Stochastic stability of networked control systems with network-induced delay and data dropout

This paper deals with the stochastic stability of networked control systems with the presence of network- induced delay and transmitted data dropout. Based on the Lyapunov approach, sufficient conditions for the mean-square stability of the networked control system are derived subject that the sequence of transmission interval is driven by an identically independently distributed sequence and by a finite state Markov chain, respectively. Stabilization controllers are constructed in terms of linear matrix inequalities correspondingly. An example is provided to illustrate our results.

Controller design for networked control system with data packet dropout and transmission delays

In this paper, the stabilization problem for a class of networked control systems （NCSs） with data packet dropouts and transmission time delays is considered, where the delays are time-varying and uncertain, the data packet dropout is modeled as a two-state Markov chain. To compensate the lost packet, a data packet dropout compensator is established. Thus a more realistic model for such NCSs is presented. Sufficient conditions for the stabilization of the new resulting system are derived in the form of linear matrix inequalities （LMIs）. Numerical example illustrates the solvability and effectiveness of the results.

PRECESION： progressive recovery and restoration planning of interdependent services in enterprise data centers

The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].

Quantized dynamic output feedback control for networked control systems

The problem of the quantized dynamic output feedback controller design for networked control systems is mainly discussed. By using the quantized information of the system measurement output and the control input, a novel networked control system model is described. This model includes many networkinduced features, such as multi-rate sampled-data, quantized signal, time-varying delay and packet dropout. By constructing suitable Lyapunov-Krasovskii functional, a less conservative stabilization criterion is established in terms of linear matrix inequalities. The quantized control strategy involves the updating values of the quantizer parameters μi（i = 1, 2）（μi take on countable sets of values which dependent on the information of the system measurement outputs and the control inputs）. Furthermore, a numerical example is given to illustrate the effectiveness of the proposed method.

City Routing Velocity Estimation Model under theEnvironment of Lack of Floating Car Data

After introducing the principle of float car data (FCD), this paper gives the primary flow of pre-handing and map- matching of the FCD. After analyzing the percentage of coverage of FCD on the road network, large quantity of heritage database of routing status is used to estimate the routing velocity when lack of FCD on parts road segments. Multi liner regression model is then put forwarded by considering the spatial correlativity among the road network, and some model parameters are deduced when time series is classified in day and week. Besides, error of velocity probability and error of status probability are achieved based on the result from field testing while the feasibility and reliability of the velocity estimation model is obtained as well. Finally, as a case study in Shanghai center area, the whole routing velocity in the road network is estimated and published in real time.

Alternative approaches for estimating missing climate data:application to monthly precipitation records in South- Central Chile

Background: Over the last decades interest has grown on how climate change impacts forest resources. However,one of the main constraints is that meteorological stations are riddled with missing climatic data. This study compared five approaches for estimating monthly precipitation records: inverse distance weighting(IDW), a modification of IDW that includes elevation differences between target and neighboring stations(IDW_m), correlation coefficient weighting(CCW), multiple linear regression(MLR) and artificial neural networks(ANN).Methods: A complete series of monthly precipitation records(1995-2012) from twenty meteorological stations located in central Chile were used. Two target stations were selected and their neighboring stations, located within a radius of25 km(3 stations) and 50 km(9 stations), were identified. Cross-validation was used for evaluating the accuracy of the estimation approaches. The performance and predictive capability of the approaches were evaluated using the ratio of the root mean square error to the standard deviation of measured data(RSR), the percent bias(PBIAS), and the NashSutcliffe efficiency(NSE). For testing the main and interactive effects of the radius of influence and estimation approaches,a two-level factorial design considering the target station as the blocking factor was used.Results: ANN and MLR showed the best statistics for all the stations and radius of influence. However, these approaches were not significantly different with IDW_m. Inclusion of elevation differences into IDW significantly improved IDW_m estimates. In terms of precision, similar estimates were obtained when applying ANN, MLR or IDW_m, and the radius of influence had a significant influence on their estimates, we conclude that estimates based on nine neighboring stations located within a radius of 50 km are needed for completing missing monthly precipitation data in regions with complex topography.Conclusions: It is concluded that approaches based on ANN, MLR and IDWm had the best performance in two sectors located in south-central Chile with a complex topography. A radius of influence of 50 km(9 neighboring stations) is recommended for completing monthly precipitation data.

复杂工况下航空电子通信网络数据传输延时补偿研究

为了解决航空电子通信网络数据在复杂工况下受到环境噪声干扰的影响大以及数据传输延时过大的问题,以复杂工况为背景,研究航空电子通信网络数据传输延时补偿。采用非线性变换算法分析通信网络数据状态,得到影响延时因素的离散性值及线性变化。建立数据传输延时状态模型,分析每个节点实时状态,计算数据传输状态和延时参数,并求解二者间的关系。将数据传输延时问题抽象为先到先服务的排队服务,并计算数据发送、到达速率与延时时间的关系。在此基础上,求解最大时间节点的实时延迟数值,按照延迟优先级顺序实行差值填补,完成数据传输延时补偿。结果表明,所提方法有良好的补偿精度和补偿效果,可降低航空电子通信的延迟时间,实现实时命令接收和消息传递功能,说明所提方法具有一定的实用价值。

人工神经网络在石油化工中的应用研究进展

石油化工行业体系庞大而复杂,其生产过程设备稳定性、安全性、节能环保等至关重要,而基于数据驱动的人工神经网络能很好地处理其生产过程中复杂的高维度、非线性数据,从而通过建模解决实际生产过程中难题。介绍了人工神经网络发展历程和基本原理,概述了人工神经网络在石油化工装置产品预测与操作优化、设备故障诊断、安全预警、节能减排等方面的应用,提出了持续优化、研发更有广度和深度的神经网络模型是未来石化工作的方向。

基于多源数据融合的配电网拓扑识别方法研究

针对目前配电网拓扑识别时多源融合数据利用率低的问题,提出了一种基于多源数据融合的配电网拓扑识别方法。首先,设计了一种多源数据时间尺度对齐方法。其次,提出了一种基于线性外推的伪测量数据生成方法,从而提高拓扑识别方法的数据精度。最后,建立了多源数据融合配电网离散化和线性化最优拓扑识别方法。在试验阶段,以IEEE 33馈线系统为例验证所提方法。试验结果表明,当节点注入功率数据的实时测量误差为30%、伪测量误差为5%时,所提方法拓扑识别准确率为70.98%,较无融合方法提升了5.86%。所提方法对提高配电网拓扑识别准确率具有一定指导作用。

基于数据融合分析的流感监测预警决策模型

我国现有的流感监测预警基于确诊病例数据,因患者从出现症状到确诊存在时间差,预警时效性需要提升,故研究基于症状数据和诊断数据融合的流感疫情预测问题。首先,用范围选词法按流感预防、症状、治疗分类选取16个网络搜索关键词。然后,收集2017年第46周至2019年第31周的搜索关键词百度指数数据,经互相关分析得到关键词的先行时序关系。再分别使用先行4周和先行2周数据,建立流感预测的多元线性回归、反向传播神经网络预测模型。使用统计分析软件SPSS和Matlab编程方式训练模型进行测试。结果显示,反向传播神经网络模型的预测效果较多元线性回归模型好,使用先行2周数据的预测结果较先行4周的数据准确。

整车天线无源性能测试结果计算模型研究

整车天线测试是为保障智能网联汽车通信性能而开展的基础性测试。在实际测试场景中,结果数据的处理和分析是一个重要环节。天线种类复杂、测试频点众多、计算需求多样等因素,增加了结果数据处理和分析的难度及复杂度。本文以目前最常见的球面近场测试为研究背景,以线性平均增益计算为研究目标,提取了整车天线测试场景下的关键要素,归纳了数据处理的关键环节,设计出线性平均增益计算模型的整体框架。针对不同测试场景,对结果计算模型的数据处理逻辑进行了详细的设计。该模型通过模块化、流程化的设计,在满足不同计算需求的同时能够大大提高测试结果数据处理的效率。

基于神经网络的无线传感器网络异常数据检测方法

传感器网络的异常数据检测对于环境监测具有十分重要的意义。基于BP神经网络模型和线性神经网络模型,分别提出了两种无线传感器网络异常数据检测方法。提出的方法在每个当前时刻通过最近的固定长度的历史数据集训练神经网络,来完成下一时刻的预报。通过神经网络的模型残差,确定概率为P的置信区间。当下一时刻数据落入置信区间内,则该数据被判为正常;反之,则为异常。为了比较和验证两种检测方法的性能,在Matlab环境下完成了仿真实验。实验结果表明,基于线性神经网络的异常数据检测方法的检测率(detection rate)达到了97.9%,误报率(false positive rate)不超过0.76%;基于BP神经网络的异常数据检测方法的检测率为96.7%,误报率不超过0.84%。

基于RBF神经网络的桥梁挠度数据恢复研究

本文以桥梁挠度数据作为研究对象,对其测量点进行了关联分析,并依据RBF神经网络强大的函数逼近能力,提出了一种基于神经网络模型来恢复不可靠测量数据的方法。在仿真实验中,通过对比实验(该方法的均方误差为2E-9,线性回归法均方误差为0.697 4),证实了该方法在理论和实践上的精确性和可行性。

面向OpenFlow网络的访问控制规则自动实施方案

针对OpenFlow网络数据平面频繁改变导致网络难以实时满足访问控制策略要求的问题,提出了面向OpenFlow网络的访问控制规则自动实施方案。首先,由实时构建的转发路径获得可达空间,并通过规则集动态合成算法消除访问控制规则间的冲突;之后,采用规则空间分割算法将合成后访问控制规则的拒绝空间与可达空间比较,以检测直接和间接违反访问控制规则的非法转发路径;在此基础上,结合网络更新事件与违反检测结果灵活采取自动的违反解决方法,包括规则更新拒绝、规则序列移除、基于线性规划(LP)的近源端规则部署和末端规则部署4种;最后转换访问控制规则形式。理论分析和仿真结果表明,方案可用于控制器上运行多个安全应用程序和交换机内存受限的情况,并且基于LP的近源端规则部署方法可以降低网络中的不期望流量。

传感器网络异常时间同步数据过滤算法

利用节点时间偏移的分布特性和时间同步数据的线性特征,使用概率统计中的样本空间估计理论和方法,设计时间同步数据过滤预测区间,提出基于线性回归的无线传感器网络数据过滤算法.该算法利用预测空间过滤异常时间同步数据,解决了因传感器网络数据错误率高且容易受到攻击而影响网络时间同步的难题.算法判定过程简单,避免针对异常数据而运行复杂的线性回归算法,有效降低节点的计算开销.仿真结果表明,该算法有效消除了异常数据对线性回归估计值的影响,提高了基于线性回归的传感器网络时间同步精度.

基于时间相关性的无线传感器网络数据压缩与优化算法

针对无线传感器网络(WSN)数据采集存在数据冗余度大、累积误差大和数据精度低等问题,根据采集数据之间的时间相关性,提出一种无线传感器网络数据压缩与优化算法。该算法通过分析时间序列中采集数据的线性关系,建立分段一元线性回归模型;根据采集数据与回归模型预测值之间的误差,自适应地调整下一个采集时间,并动态地优化回归模型。仿真结果表明该算法在不同的数据变化情况下,均能降低数据冗余度和网络通信量,提高采集数据的重构精度。最后在真实的无线传感器网络应用环境中验证了算法的可行性。

存在时延和数据包丢失情况下状态反馈网络控制系统的指数稳定性

同时考虑网络诱导时延和数据包丢失以及传感器与控制器、控制器与执行器之间均存在网络的情况,研究了状态反馈网络控制系统的稳定性问题.基于一定的数据包丢失率和恒定时延,系统被建模为由结构事件率约束的异步动态切换系统.利用李亚普诺夫方法和线性矩阵不等式描述,推导出由数据包丢失率约束的系统指数稳定的充分条件,给出了系统指数稳定的容许数据丢包率和系统开环状态及闭环结构的关系,以使系统指数稳定.用M atlab LM I工具箱容易判定系统的指数稳定性,同时获得系统指数稳定的状态反馈控制律.M atlab仿真说明,分析方法是有效的,稳定判据是可行的.*

网络控制系统的最新研究综述

随着传感、信号处理和通信技术的快速发展,关于网络控制系统(Networked control systems,NCSs)的研究引起了极大的关注.本文拟回顾关于网络控制系统的最新研究进展.为揭示反馈通信网络对控制系统的影响,主要讨论了为满足不同控制目的所需的各种网络条件,例如:在时变信道的环境下,保证线性系统可镇定性所需的最低编码率;在间断观测的环境下,保证卡尔曼滤波器稳定性的临界丢包条件;在时不变连接图的环境下,达到线性多自主体系统趋同性所需的网络拓扑结构;在通信资源有限的情况下,基于事件驱动的采样与控制方法等.

基于线性异构无线传感器网络的协同可靠数据传输

大量无线传感器应用具有线性网络拓扑结构.结合高压输电线路监控系统的层次型异构无线传感器网络架构,提出了骨干网和子网协作进行可靠数据传输,以解决线性拓扑结构的单点失效问题.设计了骨干网节点自适应功率调整机制和传感器节点自动子网切换机制,并在含9个骨干网节点的实验床上实现和性能验证.实验结果表明所提可靠数据传输机制能解决单点失效问题,并能有效改善网络性能.