,保证网络的正常运行。     

电力骨干SDH通信网可靠运行评价指标体系研究与应用

本文从电力骨干SDH通信网的网络架构、网络资源、承载业务三个方面开展了网络可靠运行指标研究工作,形成三大类12个指标;同时初步选用国家电网公司一级骨干通信网某地区网络对指标进行了计算,根据计算结果对该地区网络开展了优化改造工作,有效提升了该地区网络的安全运行水平。     

基于MMFD-FCM的退化状态识别方法及其应用研究

为了有效地对机械设备运行状态进行监测,进而对其性能退化状态进行识别,提出一种基于形态多重分形维数(MMFD)与模糊C均值聚类(FCM)的性能退化状态识别方法;该方法首先计算机械设备振动信号的形态多重分形维数,以此作为性能退化特征指标;该特征指标能够有效反映峰值在振动信号中概率分布的不均匀程度,从而定量描述振动信号的性能退化状态,并且与多重形态分形维数相比,利用数学形态学计算的MMFD精度更高,计算速度更快;在此基础上,鉴于不同退化状态之间的模糊性,针对性地采用模糊C均值聚类方法对特征指标进行模糊聚类,从而有效识别性能退化状态;将该方法应用于滚动轴承全寿命周期振动信号中,分析结果验证了该方法的有效性。     

网络控制系统的一种变采样周期动态调度策略

提出一种基于网络运行状态的网络控制系统动态调度器的设计方法．首先利用监测器在线获取当前的网络利用率、网络诱导误差和数据包执行时间，基于获取的网络状态，预测下一监测周期内的网络利用率和数据包执行时间．然后按照网络运行性能和控制性能的需求，基于网络利用率和数据包执行时间的预估值分配网络资源，计算控制系统新的采样周期．当数据包传输发生冲突时，采用MEF（Maximum Error First）作为辅助调度策略，确定数据包的发送优先级．最后通过一组仿真结果验证了所设计的动态调度器的有效性．     

基于自相似特性的流量测量采样方法

针对大型网络流量测量中测量数据量巨大、不能有效地描述流量特征的问题,本文将近年来网络研究的热点自相似性运用在流量测量中。根据流量变化对自相似参数H的影响,提出了一种基于网络的自相似特性的流量测量采样方法。该方法降低了流量测量的采样频率,能够及时、准确地发现网络异常,并能使测量数据更准确地刻画流量特征。     

A novel Bayesian classification for uncertain data

Data uncertainty can be caused by numerous factors such as measurement precision limitations, network latency, data staleness and sampling errors. When mining knowledge from emerging applications such as sensor networks or location based services, data uncertainty should be handled cautiously to avoid erroneous results. In this paper, we apply probabilistic and statistical theory on uncertain data and develop a novel method to calculate conditional probabilities of Bayes theorem. Based on that, we propose a novel Bayesian classification algorithm for uncertain data. The experimental results show that the proposed method classifies uncertain data with potentially higher accuracies than the Naive Bayesian approach. It also has a more stable performance than the existing extended Naive Bayesian method.     

Sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle

In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors’ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.     

Efficient inferencing for sigmoid Bayesian networks by reducing sampling space

A sigmoid Bayesian network is a Bayesian network in which a conditional probability is a sigmoid function of the weights of relevant arcs. Its application domain includes that of Boltzmann machine as well as traditional decision problems. In this paper we show that the node reduction method that is an inferencing algorithm for general Bayesian networks can also be used on sigmoid Bayesian networks, and we propose a hybrid inferencing method combining the node reduction and Gibbs sampling. The time efficiency of sampling after node reduction is demonstrated through experiments. The results of this paper bring sigmoid Bayesian networks closer to large scale applications.     

基于深度学习LDAMP网络的量子状态估计

设计出一种基于学习去噪的近似消息传递(Learned denoising-based approximate message passing, LDAMP)的深度学习网络,将其应用于量子状态的估计.该网络将去噪卷积神经网络与基于去噪的近似消息传递算法相结合,利用量子系统输出的测量值作为网络输入,通过设计出的带有去噪卷积神经网络的LDAMP网络重构出原始密度矩阵,从大量的训练样本中提取各种不同类型密度矩阵的结构特征,来实现对量子本征态、叠加态以及混合态的估计.在对4个量子位的量子态估计的具体实例中,分别在无和有测量噪声干扰情况下,对基于LDAMP网络的量子态估计进行了仿真实验性能研究,并与基于压缩感知的交替方向乘子法和三维块匹配近似消息传递等算法进行估计性能对比研究.数值仿真实验结果表明,所设计的LDAMP网络可以在较少的测量的采样率下,同时完成对4种量子态的更高精度估计.     

网络断层扫描技术的研究与发展

当今网络的大尺度、不协作、异质和分布式管理等特点,使得网络状态与性能的直接动态测量很困难.研究针对网络中不能直接测量的特性参数的统计推断方法十分重要.以通信网络、断层扫描和统计学理论相结合的网络断层扫描是一种全新的、最具前景的网络性能测量与推断技术,它通过边缘测量推断不可观测的网络行为且不要求网络内部元素和边缘节点的协作.简要介绍了网络断层扫描的基本概念与数学模型,从数据测量技术、统计推断技术两个方面论述了网络断层扫描技术的研究现状和一些有价值的新发展,并指出了进一步研究的方向.     

基于抽样流记录的RTT估计

往返时延(RTT)是网络测量中的一个重要测度,是刻画网络性能的重要指标。传统的RTT测量都是基于报文的,需要专门的主动或被动测量平台的支持。提出一种新的 RT T 估计方法,仅使用现有路由器设备提供的流记录,不需要额外的网络测量设施。通过对 TCP 块状流传输特性的分析,分别建立了当套接字缓冲区长度与带宽延迟积BDP相对较小、较大和相近这3种情况下的RTT估计模型。实验结果表明,这些模型都能很好地完成RTT估计。同时,由于在估计当中只使用了流持续时间和总报文两个变量,因此,该方法同样适用于以抽样流记录为输入的环境,能够有效地应用于现有的大规模主干网环境的网络检测与管理。     

Communication Benchmarking and Performance Modelling of MPI Programs on Cluster Computers

This paper gives an overview of two related tools that we have developed to provide more accurate measurement and modelling of the performance of message-passing communication and application programs on distributed memory parallel computers. MPIBench uses a very precise, globally synchronised clock to measure the performance of MPI communication routines. It can generate probability distributions of communication times, not just the average values produced by other MPI benchmarks. This allows useful insights to be made into the MPI communication performance of parallel computers, and in particular how performance is affected by network contention. The Performance Evaluating Virtual Parallel Machine (PEVPM) provides a simple, fast and accurate technique for modelling and predicting the performance of message-passing parallel programs. It uses a virtual parallel machine to simulate the execution of the parallel program. The effects of network contention can be accurately modelled by sampling from the probability distributions generated by MPIBench. These tools are particularly useful on clusters with commodity Ethernet networks, where relatively high latencies, network congestion and TCP problems can significantly affect communication performance, which is difficult to model accurately using other tools. Experiments with example parallel programs demonstrate that PEVPM gives accurate performance predictions on commodity clusters. We also show that modelling communication performance using average times rather than sampling from probability distributions can give misleading results, particularly for programs running on a large number of processors.     

TCP Yuelu:一种基于有线/无线混合网络端到端的拥塞控制机制

无线链路传输数据的比特率出错导致TCP协议在有线／无线混合网络环境下性能低下,在改进算法TCPReno的基础上,文章提出了一种适用于有线／无线混合网络的拥塞控制机制,该机制包括一种分阶段平滑慢启动机制,改善了突发流量对网络性能的损害,引入网络测量技术获得了往返时间（RTT）、网络带宽、瓶颈链路队列长度等网络状态参数,区分网络拥塞和无线链路比特差错,避免了终端节点对网络状态不了解产生的盲目行为,有效改进了TCP的加性增加乘性减少（AIMD）窗口调节机制,提高了网络性能．同时,在仿真软件NS2中实现了该算法,进行了大量的仿真实验,实验结果表明TCP Yuelu有效降低了网络抖动,提高了网络传输性能,并保持了良好的公平性和对其它TCP流的友好性．     

Estimation of a Population Size in Large-Scale Wireless Sensor Networks

Efficient estimation of population size is a common requirement for many wireless sensor network applications. Examples include counting the number of nodes alive in the network and measuring the scale and shape of physically correlated events. These tasks must be accomplished at extremely low overhead due to the severe resource limitation of sensor nodes, which poses a challenge for large-scale sensor networks. In this article we design a novel measurement technique, FLAKE based on sparse sampling that is generic, in that it is applicable to arbitrary wireless sensor networks (WSN). It can be used to efficiently evaluate system size, scale of event, and other global aggregating or summation information of individual nodes over the whole network in low communication cost. This functionality is useful in many applications, but hard to achieve when each node has only a limited, local knowledge of the network. Therefore, FLAKE is composed of two main components to solve this problem. One is the Injected Random Data Dissemination (Sampling) method, the other is sparse sampling algorithm based on Inverse Sampling, upon which it improves by achieving a target variance with small error and low communication cost. FLAKE uses approximately uniform random data dissemination and sparse sampling in sensor networks, which is an unstructured and localized method. At last we provide experimental results demonstrating the effectiveness of our algorithm on both small-scale and large-scale WSNs. Our measurement technique appears to be the practical and appropriate choice.     

A data-based framework for fault detection and diagnostics of non-linear systems with partial state measurement

A novel framework based on the use of dynamic neural networks for data-based process monitoring, fault detection and diagnostics of non-linear systems with partial state measurement is presented in this paper. The proposed framework considers the presence of three kinds of states in a generic system model: states that can easily be measured in real time and in-situ, states that are difficult to measure online but can be measured offline to generate training data, and states that cannot be measured at all. The motivation for such a categorization of state variables comes from a wide class of problems in the manufacturing and chemical industries, wherein certain states are not measurable without expensive equipments or offline analysis while some other states may not be accessible at all. The framework makes use of a recurrent neural network for modeling the hidden dynamics of the system from available measurements and uses this model along with a non-linear observer to augment the information provided by the measured variables. The performance of the proposed method is verified on a synthetic problem as well as a benchmark simulation problem.