Robust event correlation scheme for fault identification in communication networks

The complexity of communication networks and the amount of information transferred in these networks have made the management of such networks increasingly difficult. Since faults are inevitable, quick detection, identification, and recovery are crucial to make the systems more robust and their operation more reliable. This paper proposes a novel event correlation scheme for fault identification in communication networks. This scheme is based on the algebraic operations of sets. The causality graph model is used to describe the cause‐and‐effect relationships between network events. For each disorder, and each manifestation, a unique prime number is assigned. The use of the greatest common devisor (GCD) makes the correlation process simple and fast. A simulation model is developed to verify the effectiveness and efficiency of the proposed scheme. From simulation results, we notice that this scheme not only identifies multiple disorders at one time but also is insensitive to noise. The time complexity of the correlation process is close to a function of n, where n is the number of observed manifestations, with order O(n²); therefore, the on‐line fault identification is easy to achieve. Copyright © 1999 John Wiley & Sons, Ltd.     

因素空间中属性约简的区分函数

粗糙集用属性所构建的信息系统来描写事物,用各种细化的熵指标来实现信息的标度,为挖掘知识的关系数据库提供了数学基础,当前人们最关注的是她在属性约简中所能发挥的作用。但是它用以约简的区分函数定义不清楚,当没有属性能区分两个对象时,相应的属性变量为什么不取0而是取1?这一问题成为粗糙集应用的一个瓶颈。本文的目的是要为区分函数寻找更合理的解释和运用。所采用的方法是,首先要对属性名之间的运算要下定义,属性名与属性值不同,如果用属性值的运算来代替属性名的运算,就会在理解上出现混乱。为此,我们用因素空间的理论,将属性名视为因素,用因素之间的运算来定义属性名的运算,使区分函数有了明确的定义,同时也清楚解释了属性变量在特殊情况下为何取1的问题。这一结果说明因素空间可以加深粗糙集的理论基础,提高其解决问题的能力。     

一种高维数据的因果推断算法

发现数据间的因果关系是科学领域的一个重要问题,然而在高维数据中的因果推断暂时还没有有效的算法. 这里提出了一种基于条件独立性测试和互信息的适应于高维数据的因果推断算法. 该算法先用条件独立性测试和互信息降低数据集的维度,然后利用一种基于非线性加噪声模型的算法对节点间的方向进行判别. 数据试验表明,该算法在高维数据的情况下要优于目前其他的算法.     

我国铜工业与经济发展相关性研究

立足我国铜工业现状,根据计量经济学理论与方法,结合1990～2009年铜消费及GDP数据,利用Eviews5.1软件作实证分析,结果表明铜消费与GDP间存在协整关系以及双向因果关系。须努力提高国内及海外铜资源开采力度、增加再生铜产量来提升我国铜供应能力,同时调低铜冶炼及加工业产能的扩张速度,以确保国内铜工业产业链协调发展及促进国民经济持续快速发展。     

Causalities between CO2, electricity,and other energy variables during phase I and phase II of the EU ETS

The topic of this article is the analysis of the interplay between daily carbon, electricity and gas price data with the European Union Emission Trading System (EU ETS) for CO₂ emissions. In a first step we have performed Granger causality tests for Phase I of the EU ETS (January 2005 until December 2007) and the first year of Phase II of the EU ETS (2008). The analysis includes both spot and forward markets—given the close interactions between the two sets of markets. The results show that during Phase I coal and gas prices, through the clean dark and spark spread, impacted CO₂ futures prices, which in return Granger caused electricity prices. During the first year of the Phase II, the short-run rent capture theory (in which electricity prices Granger cause CO₂ prices) prevailed. On the basis of the qualitative results of the Granger causality tests we obtained the formulation testable equations for quantitative analysis. Standard OLS regressions yielded statistically robust and theoretically coherent results.     

城市会展与经济增长关系实证研究

为了更好地揭示城市会展与经济增长间的关系,弥补投入产出分析和可计算一般均衡模型的缺陷,以广州市为例,利用协整分析和Granger 因果关系检验进行实证研究,建立了城市会展与三次产业结构之间的向量误差纠正模型。检验结果表明:城市会展与城市经济之间存在互动关系;城市会展经济系统具有涌现性特征;会展需求、成交金额与工业产出、服务业产出之间协整关系中短期有效,但从长期来看,城市建设管理者与会展经济参与主体需谨慎对待互动关系所带来的不确定风险。     

城市住宅价格与宏观经济变量的关系分析 —— 基于VAR 模型的实证研究

通过分析选取住宅价格指数（HPI）、国内生产总值增长率（GDP）、1-3 年期银行贷款利率（IR）作为内生变量,滞后两期的广义货币供应量（M2）作为外生变量,建立了向量自回归模型。并在此基础上,运用协整分析、脉冲响应函数、方差分解和格兰杰因果检验等研究方法对住宅价格指数与宏观经济变量之间的动态相关关系进行了研究。研究结果表明,我国的住宅价格与所选用的宏观经济变量之间存在一定交互响应作用,并就此提出了相关建议。     

资源型城市旅游业与经济增长互动关系实证研究——以焦作市为例

以焦作市为例,利用2000-2015年焦作市旅游业发展和经济增长的相关时间序列数据,通过协整检验、Granger因果关系检验和误差修正模型来研究资源型城市旅游业发展和经济增长之间的互动关系。结果显示,资源型城市旅游业的发展和经济增长存在很强的正相关关系,具有长期稳定的均衡关系,并且二者具有双向的Granger因果关系：旅游业的发展有助于促进资源型城市地区经济的增长,经济的增长反过来又会带动旅游业的发展。在此结论的基础上提出资源型城市发展旅游业的相关政策建议。      

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Fault diagnostics is important for safe operation of nuclear power plants (NPPs).In recent years,data-driven approaches have been proposed and implemented to tackle the problem,e.g.,neural networks,fuzzy and neurofuzzy approaches,support vector machine,K-nearest neighbor classifiers and inference methodologies.Among these methods,dynamic uncertain causality graph (DUCG)has been proved effective in many practical cases.However,the causal graph construction behind the DUCG is complicate and,in many cases,results redundant on the symptoms needed to correctly classify the fault.In this paper,we propose a method to simplify causal graph construction in an automatic way.The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree (FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT.Genetic algorithm (GA) is,then,used for the optimization of the FDT,by performing a wrapper search around the FDT:the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system.The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation.The results show that the FDT,with GA-optimized symptoms and diagnosis strategy,can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.     

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Fault diagnostics is important for safe operation of nuclear power plants (NPPs).In recent years,data-driven approaches have been proposed and implemented to tackle the problem,e.g.,neural networks,fuzzy and neurofuzzy approaches,support vector machine,K-nearest neighbor classifiers and inference methodologies.Among these methods,dynamic uncertain causality graph (DUCG) has been proved effective in many practical cases.However,the causal graph construction behind the DUCG is complicate and,in many cases,results redundant on the symptoms needed to correctly classify the fault.In this paper,we propose a method to simplify causal graph construction in an automatic way.The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree (FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT.Genetic algorithm (GA) is,then,used for the optimization of the FDT,by performing a wrapper search around the FDT:the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system.The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation.The results show that the FDT,with GA-optimized symptoms and diagnosis strategy,can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.