Data for critical infrastructure network modelling of natural hazard impacts:Needs and influence on model characteristics

Natural hazards impact interdependent infrastructure networks that keep modern society functional.While a va-riety of modelling approaches are available to represent critical infrastructure networks(CINs)on different scales and analyse the impacts of natural hazards,a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data.The resulting data gaps often require mod-ellers to assume specific technical parameters,functional relationships,and system behaviours.In other cases,expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps.The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture,thereby eroding the reliability of these models to guide resilience enhancements.Additionally,ways of overcoming the data avail-ability challenges in CIN modelling,with respect to each modelling purpose,remain an open question.To address these challenges,a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations,as well as the six CIN modelling stages,including mapping of infrastructure assets,quantification of dependencies,assessment of natural hazard impacts,response&recovery,quantification of CI services,and adaptation measures.The data requirements of each stage were systematically defined,and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls.The application of the derived workflow funnels into a framework to assess data availability challenges.This is shown through three case studies,taking into account their different modelling purposes:hazard hotspot assess-ments,hazard risk management,and sectoral adaptation.Based on the three model purpose types provided,a framework is suggested to explore the implications of data scarcity for certain data types,as well as their reasons and consequences for CIN model reliability.Finally,a discussion on overcoming the challenges of data scarcity is presented.

Deep Reinforcement Learning Object Tracking Based on Actor-Double Critic Network

Aiming at the problem of poor tracking robustness caused by severe occlusion,deformation,and object rotation of deep learning object tracking algorithm in complex scenes,an improved deep reinforcement learning object tracking algorithm based on actor-double critic network is proposed.In offline training phase,the actor network moves the rectangular box representing the object location according to the input sequence image to obtain the action value,that is,the horizontal,vertical,and scale transformation of the object.Then,the designed double critic network is used to evaluate the action value,and the output double Q value is averaged to guide the actor network to optimize the tracking strategy.The design of double critic network effectively improves the stability and convergence,especially in challenging scenes such as object occlusion,and the tracking performance is significantly improved.In online tracking phase,the well-trained actor network is used to infer the changing action of the bounding box,directly causing the tracker to move the box to the object position in the current frame.Several comparative tracking experiments were conducted on the OTB100 visual tracker benchmark and the experimental results show that more intensive reward settings significantly increase the actor network’s output probability of positive actions.This makes the tracking algorithm proposed in this paper outperforms the mainstream deep reinforcement learning tracking algorithms and deep learning tracking algorithms under the challenging attributes such as occlusion,deformation,and rotation.

Confidentiality-aware message scheduling for security-critical wireless networks

Without considering security, existing message scheduling mechanisms may expose critical messages to malicious threats like confidentiality attacks. Incorporating confidentiality improvement into message scheduling, this paper investigates the problem of scheduling aperiodc messages with time-critical and security-critical requirements. A risk-based security profit model is built to quantify the security quality of messages; and a dynamic programming based approximation algorithm is proposed to schedule aperiodic messages with guaranteed security performance. Experimental results illustrate the efficiency and effectiveness of the proposed algorithm.

End-to-end Delay Analysis for Mixed-criticality WirelessHART Networks

WirelessHART, as a robust and reliable wireless protocol, has been widely-used in industrial wireless sensoractuator networks. Its real-time performance has been extensively studied, but limited to the single criticality case. Many advanced applications have mixed-criticality communications, where different data flows come with different levels of importance or criticality. Hence, in this paper, we study the real-time mixedcriticality communication using WirelessHART protocol, and propose an end-to-end delay analysis approach based on fixed priority scheduling. To the best of our knowledge, this is the first work that introduces the concept of mixed-criticality into wireless sensor-actuator networks. Evaluation results show the effectiveness and efficacy of our approach. © 2014 Chinese Association of Automation.

Analysis of Computer Network Reliability and Criticality: Technique and Features

The paper describes modern technologies of Computer Network Reliability. Software tool is developed to estimate of the CCN critical failure probability (construction of a criticality matrix) by results of the FME(C)A-technique. The internal information factors, such as collisions and congestion of switchboards, routers and servers, influence on a network reliability and safety (besides of hardware and software reliability and external extreme factors). The means and features of Failures Modes and Effects (Critical) Analysis (FME(C)A) for reliability and criticality analysis of corporate computer networks (CCN) are considered. The examples of FME(C)A-Technique for structured cable system (SCS) is given. We also discuss measures that can be used for criticality analysis and possible means of criticality reduction. Finally, we describe a technique and basic principles of dependable development and deployment of computer networks that are based on results of FMECA analysis and procedures of optimization choice of means for fault-tolerance ensuring.

Method of Detection Abnormal Features in Ionosphere Critical Frequency Data on the Basis of Wavelet Transformation and Neural Networks Combination

The research is focused on the development of automatic detection method of abnormal features, that occur in recorded time series of ionosphere critical frequency fOF2 during periods of high solar or seismic activity. The method is based on joint application of wavelet-transformation and neural networks. On the basis of wavelet transformation algorithms for the detection of features and estimation of their parameters were developed. Detection and analysis of characteristic components of time series are performed on the basis of joint application of wavelet transformation and neural networks. Method's approbation is performed on fOF2 data obtained at the observatory “Paratunka” (Paratunka settlement, Kamchatskiy Kray).

基于CRITIC方法的OpenStreetMap路网匹配

OpenStreetMap(OSM)数据具有更新速度快、易获取等优点,已成为城市道路数据更新的重要数据源之一。采用OSM数据对城市道路数据更新,其中OSM与已有路网数据间的匹配是关键技术之一。现有路网匹配方法中,常采用几何相似因子及其权重来实现路网间的匹配,但这些方法大多使用固定的几何相似因子权重来处理整个数据集。相比于专业路网数据,OSM数据具有明显的非系统性形变特征,采用现有方法处理OSM与专业路网间的匹配时,匹配精度较低。为了提高OSM路网与专业路网的匹配精度,提出一种基于CRITIC方法的OSM路网匹配方法,该方法针对OSM路网数据的非系统性形变特征,选用CRITIC方法自动计算得到每个道路要素的距离、面积和方向这3个几何相似因子权重,减少人为定权的局限性。通过实验验证,文中匹配方法在OSM路网的匹配中取得了较好的效果,匹配精度较高。匹配的准确率、召回率、F1分数分别达到了96.73%、98.67%和97.68%。

图论在网络的可靠性分析中的应用—对基于1-critical-pathsubset网络的性能分析

本文对一种网络流模型的可靠性进行分析 .在这个模型中 ,我们考虑一对源节点和汇节点的图 ,它的弧是随机失效的 .当网络最大流大于正常工作流 ,我们就说系统是正常工作的 .考虑正常工作流的一种特殊情况 ,这里 ,所有的弧都具有相同的容量 .在这种特殊的情况中 ,潜在的系统是 1- critical的 ,也就是说 ,所有的弧的最小截大小为 2 .此时 ,问题转化为在有向图中 ,求所有的失效弧都在同一条路径上的概率 。

基于BP-ANN结合CRITIC法优化当归尾提取工艺参数

通过反向传播神经网络(BP-ANN)结合CRITIC法多指标优化当归尾的提取工艺。以提取次数、料液比、提取时间为考察因素,用CRITIC法计算阿魏酸、绿原酸、欧前胡素、藁本内酯、当归尾药材干膏率的多指标综合评分作为评价指标,先采用正交设计,再建立反向传播神经网络模型,通过网络训练,预测当归尾的最优提取工艺。优化得到的当归尾最优提取工艺为加9.6倍量水,提取时间67 min,提取3次,检测样本的网络预测值和实际测量值的相对误差小于1%。通过相关数学模型分析和预测所得的当归尾提取工艺稳定可行,可有效提高当归尾中有效成分的提取效率。

网络逻辑、集体智慧与社会博弈:论新媒体大众批评的性质

新媒体大众批评产生了一些不同于传统文艺批评的全新性质。首先,新媒体大众批评不是孤立的批评文本,而是通过众多参与者交互形成的关系网络,它使用“涌现”的策略产生优质批评。其次,它能够通过“涌现”和整体的方式呈现“集体智慧”,从而为人们提供文艺批评上的洞见。最后,它为社会群体的不同观点提供了社会博弈的批评场域,让批评文本具有亲民性。

基于CRITIC法赋权的Box-Behnken响应面法结合GA-BP神经网络优化白芍甘草脐贴处方

目的 优化白芍甘草脐贴(简称脐贴)处方。方法 采用体外经皮渗透试验并利用高效液相指纹图谱(high performance liquid chromatography,HPLC)法,以外观、8种指标成分累积释放度、总成分累积释放度为评价指标,采用单因素试验、Box-Behnken响应面法结合GA-BP神经网络优化脐贴处方中载药量、促渗剂用量、微粉硅胶占PEG6000和PEG4000总量的比例,确定最佳处方。结果 Box-Behnken响应面法和GA-BP神经网络优化的综合评价指标分别为1.2808(n=3,RSD=3.05%)、1.1937(n=3,RSD=2.26%),经对比及验证,脐贴最佳处方为微粉硅胶占PEG6000和PEG4000总量的比例为9.71%,载药量为24.56%,肉豆蔻异丙酯用量为2.87%。结论 利用Box-Behnken响应面法结合GA-BP神经网络择优确定了脐贴最佳处方,该处方制得的脐贴软硬适中,具有良好的皮肤渗透性。

Actor-critic框架下的二次指派问题求解方法

二次指派问题(QAP)属于NP-hard组合优化问题,在现实生活中有着广泛应用。目前相对成熟的启发式算法通常以问题为导向来设计定制化算法,缺乏迁移泛化能力。为提供一个统一的QAP求解策略,将QAP问题的流量矩阵及距离矩阵抽象成两个无向完全图并构造相应的关联图,从而将设施和地点的指派任务转化为关联图上的节点选择任务,基于actor-critic框架,提出一种全新的求解算法ACQAP。首先,利用多头注意力机制构造策略网络,处理来自图卷积神经网络的节点表征向量;然后,通过actor-critic算法预测每个节点被作为最优节点输出的概率;最后,依据该概率在可行时间内输出满足目标奖励函数的动作决策序列。该算法摆脱人工设计,且适用于不同规模的输入,更加灵活可靠。实验结果表明,在QAPLIB实例上,本算法在精度媲美传统启发式算法的前提下,迁移泛化能力更强;同时相对于NGM等基于学习的算法,求解的指派费用与最优解之间的偏差最小,且在大部分实例中,偏差均小于20%。

一种自适应模糊Actor-Critic学习

提出一种基于模糊RBF网络的自适应模糊A ctor-C ritic学习.采用一个模糊RBF神经网络同时逼近A ctor的动作函数和C ritic的值函数,解决状态空间泛化中易出现的“维数灾”问题.模糊RBF网络能够根据环境状态和被控对象特性的变化进行网络结构和参数的自适应学习,使得网络结构更加紧凑,整个模糊A ctor-C ritic学习具有泛化性能好、控制结构简单和学习效率高的特点.M oun ta in C ar的仿真结果验证了所提方法的有效性.

基于改进层次分析法、CRITIC法与逼近理想解排序法的输电网规划方案综合评价

针对进行输电网规划时难以量化各指标主观权重与客观权重的问题,提出了一种将改进层次分析法、基于指标相关性的指标权重确定(CRITIC)法和逼近理想解排序法(TOPSIS)相结合的输电网规划方案评价方法。该方法首先分别利用改进层次分析法与CRITIC法计算各指标的主观、客观权重,并将两权重结合得到综合权重;然后利用TOPSIS计算各规划方案与理想解的相对贴近度,以相对贴近度的大小为衡量标准,实现对规划方案的排序。这种综合考量主、客观权重的方法有效地利用了指标数据的客观信息,并充分考虑了实际电网规划中主观评判和决策的重要作用。以输电网规划常用的经典IEEE Garver-6节点系统为算例验证了所提评价方法的有效性。

基于TOPSIS和CRITIC法的电网关键节点识别

准确快速地识别出电网关键节点,对于预防电网发生大面积停电事故具有非常重要的意义。首先,基于区域电网的复杂网络拓扑特性和电气特性,提出了评估区域电网关键节点的指标,即电力网络节点的功率集中度、电气介数以及电力网络的传输效能、凝聚度、生成树变化率;然后,提出了基于TOPSIS法的节点重要度评估方法以及基于CRITIC法的指标客观综合权重确定方法。最后,以广东某区域电网为例验证所提出的节点重要度评估方法的有效性。算例分析结果表明,所提出的方法能够较好地识别出电网的关键节点,避免了人为确定权重的主观性,计及了指标在不同评价对象的取值差异性和评价指标之间的冲突性,因而评估结果更加符合电网运行实际情况。

基于改进的AHP-CRITIC-MARCOS配电网设备风险评估方法

实现配电网设备风险的准确评估对提高配电网的可靠性有着重要意义。针对在不同权重下同时考虑多种风险因素的评估问题,提出了一种基于改进的AHP-CRITIC-MARCOS配电网设备风险评估方法。首先,针对配电网设备风险问题选取合适的评估指标。其次,用改进的AHP方法结合CRITIC方法计算各指标的主客观综合权重。最后,利用多准则决策中的MARCOS方法计算待评估配电网设备的效用函数,并根据其对各设备的风险程度进行排序。通过算例验证了所提方法的有效性,结果可以用于设备升级改造的精准选择以及提升配电网的可靠性。

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.

基于改进CRITIC-Bayes网络的激光武器毁伤效果评估方法

针对目标毁伤信息的不确定性,提出了一种基于改进CRITIC-Bayes网络的激光武器毁伤效果评估方法。确定激光武器反导的毁伤因子,引入博弈论思想,结合专家经验信息确定的主观权重与CRITIC赋权法确定的客观权重,对毁伤因子进行赋权;以Bayes网络为基础,以反舰导弹的控制仓为毁伤部位,建立激光武器毁伤效果评估模型。在实例中通过不同的方法比较验证该模型的有效性,为解决不确定环境下作战决策提供理论依据。