基于贝叶斯模型的高效主动探测算法(英文)

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 base...     

基于参数-值替换的错误定位方法

现有错误定位方法难以检测程序遗漏错误,提出一种方法层次的基于参数?值替换的错误定位方法。首先将出现在失败执行中的方法作为可疑方法候选集,计算该集合中元素对程序执行结果的影响度,再利用值替换技术计算高影响度方法的兴趣参数?值映射对,然后依据方法是否包含兴趣参数?值映射对对候选集中方法进行分类,并依据其影响度分别进行排序,最后生成一个可疑方法序列进行错误定位。实验结果表明该方法较其他错误定位方法具有更好的定位效果。     

基于GMPLS光网络的分布式多层故障定位方法

在GMPLS光网络中,为了在故障定位时减少定位数障据链路故障的信令开销,避免不必要的网络资源浪费,降低网络资源的阻塞率,提出了一种分布式多层故障定位方法。该方法在现有的单层故障定位方案的基础上,通过双向数据链路故障通知的方法,避免了一些不必要的故障相关操作,减少了网络节点的负担,提高了网络资源的利用率。     

Constraint analysis for extended dynamic fault tree

As a new extension of dynamic fault trees,time delay gate was proposed.This new mechanism can be used to model the time delay on the fault propagation from the lower level subsystems to the higher level system.The dynamic Bayesian networks was extend to solve the dynamic fault trees containing time delay gates.An algorithm based on SMT to support the optimized failure distribution under the nondeterministic range constraint of basic events was also proposed.The effectiveness is shown by comparison with existing tools on analyzing and solving real systems,and the effects of range constraints and gate parameter on the failure distribution of systems is illustrated.     

贝叶斯网络推理算法在故障诊断中的应用

故障诊断过程中,存在这样或那样的不确定性因素。而贝叶斯网络是不确定知识表达和推理领域最有效的理论模型之一,所以以贝叶斯网络理论为基础进行故障诊断,可以有效地对不确定性知识进行表达,增加故障诊断结果的合理性与准确率。     

ASON网络中一种新型的域间节点故障定位机制

在研究链路管理协议(LMP)单域故障定位的基础上,提出了一种自动交换光网络(ASON)的域间节点故障定位的新型方法.基于网关节点跨域通信的域间节点故障定位机制,利用ASON分布式控制平面完成故障定位功能,可以不依赖于传送平面的编码方式和网管人员的参与,并在各种域间节点故障情形下分别阐述了定位机制的工作原理以及定位过程.     

某型电子信息系统模块级故障诊断技术研究

针对电子信息系统结构复杂、难于建立精确数学模型等特点,以某型电子信息系统为例,采用故障字典和BP神经网络相结合的方法,利用Multisim软件进行电路仿真。再由实测样本数据对BP神经网络进行训练,完成对网络各参数的设置。仿真结果表明,该方法可以较好地将故障定位到元器件,同时可为其他电子信息系统模块级故障诊断技术研究提供借鉴。     

The variant cycle-cover problem in fault detection and localization for mesh all-optical networks

With the soaring channel speed and density in all-optical networks (AONs), the risk of high data loss upon network faults increases quickly. To manage network faults efficiently, an m-cycle based fault detection and localization (MFDL) scheme has been introduced recently. This paper verifies the necessary and sufficient condition for achieving the complete fault localization (CFL) in MFDL, which is defined as the case that every single network fault can be located to a unique link. We model the m-cycle construction as a new mathematical problem: the variant version of the constrained cycle-cover problem (vCCCP) and explore its formal expression. The model includes the consideration of the cycle-length limit, cycle number, and wavelength cost, while also keeps the CFL achievable. A two-phase branch-and-bound (B&B) algorithm was developed for solving the vCCCP, which guarantees to find near-optimal solutions. This algorithm is then applied to four typical and four random network examples to validate and assess the performance. The results are analyzed and compared with some previously reported algorithms, in terms of fault localization degree, cycle number, wavelength overhead, and cost reduction. The performance evaluation and comparison reveal that the new model and algorithm could significantly reduce the MFDL cost, including both the cost of monitoring devices and reserved wavelengths.     

Efficient closed-form solution for target localization using TDOA measurements in the presence of sensor position errors

An efficient solution for locating a target was proposed, which by using time difference of arrival (TDOA) measurements in the presence of random sensor position errors to increase the accuracy of estimation. The cause of position estimation errors in two-stage weighted least squares (TSWLS) method is analyzed to develop a simple and effective method for improving the localization performance. Specifically, the reference sensor is selected again and the coordinate system is rotated according to preliminary estimated target position by using TSWLS method, and the final position estimation of the target is obtained by using weighted least squares (WLS). The proposed approach exhibits a closed-form and is as efficient as TSWLS method. Simulation results show that the proposed approach yields low estimation bias and improved robustness with increasing sensor position errors and thus can easily achieve the Cramer-Rao lower bound (CRLB) easily and effectively improve the localization accuracy.     

电子装备单点多征兆故障D-S理论诊断方法研究

为了提高新型电子装备单点多征兆故障定位的准确性,构建了一种基于D-S证据理论的故障诊断模型和信息融合算法,提出了把故障诊断过程分为3步、构成3个识别框架、3次使用D-S证据推理、选取不同的决策规则实现故障定位的方法。实例证明,该方法较好地解决了电子装备单点多征兆故障诊断中的不确定性问题。     

Cooperative localization techniques for wireless sensor networks: free,signal and angle based techniques

This paper addresses the problem of localization in sensor networks where, initially, a certain number of sensors are aware of their positions (either by using GPS or by being hand‐placed) and are referred to as anchors. Our goal is to localize all sensors with high accuracy, while using a limited number of anchors. Sensors can be equipped with different technologies for signal and angle measurements. These measures can be altered by some errors because of the network environment that induces position inaccuracies. In this paper, we propose a family (AT‐Family) of three new distributed localization techniques in wireless sensor networks: free‐measurement (AT‐Free) where sensors have no capability of measure, signal‐measurement (AT‐Dist) where sensors can calculate distances, and angle‐measurement (AT‐Angle) where sensors can calculate angles. These methods determine the position of each sensor while indicating the accuracy of its position. They have two important properties: first, a sensor node can deduce if its estimated position is close to its real position and contribute to the positioning of others nodes; second, a sensor can eliminate wrong information received about its position. This last property allows to manage measure errors that are the main drawback of measure‐based methods such as AT‐Dist and AT‐Angle techniques. By varying the density and the error rate, simulations show that the three proposed techniques achieve good performances in term of high accuracy of localized nodes and less energy consuming while assuming presence of measure errors and considering low number of anchors. Copyright © 2012 John Wiley & Sons, Ltd.     

Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme.     

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

This article contributes to science at two points. The first contribution is at a point of introducing a novel direction‐of‐arrival (DOA) estimation method which based on subspaces methods called Probabilistic Estimation of Several Signals (PRESS). The PRESS method provides higher resolution and DOA accuracy than current models. Second contribution of the article is at a point of localizing the unknown signal source. The process of localization achieved by using DOA information for the first time. The importance of localization exists in a large area of engineering applications. The aim is to determine the location of multiple sources by using PRESS with minimum effort of computation. We used the maximum probabilistic process in this study. Initially, all the signals are collected by the array of sensors and accurately identified using the proposed algorithm. The receiver at the best in test estimates the source location using only the knowledge of the geographical latitude and longitude values of the array of sensors. Several test points with an accurately calculated angle of arrival enable us to draw linear lines towards the transmitter. The transmitter location can be accurately identified with the line of interceptions. Simulation and numerical results show the outstanding performance of both the DOA estimation method and transmitter localization approach compared with many classical and new DOA estimation methods. The PRESS localization method first tested at 19°, 26°, and 35° with an signal‐to‐noise ratio (SNR) value of ‐5 dB. The PRESS method produced results with an extremely low bias of 0 and 0.00080°. The simulation tests are repeated and produced results with zero bias, which give the exact location of the unknown source.     

基于GA-DS的多神经网络故障诊断方法

针对基于信息熵的神经网络证据形成过程需要专家经验确定参数这一不足,提出一种改进方法,即用遗传算法(Genetic Algorithm,GA)进行寻优,自动确定参数,其实质是利用了遗传算法的高效的并行寻优能力和对初始种群的较低的敏感性,通过训练样本自适应调整证据形成过程中的参数,提升DS(Dempster Shafer)证据理论的融合效率.对多类轴承故障数据的诊断结果表明,该方法(GA-DS)能够自动调整神经网络的证据形成过程,从而有效地降低了融合过程中证据的冲突性,并且显著提高了诊断精度和稳定性.     

示功图特征提取选择在油井故障诊断中的研究应用

示功图特征值提取是在功图数据中挑选具有代表性的数据,经过计算处理得到最有效的特征值,作为故障诊断系统中神经网络的输入.本文用了几何参数法、灰度矩阵法来获取示功图的特征值,减小了神经网络的输入规模,使特征值更具有针对性,提高了神经网络的训练速度,从而使诊断的准确性更高.该方法已在江苏油田的实际应用中取得了良好的效果.     

Comparative node selection‐based localization technique for wireless sensor networks: A bilateration approach

Wireless sensor networks find extensive applications, such as environmental and smart city monitoring, structural health, and target location. To be useful, most sensor data must be localized. We propose a node localization technique based on bilateration comparison (BACL) for dense networks, which considers two reference nodes to determine the unknown position of a third node. The mirror positions resulted from bilateration are resolved by comparing their coordinates with the coordinates of the reference nodes. Additionally, we use network clustering to further refine the location of the nodes. We show that BACL has several advantages over Energy Aware Co‐operative Localization (EACL) and Underwater Recursive Position Estimation (URPE): (1) BACL uses bilateration (needs only two reference nodes) instead of trilateration (that needs three reference nodes), (2) BACL needs reference (anchor) nodes only on the field periphery, and (3) BACL needs substantially less communication and computation. Through simulation, we show that BACL localization accuracy, as root mean square error, improves by 53% that of URPE and by 40% that of EACL. We also explore the BACL localization error when the anchor nodes are placed on one or multiple sides of a rectangular field, as a trade‐off between localization accuracy and network deployment effort. Best accuracy is achieved using anchors on all field sides, but we show that localization refinement using node clustering and anchor nodes only on one side of the field has comparable localization accuracy with anchor nodes on two sides but without clustering.     

Non‐convex model for sensor network localization based on connectivity

In this paper, a new model utilizing all the information derived from connectivity‐based sensor network localization is introduced. The connectivity information between any pair of nodes is modeled as convex and non‐convex constraints. The localization problem is solved by searching for a solution that would satisfy all the constraints established in the problem. A two‐objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES) is used to solve the localization problem. The solution can reach the most suitable configuration of the unknown nodes because the information on both convex and non‐convex constraints related to connectivity has been utilized. From simulation results, a relationship between the communication range and accuracy is obtained. Furthermore, a two‐level range connectivity‐based sensor network localization method is proposed to enrich the connectivity information. The two‐level range/indication of connectivity between each pair of nodes would indicate three levels of connectivity: strong, weak, or nil. A comparison on accuracy between the one‐level and two‐level ranges of connectivity is carried out by simulation using six different topological networks all containing 100 nodes. Simulation results have shown that better solution can be obtained by using two‐level range connectivity compared with the usual one‐level range connectivity‐based localization. Copyright © 2012 John Wiley & Sons, Ltd.     

基于改进DS证据理论和BN的信息安全风险评估

针对信息安全风险评估过程中专家评价意见的多样性以及不确定信息难以量化处理的问题,提出了一种基于改进的DS证据理论与贝叶斯网络(BN)结合的风险评估方法.首先,在充分研究信息安全风险评估流程和要素的基础上,建立了风险评估模型,确定风险影响因素;其次,根据评估模型并结合专家知识构建相应的贝叶斯网络模型,确定贝叶斯网络模型中的条件概率表;再次,利用基于权值分配和矩阵分析的改进DS证据理论融合多位专家对风险影响因素的评价意见;最后,根据贝叶斯网络模型的推理算法,计算被测信息系统处于不同风险等级的概率值,并对结果进行有效性分析.分析表明,将改进后的DS证据理论与贝叶斯网络应用到风险评估过程中,在一定程度上能够提高评估结果的可信度和直观性.     

RIALS: RSU/INS‐aided localization system for GPS‐challenged road segments

This paper introduces a new vehicle localization approach for global positioning system‐challenged road segments (e.g., tunnels), which takes advantage of roadside units (RSUs) and in‐vehicle inertial navigation system (INS). In the proposed approach, namely RSU/INS‐aided localization system (RIALS), vehicles only need one RSU in their transmission range for an accurate positioning. The beacons received from the RSU along with the information provided by the INS system are used for establishing and maintaining particular locus circles. After linearization, the system of locus circle equations is solved using the linear least square estimation technique, and the estimated vehicle's position is obtained. In the presence of speed variations and existing ranging and INS estimation errors, the proposed RIALS adaptively sets the sufficient number of required locus circles, aiming at keeping the localization error below a given threshold. We study the effects of ranging and INS estimation errors on localization accuracy of RIALS from analytical and simulation perspectives. Results of extensive simulations show that the localization error is more sensitive to the ranging error than the INS error. Moreover, the network traffic overhead of the proposed method is considerably lower than other competitive localization approaches. Copyright © 2015 John Wiley & Sons, Ltd.     

基于格理论的装备保障信息网络快速身份认证方案

量子算法和量子计算机对装备保障信息网络的认证方案已构成严重的潜在威胁。针对当前装备保障信息网络身份认证方案无法抵抗量子计算机攻击、认证效率相对较低的问题,引入格理论的本原格抽样算法和双峰高斯抽样技术,提出了装备保障信息网络在量子环境下安全且快速的身份认证方案,给出了方案的正确性、安全性的理论证明以及方案运行效率的比较分析。结果表明,基于随机预言机证明模型,该方案在小整数解问题困难性假设下达到了适应性选择身份和选择消息攻击的存在性不可伪造性;在保证安全的前提下,新方案在私钥提取阶段和身份认证阶段的运行效率均高于已有的几个同类格基身份认证方案。这为提高我国装备保障信息网络安全认证能力提供了新的思路和方法。