A numerical homogenisation strategy for micromorphic continua

In this contribution, we apply a numerical homogenisation scheme for micromorphic continua replacing constitutive equations on the macroscale by a microscopic boundary value problem. The aim of this procedure is to describe the influence of the microtopology on the effective behaviour of microstructured materials such as biological tissues as well as polymer or metal foams. On the one hand, that allows for avoiding the numerically expensive calculation of a fully resolved microstructure. On the other hand there is no need to identify additional material parameters which are in general hard to interpret from the physical point of view. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A method for constructing ordered continua

We present a method for constructing ordered continua. We illustrate our method by constructing (i) a new order-homogeneous non-reversible continuum, and (ii) an ordered continuum with a minimal set of continuous self-maps.     

A novel particle swarm optimization algorithm based on particle migration

Inspired by the migratory behavior in the nature, a novel particle swarm optimization algorithm based on particle migration (MPSO) is proposed in this work. In this new algorithm, the population is randomly partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization with time varying inertia weight and acceleration coefficients (LPSO-TVAC). At periodic stage in the evolution, some particles migrate from one complex to another to enhance the diversity of the population and avoid premature convergence. It further improves the ability of exploration and exploitation. Simulations for benchmark test functions illustrate that the proposed algorithm possesses better ability to find the global optima than other variants and is an effective global optimization tool.     

A Markov-type inequality for arbitrary plane continua

Markov's inequality is

for all polynomials . We prove a precise version of this inequality with an arbitrary continuum in the complex plane instead of the interval .

     

A superpolynomial lower bound for strategy iteration based on snare memorization

This paper presents a superpolynomial lower bound for the recently proposed snare memorization non-oblivious strategy iteration algorithm due to Fearnley. Snare memorization is a method to train strategy iteration techniques to remember certain profitable substrategies and reapply them again. We show that there is not much hope to find a polynomial-time algorithm for solving parity games by applying such non-oblivious techniques.     

A stochastic algorithm for optimization problems with continua of inequalities

Optimization algorithms for solving mathematical programming problems involving continua of inequalities are presented. The algorithms use an outer-approximation method, by which they attempt to approximate, at each point, the maxima of sets of inequality constraints. They do so by performing random experiments, resulting in a finite number of points, over which the maximum is taken. They use constraint-dropping schemes, by which they eliminate points from the constraint set at hand, which are felt to be irrelevant. At each point that the algorithms construct, they evaluate a measure of optimality, which indicates the distance of the point from the set of solutions of the optimization problem. They use this measure to determine the number of random experiments performed. Thus, the number of such experiments tends to be small initially, when the points at hand are far from optimal, and they tend to increase when an optimal point is approached.     

一种基于双链量子编码的动态车辆路径问题解决策略

针对配送调度事件动态变化的动态车辆路径问题(DVRP), 以最小化运输成本、最小化配送时间 与最大化载货率为目标, 建立了问题的数学模型,提出了改进的多相量子粒子群算法. 针对DVRP问题的特点,提出基于车辆链和货物链的双链量子编码方法; 同时设计了基于周期和 重调度因子驱动的动态调度策略. 最后将方法应用于动态仿真算例, 并与其他经典算法比较, 结果验证了所提出方法的有效性.     

A group based solution strategy for multi-physics simulations in parallel

Multi-physics simulation often requires the solution of a suite of interacting physical phenomena, the nature of which may vary both spatially and in time. For example, in a casting simulation there is thermo-mechanical behaviour in the structural mould, whilst in the cast, as the metal cools and solidifies, the buoyancy induced flow ceases and stresses begin to develop. When using a single code to simulate such problems it is conventional to solve each ‘physics’ component over the whole single mesh, using definitions of material properties or source terms to ensure that a solved variable remains zero in the region in which the associated physical phenomenon is not active. Although this method is secure, in that it enables any and all the ‘active’ physics to be captured across the whole domain, it is computationally inefficient in both scalar and parallel. An alternative, known as the ‘group’ solver approach, involves more formal domain decomposition whereby specific combinations of physics are solved for on prescribed sub-domains. The ‘group’ solution method has been implemented in a three-dimensional finite volume, unstructured mesh multi-physics code, which is parallelised, employing a multi-phase mesh partitioning capability which attempts to optimise the load balance across the target parallel HPC system. The potential benefits of the ‘group’ solution strategy are evaluated on a class of multi-physics problems involving thermo-fluid–structural interaction on both a single and multi-processor systems. In summary, the ‘group’ solver is a third faster on a single processor than the single domain strategy and preserves its scalability on a parallel cluster system.     

A correlative classifiers approach based on particle filter and sample set for tracking occluded target

Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.     

A kernel extreme learning machine algorithm based on improved particle swam optimization

Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unstable. In this paper, we use the active operators particle swam optimization algorithm (APSO) to obtain an optimal set of initial parameters for KELM, thus creating an optimal KELM classifier named as APSO-KELM. Experiments on standard genetic datasets show that APSO-KELM has higher classification accuracy when being compared to the existing ELM, KELM, and these algorithms combining PSO/APSO with ELM/KELM, such as PSO-KELM, APSO-ELM, PSO-ELM, etc. Moreover, APSO-KELM has good stability and convergence, and is shown to be a reliable and effective classification algorithm.     

Chaotic secure communication based on particle filtering

Applying the particle filtering technique, this paper considers the problem of chaotic secure communication. Even if there exist system noise and measurement noise in chaotic maps, the modulated messages can be estimated by a modified particle filter algorithm, i.e. a particle filter with message estimation. Furthermore, the driving signal can be arbitrarily nonlinear, which improves the security level for communication. Simulation results of Holmes map verify our main result.     

Chaotic secure communication based on particle filtering

Applying the particle filtering technique, this paper considers the problem of chaotic secure communication. Even if there exist system noise and measurement noise in chaotic maps, the modulated messages can be estimated by a modified particle filter algorithm, i.e. a particle filter with message estimation. Furthermore, the driving signal can be arbitrarily nonlinear, which improves the security level for communication. Simulation results of Holmes map verify our main result.     

A sheaf model for intuitionistic quantum mechanics

We analyze the passing of a single particle through a double-slit apparatus. This is done using a sheaf model of intuitionistic logic. We develop an algebra of slits and study the non-classical behaviour of quantum slits. A specific interference formula is obtained by averaging. This formula extends the usual one.This work was partially supported by an Australian Research Council program grant and by theDeutsche Forschungsgemeinschaft.     

A methodology for supporting strategy implementation based on the VSM: A case study in a Latin-American multi-national

Soft OR tools have increasingly been used to support the strategic development of companies at operational and managerial levels. However, we still lack OR applications that can be useful in dealing with the “implementation gap”, understood as the scarcity of resources available to organizations seeking to align their existing processes and structures with a new strategy. In this paper we contribute to filling that gap, describing an action research case study where we supported strategy implementation in a Latin American multinational corporation through a soft OR methodology. We enhanced the ‘Methodology to support organizational self-transformation’, inspired by the Viable System Model, with substantive improvements in data collection and analyses. Those adjustments became necessary to facilitate second order learning and agreements on required structural changes among a large number of participants. This case study contributes to the soft OR and strategy literature with insights about the promise and constraints of this soft OR methodology to collectively structure complex decisions that support organizational redesign and strategy implementation.     

A high‐order finite difference discretization strategy based on extrapolation for convection diffusion equations

We propose a new high‐order finite difference discretization strategy, which is based on the Richardson extrapolation technique and an operator interpolation scheme, to solve convection diffusion equations. For a particular implementation, we solve a fine grid equation and a coarse grid equation by using a fourth‐order compact difference scheme. Then we combine the two approximate solutions and use the Richardson extrapolation to compute a sixth‐order accuracy coarse grid solution. A sixth‐order accuracy fine grid solution is obtained by interpolating the sixth‐order coarse grid solution using an operator interpolation scheme. Numerical results are presented to demonstrate the accuracy and efficacy of the proposed finite difference discretization strategy, compared to the sixth‐order combined compact difference (CCD) scheme, and the standard fourth‐order compact difference (FOC) scheme. © 2003 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 20: 18–32, 2004.