Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays

In this paper, we discuss average consensus problem in undirected networks of dynamic agents with fixed and switching topologies as well as multiple time-varying communication delays. By employing a linear matrix inequality method, we prove that all the nodes in the network achieve average consensus asymptotically for appropriate communication delays if the network topology is connected. Particularly, several feasible linear matrix inequalities are established to determine the maximal allowable upper bound of time-varying communication delays. Numerical examples are given to demonstrate the effectiveness and the sharpness of the theoretical results.     

Non-linear protocols for optimal distributed consensus in networks of dynamic agents

We consider stationary consensus protocols for networks of dynamic agents with fixed topologies. At each time instant, each agent knows only its and its neighbors’ state, but must reach consensus on a group decision value that is function of all the agents’ initial state. We show that the agents can reach consensus if the value of such a function is time-invariant when computed over the agents’ state trajectories. We use this basic result to introduce a non-linear protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents’ initial states. As a second contribution we show that our protocol design is the solution of individual optimizations performed by the agents. This notion suggests a game theoretic interpretation of consensus problems as mechanism design problems. Under this perspective a supervisor entails the agents to reach a consensus by imposing individual objectives. We prove that such objectives can be chosen so that rational agents have a unique optimal protocol, and asymptotically reach consensus on a desired group decision value. We use a Lyapunov approach to prove that the asymptotical consensus can be reached when the communication links between nearby agents define a time-invariant undirected network. Finally we perform a simulation study concerning the vertical alignment maneuver of a team of unmanned air vehicles.     

容错云计算网络中自适应故障检测方案研究

当前大多数故障检测(FD)技术无法针对动态网络条件自动调整它们的检测服务参数,导致无法应用于实际环境中。针对容错云计算网络中的故障检测特性进行研究,提出了一种通用的容错云计算网络自适应故障检测(SFD)算法;基于上述通用算法,对当前故障检测器进行优化,提出了一种自适应累积型故障检测器,并对SFD算法的部署进行了研究。基于七种典型的广域网案例,比较评估了该故障检测算法与当前其他算法的性能。实验结果表明,该算法可以实现故障的自适应检测,满足用户需求,拥有较高的系统性能。     

Hybrid diagnosis of regular networks under the HPMC fault model

The Journal of Supercomputing - Faulty diagnosis of multiprocessor systems is an important area of study in the design and maintenance of multiprocessor systems. However, most researchers on...     

Early detection method for emerging topics based on dynamic bayesian networks in micro-blogging networks

Micro-blogging networks have become the most influential online social networks in recent years, more and more people are used to obtain and diffuse information in them. Detecting topics from a great number of tweets in micro-blogging is important for information propagation and business marketing, especially detecting emerging topics in the early period could strongly support these real-time intelligent systems, such as real-time recommendation, ad-targeting, marketing strategy. However, most of previous researches are useful to detect emerging topic on a large scale, but they are not so effective for the early detection due to less informative properties in a relatively small size. To solve this problem, we propose a new early detection method for emerging topics based on Dynamic Bayesian Networks in micro-blogging networks. We first analyze the topic diffusion process and find two main characteristics of emerging topic which are attractiveness and key-node. Then based on this finding, we select features from the topology properties of topic diffusion, and build a DBN-based model by the conditional dependencies between features to identify the emerging keywords. An emerging keyword not only occurs in a given time period with frequency properties, but also diffuses with specific topology properties. Finally, we cluster the emerging keywords into emerging topics by the co-occurrence relations between keywords. Based on the real data of Sina micro-blogging, the experimental results demonstrate that our method is effective and capable of detecting the emerging topics one to two hours earlier than the other methods.     

Online fault detection and isolation of nonlinear systems based on neurofuzzy networks

An online fault detection and isolation (FDI) technique for nonlinear systems based on neurofuzzy networks (NFN) is proposed in this paper. Two NFNs are used. The first one trained by data obtained under normal operating condition models the system and the second one trained online models the residuals. Fuzzy rules that are activated under fault free and faulty conditions are extracted from the second NFN and stored in the symptom vectors using a binary code. A fault database is then formed from these symptom vectors. When applying the proposed FDI technique, the NFN that models the residuals is updated recursively online, from which the symptom vector is obtained. By comparing this symptom vector with those in the fault database, faults are isolated. Further, the fuzzy rules obtained from the symptom vector can also provide linguistic information to experienced operators for identifying the faults. The implementation and performance of the proposed FDI technique is illustrated by simulation examples involving a two-tank water level control system under faulty conditions.     

Impact of a transmission facility link failure on dynamic call routing circuit-switched networks under various circuit layout policies

A major transmission facility failure such as a fiber cut can significantly deteriorate the performance of a network by affecting multiple trunk groups in the network. To improve network performance various restoration options (pre-planned or real-time) in the traffic and/or the transmission facility network can be addressed. In this paper, we address the implication of various pre-planned circuit layout policies (based on trunk group diversity) on dynamic routing circuit-switched networks in the event of a failure. Using a network based on realistic data, we give results on comparative network performance under different circuit layout policies and on the effect of a failure depending on the time of the day it occurs. We observe that in the event of a failure, a network with trunk group diversity provides load balancing in terms of pairwise blocking and avoids isolation of traffic pairs compared to a network without trunk group diversity. However, in some instances, the overall network blocking may not be lower in a network with trunk group diversity compared to a network without trunk group diversity.     

雷达故障测试路径动态优化方法研究与实现

为了优化自动测试流程以及将故障诊断算法动态插入到雷达故障诊断循环中,提出了一种新的测试软件框架.该软件框架将雷达故障状态测试流程与故障诊断算法流程分离,使得测试流程与算法流程可以成为计算机中不同的线程或进程.基于该软件框架,定义了一种测试流程优化目标函数.利用此函数,讨论了一种基于预定义测试流程的动态优化方法.实现了在测试人员自主修改测试路径后,默认测试流程自动优化.     

Forming consensus in the networks of knowledge

Information granules, such as e.g., fuzzy sets, capture essential knowledge about data and the key dependencies between them. Quite commonly, we may envision that information granules (fuzzy sets) have become a result of fuzzy clustering and therefore could be succinctly represented in the form of some fuzzy partition matrices. Interestingly, the same data set could be represented from various standpoints and this multifaceted view yields a collection of different partition matrices being reflective of the higher-order granular knowledge about the data. The levels of specificity of the clusters the data are organized into could be quite different—the larger the number of clusters, the more detailed insight into the structure of data becomes available. Given the granularity of the resulting constructs (rather than plain data themselves), one could view a collection of partition matrices as a certain type of a network of knowledge. Considering a variety of sources of knowledge encountered across the network, we are interested in forming consensus between them. In a nutshell, this leads to the construction of certain fuzzy partition matrices which “reconcile” the knowledge captured by the individual partition matrices. Given that the granularity of the sources of knowledge under consideration could vary quite substantially, we develop a unified optimization perspective by introducing fuzzy proximity matrices that are induced by the corresponding partition matrices. In the sequel, the optimization is realized on a basis of these proximity matrices. We offer a detailed algorithm and illustrate its performance using a series of numeric experiments.     

On the number of nodes in universal networks of evolutionary processors

We consider the networks of evolutionary processors (NEP) introduced by J. Castellanos, C. Martí n-Vide, V. Mitrana and J. Sempere recently. We show that every recursively enumerable (RE) language can be generated by an NEP with three nodes modulo a terminal alphabet and moreover, NEPs with four nodes can generate any RE language. Thus, we improve existing universality result from five nodes down to four nodes. For mNEPs (a variant of NEPs where operations of different kinds are allowed in the same node) we obtain optimal results: each RE language can be generated by an mNEP with one node modulo a terminal alphabet, and mNEPs with two nodes can generate any RE language; this is not possible for mNEPs with one node. Some open problems are formulated.     

Set-membership fault detection under noisy environment with application to the detection of abnormal aircraft control surface positions

The paper develops a set membership detection methodology which is applied to the detection of abnormal positions of aircraft control surfaces. Robust and early detection of such abnormal positions is an important issue for early system reconfiguration and overall optimisation of aircraft design. In order to improve fault sensitivity while ensuring a high level of robustness, the method combines a data-driven characterisation of noise and a model-driven approach based on interval prediction. The efficiency of the proposed methodology is illustrated through simulation results obtained based on data recorded in several flight scenarios of a highly representative aircraft benchmark.     

Generalized likelihood ratio approach for fault detection in linear dynamic stochastic systems with unknown inputs

The generalized likelihood ratio (GLR) technique performs statistical tests on the innovations sequence of a Kalman filter state estimator. Using the results of these tests, sensor or actuator failures are detected and identified. In this paper, we generalize this strategy for linear dynamic stochastic systems with unknown inputs     

Pancyclicity of ternary n-cube networks under the conditional fault model

A graph G is said to be conditional k-edge-fault pancyclic if after removing k faulty edges from G, under the assumption that each vertex is incident to at least two fault-free edges, the resulting graph contains a cycle of every length from its girth to |V(G)|. In this paper, we consider ternary n-cube networks and show that they are conditional (4n−5)-edge-fault pancyclic.     

Reaching a consensus in networks of high-order integral agents under switching directed topologies

Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent’s full state is available or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent’s (estimated) state and the gain vector associated with the relative (estimated) states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. Also, the convergence of the transformed first-order integral agent’s state indicates the convergence of the original high-order integral agent’s state, if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative (estimated) states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be ‘uniformly jointly quasi-strongly connected’, which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.     

Observer-based consensus in networks of discrete-time switched linear dynamics under a fixed topology

This paper considers the consensus problem of discrete-time switched linear multi-agent systems under a fixed communication topology. An observer-based protocol with sampled data is proposed for solving such a problem. Assume that the digraph has a directed spanning tree and that all switched subsystem of each agent are stabilisable and detectable. It is shown that the proposed protocol solves the consensus problem when the sampling period is sufficiently small and the average dwell time of the switching signal is sufficiently large. Moreover, to reduce the conservatism, a sufficient condition for consensus is obtained to design the feedback gain matrices and the observer gain matrices by linear matrix inequalities, which guarantee that consensus can be achieved when the sampling period and the average dwell time are in the general case. Finally, the effectiveness of the theoretical results is demonstrated through an example.     

Optimal algorithms for the channel-assignment problem on a reconfigurable array of processors with wider bus networks

The computation model on which the algorithms are developed is the reconfigurable array of processors with wider bus networks (abbreviated to RAPWBN). The main difference between the RAPWBN model and other existing reconfigurable parallel processing systems is that the bus width of each network is bounded within the range [2,[/spl radic/(N)]]. Such a strategy not only saves the silicon area of the chip as well as increases the computational power enormously, but the strategy also allows the execution speed of the proposed algorithms to be tuned by the bus bandwidth. To demonstrate the computational power of the RAPWBN, the channel-assignment problem is derived in this paper. For the channel-assignment problem with N pairs of components, we first design an O(T + [N//spl omega/]) time parallel algorithm using 2N processors with a 2N-row by 2N-column bus network, where the bus width of each bus network is /spl omega/-bit for 2 /spl les/ /spl omega/ /spl les/ [/spl radic/N] and T = [log/sub /spl omega//N] + 1. By tuning the bus bandwidth to the natural log N-bit and the extended N/sup 1/c/-bit (N/sup 1/c/ > log N) for any constant c and c /spl ges/ 1, two more results which run in O(log N/log log N) and O(1) time, respectively, are also derived. When compared to the algorithms proposed by Olariu et al. [17] and Lin [14], it is shown that our algorithm runs in the equivalent time complexity while significantly reducing the number of processors to O(N).     

Nonuniform networks with restrictions on the residence time in maintenance mode

An open nonuniform network of queuing with the simplest incoming flow, exponential service at the nodes, and Markov routing was studied. In each of the nodes there is only one device, which can operate in several modes. For isolated nodes in a fictitious medium, conditions of reversibility, under which the stationary probability distribution of states of the network has a multiplicative form, were obtained. It is proved that customers flows coming out of the network are independent and Poisson.     

Average consensus problems in networks of agents with delayed communications

The present paper is devoted to the study of average consensus problems for undirected networks of dynamic agents having communication delays. By focusing on agents with integrator dynamics, the accent is put here on the study of the time-delay influence: both constant and time-varying delays are considered, as well as uniform and non-uniform repartitions of the delays in the network. The main results provide sufficient conditions (also necessary in most cases) for the existence of average consensus under bounded communication delays. Simulations are provided that show adequation with these results.