Computational aspects of two-segment separable programming

Recursive separable programming algorithms based on local, two-segment approximations are described for the solution of separable convex programs. Details are also given for the computation of lower bounds on the optimal value by both a primal and a dual approach, and these approaches are compared. Computational comparisons of the methods are provided for a variety of test problems, including a water supply application (with more than 600 constraints and more than 900 variables) and an econometric modelling problem (with more than 200 variables). Research supported by National Science Foundation Grants MCS74-20584 A02 and MCS-7901066.     

Integral decomposition in polyhedra

We say that a polyhedronP satisfies weak integral decomposition if whenever an integral vector is the sum ofk vectors inP it is also the sum ofk integral vectors inP. This property is related to rounding results for packing and covering problems. We study the property and two related properties, and give results concerning integral polymatroids, totally unimodular matrices and network flows, pairs of strongly-base-orderable matroids, and branchings in directed graphs.     

Network formed by traces of random walks

We propose a model of time evolving networks in which a kind of transport between vertices generates new edges in the graph. We call the model “Network formed by traces of random walks”, because the transports are represented abstractly by random walks. Our numerical calculations yield several important properties observed commonly in complex networks, although the graph at initial time is only a one-dimensional lattice. For example, the distribution of vertex degree exhibits various behaviors such as exponential, power law like, and bi-modal distribution according to change of probability of extinction of edges. Another property such as strong clustering structure and small mean vertex–vertex distance can also be found. The transports represented by random walks in a framework of strong links between regular lattice is a new mechanisms which yields biased acquisition of links for vertices.     

Identifying the underlying structure and dynamic interactions in a voting network

We analyse the structure and behaviour of a specific voting network using a dynamic structure-based methodology which draws on Q-Analysis and social network theory. Our empirical focus is on the Eurovision Song Contest over a period of 20 years. For a multicultural contest of this kind, one of the key questions is how the quality of a song is judged and how voting groups emerge. We investigate structures that may identify the winner based purely on the topology of the network. This provides a basic framework to identify what the characteristics associated with becoming a winner are, and may help to establish a homogenous criterion for subjective measures such as quality. Further, we measure the importance of voting cliques, and present a dynamic model based on a changing multidimensional measure of connectivity in order to reveal the formation of emerging community structure within the contest. Finally, we study the dynamic behaviour exhibited by the network in order to understand the clustering of voting preferences and the relationship between local and global properties.     

Pragmatical generalized synchronization of chaotic systems with uncertain parameters by adaptive control

A new kind of generalized synchronization of two chaotic systems with uncertain parameters is proposed. Based on a pragmatical asymptotical stability theorem and an assumption of equal probability for ergodic initial conditions, an adaptive control law is derived so that it can be proved strictly that the common null solution of error dynamics and of parameter dynamics is actually asymptotically stable, i.e. these two identical systems are in generalized synchronization and the estimated parameters approach the uncertain values. It is called pragmatical generalized synchronization. Finally, two numerical examples are studied for two Quantum-CNN oscillator chaotic systems to show the effectiveness of the proposed generalized synchronization strategy with a double Duffing chaotic system as a goal system.     

Defect identification in rods subject to forced vibrations using the spatial wavelet transform

Use of the wavelet transform (WT) to study the forced vibrations in a rod in order to detect the presence of a defect is proposed. The axial vibrations in an inhomogeneous rod, produced by the application of different forces, are simulated by the Network Simulation Method and the obtained response is analyzed with the WT. An analysis of the detection and location of defects for several applied forces and defects generated by locally changing density or stiffness of the rod is presented. A noise test was carried out to check the robustness of the technique in real situations.     

开源教学平台的可用性研究——以Sakai为例

越来越多的国内外高校开始考虑使用基于开源软件的网络教学平台,在投入使用之前,开源教学平台一般需要进行二次开发过程,其中需要重点解决平台的可用性问题,因为网络教学平台的可用性是影响学习效果的重要因素.发现并解决网络教学环境中的可用性问题,是促进人机交互、提高学习效率的重要途径.开源教学平台的可用性指的是系统的有效性、效率和满意度,分析了网络教学平台的可用性诉求,并以浙江大学Sakai开放学习平台为例,进行个案研究.使用了ThinkAloud、观察法和访谈法等可用性测试方法,研究结果发现,浙江大学Sakai开放学习平台主要功能人机交互设计合理,但是由于文化差异和用户使用习惯问题,部分功能存在一定的可用性问题,值得学习平台设计者借鉴.     

Finding a feasible flow in a strongly connected network

We give a linear-time algorithm to find a feasible flow in a strongly connected network with fixed supplies and demands, each summing to a common value that is at most the minimum arc capacity. This algorithm speeds up the Goldberg-Rao maximum flow method by a constant factor.     

Combined neural network and reduced FRF techniques for slight damage detection using measured response data

Summary  This paper deals with structural damage detection using measured frequency response functions (FRF) as input data to artificial neural networks (ANN). A major obstacle, the impracticality of using full-size FRF data with ANNs, was circumvented by applying a data-reduction technique based on principal component analysis (PCA). The compressed FRFs, represented by their projection onto the most significant principal components, were used as the ANN input variables instead of the raw FRF data. The output is a prediction of the actual state of the specimen, i.e. healthy or damaged. A further advantage of this particular approach is its ability to deal with relatively high measurement noise, which is a common occurrence when dealing with industrial structures. The methodology was applied to detect three different states of a space antenna: reference, slight mass damage and slight stiffness damage. About 600 FRF measurements, each with 1024 spectral points, were included in the analysis. Six 2-hidden layer networks, each with an individually-optimised architecture for a specific FRF reduction level, were used for damage detection. The results showed that it was possible to distinguish between the three states of the antenna with good accuracy, subject to using an adequate number of principal components together with a suitable neural network configuration. It was also found that the quality of the raw FRF data remained a major consideration, though the method was able to filter out some of the measurement noise. The convergence and detection properties of the networks were improved significantly by removing those FRFs associated with measurement errors. Received 9 March 2000; accepted for publication 12 December 2000     

Opinion dynamics in networked command and control organizations

An opinion dynamics model for a Command and Control (C2) organization is essential for simulating combat system effectiveness. However, few studies have addressed opinion evolution in C2 simulation. With the goal of overcoming this research gap, this paper proposes an opinion exchange model, which is illustrated through a practical example of an Armored Division network. The model is divided into homogeneous and heterogeneous aspects: the former is mainly characterized by communication rules and types, while the latter is extended with the influence of multi-level opinion leaders. After carrying out the simulation of the two main models, the results show that the opinion evolution of the hierarchical leveled C2 organization with descending influence is much more complex and unpredictable than that of social networks.