基于不变网络模型和故障注入的分布式信息系统故障溯源方法

针对传统分布式信息系统故障溯源算法对于先验知识依赖严重的问题,提出一种基于不变网络与故障注入相结合的故障溯源方法.首先,利用系统日志中收集到的系统组件运行数据,构建系统的不变网络模型,在此基础上进行节点或组件故障注入及扩散建模,建立故障网络集;然后,根据原始时间序列取值情况,制定数据质量评价规则以甄别数据是否发生突变;最后,利用实际故障网络与故障网络集中故障网络局部拟合的方式进行故障溯源,并利用数据质量评价规则对该结果进行修正,实现对系统故障源的精确定位.在仿真数据集、某开源系统数据集和某电网调度系统实采数据上的实验结果表明,所提出方法具有更高的准确率.

大型分布式信息系统故障检测研究

通过引入流强度并搜索全部流强度量测之间的不变关系,将大型分布式信息系统建模成一个不变网络,其中节点代表流强度量测,边代表不变关系.当系统发生故障时,会导致不变网络中与故障点相关的边发生中断,且由于故障会在监测数据中进行传播,从而导致多条边发生中断以及多个节点出现异常,加大了系统故障检测的难度.为此,设计了gRank+算法,根据节点测量的异常水平对不变网络中的节点进行排序,从而实现系统故障的快速检测.最后使用3组综合数据集通过准确率、召回率和增益值3个指标来验证gRank+算法的有效性和优越性.

Alpha-reliable combined mean traffic equilibrium model with stochastic travel times

Based on the reliability budget and percentile travel time(PTT) concept, a new travel time index named combined mean travel time(CMTT) under stochastic traffic network was proposed. CMTT here was defined as the convex combination of the conditional expectations of PTT-below and PTT-excess travel times. The former was designed as a risk-optimistic travel time index, and the latter was a risk-pessimistic one. Hence, CMTT was able to describe various routing risk-attitudes. The central idea of CMTT was comprehensively illustrated and the difference among the existing travel time indices was analyzed. The Wardropian combined mean traffic equilibrium(CMTE) model was formulated as a variational inequality and solved via an alternating direction algorithm nesting extra-gradient projection process. Some mathematical properties of CMTT and CMTE model were rigorously proved. Finally, a numerical example was performed to characterize the CMTE network. It is founded that that risk-pessimism is of more benefit to a modest(or low) congestion and risk network, however, it changes to be risk-optimism for a high congestion and risk network.

Discrete logistics network design model under interval hierarchical OD demand based on interval genetic algorithm

Aimed at the uncertain characteristics of discrete logistics network design,an interval hierarchical triangular uncertain OD demand model based on interval demand and network flow is presented.Under consideration of the system profit,the uncertain demand of logistics network is measured by interval variables and interval parameters,and an interval planning model of discrete logistics network is established.The risk coefficient and maximum constrained deviation are defined to realize the certain transformation of the model.By integrating interval algorithm and genetic algorithm,an interval hierarchical optimal genetic algorithm is proposed to solve the model.It is shown by a tested example that in the same scenario condition an interval solution[3275.3,3 603.7]can be obtained by the model and algorithm which is obviously better than the single precise optimal solution by stochastic or fuzzy algorithm,so it can be reflected that the model and algorithm have more stronger operability and the solution result has superiority to scenario decision.

Automated visual inspection of surface defects based on compound moment invariants and support vector machine

The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.

Chaotic invariant sets of a delayed discrete neural network of two non-identical neurons

In this paper, we show that a delayed discrete Hopfield neural network of two nonidentical neurons with no self-connections can demonstrate chaotic behavior in a region away from the origin. To this end, we first transform the model, by a novel way, into an equivalent system which enjoys some nice properties. Then, we identify a chaotic invariant set for this system and show that the system within this set is topologically conjugate to the full shift map on two symbols. This confirms chaos in the sense of Devaney. Our main result is complementary to the results in Kaslik and Balint (2008) and Huang and Zou (2005), where it was shown that chaos may occur in neighborhoods of the origin for the same system. We also present some numeric simulations to demonstrate our theoretical results.

The variational inequality formulation for unconfined seepage through three-dimensional dense fracture networks

The space block search technology is used to determine a connected three-dimensional fracture network in polygonal shapes,i.e.,seepage paths.After triangulation on these polygons,a finite element mesh for 3D fracture network seepage is obtained.Through introduction of the generalized Darcy's law,conservative equations for both fracture surface and fracture interactions are established.Combined with the boundary condition of Signorini's type,a partial differential equation(PDE) formulation is presented for the whole domain concerned.To solve this problem efficiently,an equivalent variational inequality(VI) formulation is given.With the penalized Heaviside function,a finite element procedure for unconfined seepage problem in 3D fracture network is developed.Through an example in a homogeneous rectangular dam,validity of the algorithm is verified.The analysis of an unconfined seepage problem in a complex fracture network shows that the proposed algorithm is very applicable to complex three-dimensional problems,and is effective in describing some interesting phenomenon usually encountered in practice,such as "preferential flow".

A new variational inequality formulation for unconfined seepage flow through fracture networks

Darcy's law only applying to the flow domain is extended to the entire fracture network domain including the dry domain.The partial differential equation(PDE) formulation for unconfined seepage flow problems for discrete fracture network is established,in which a boundary condition of Signorini's type is prescribed over the potential seepage surfaces.In order to reduce the difficulty in selecting trial functions,a new variational inequality formulation is presented and mathematically proved to be equivalent to the PDE formulation.The numerical procedure based on the VI formulation is proposed and the corresponding algorithm has been developed.Since a continuous penalized Heaviside function is introduced to replace a jump function in finite element analysis,oscillation of numerical integration for facture elements cut by the free surface is eliminated and stability of numerical solution is assured.The numerical results from two typical examples demonstrate,on the one hand the effectiveness and robustness of the proposed method,and on the other hand the capability of predicting main seepage pathways in fractured rocks and flow rates out of the drainage system,which is very important for performance assessments and design optimization of complex drainage system.

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

This paper is concerned with fractional-order bidirectional associative memory(BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag–Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.

Global exponential stability of cellular neural networks with multi-proportional delays

In this paper, a class of cellular neural networks （CNNs） with multi-proportional delays is studied. The nonlinear transformation yi（t） = xi（et） transforms a class of CNNs with multi-proportional delays into a class of CNNs with multi-constant delays and time- varying coefficients. By applying Brouwer fixed point theorem and constructing the delay differential inequality, several delay-independent and delay-dependent sufficient conditions are derived for ensuring the existence, uniqueness and global exponential stability of equilibrium of the system and the exponentially convergent rate is estimated. And several examples and their simulations are given to illustrate the effectiveness of obtained results.