Simulation of the diffusion of point defects in structures with local elastic stresses

The stress-mediated diffusion of nonequilibrium point defects into the bulk of a semiconductor is investigated by computer simulation. It is assumed that the point defects are generated on the surface of a semiconductor and that in the course of diffusion they pass through the local region of elastic stresses because the average length of defect migration is greater than the thickness and depth of the strained layer. Within the strained layer, point defect segregation or heavy defect depletion occurs if defect drift under stresses is directed in or out of the layer, respectively. The calculations also show that, in contrast to the case of local defect sink, the local region of elastic stresses practically does not change the distribution of defects beyond this region if there is no generation/absorption of point defects within the strained layer.     

Spatial defect pattern recognition on semiconductor wafers using model-based clustering and Bayesian inference

Defects on semiconductor wafers tend to cluster and the spatial defect patterns contain useful information about potential problems in the manufacturing process. This study proposes to use model-based clustering algorithms via Bayesian inferences for spatial defect pattern recognition on semiconductor wafers. These new algorithms can find the number of defect clusters as well as identify the pattern of each cluster automatically. They are capable of detecting curvilinear patterns, ellipsoidal patterns and nonuniform global defect patterns. Promising results have been obtained from simulation studies.     

Near-field simulation of acoustic effects in tire tread cavities

We report the results of numerical calculations of acoustic waves that are generated when the tire tread hits the road surface. The tread blocks are modeled by parallelepipeds and their compression and expansion is modeled by the motion of the cavity walls in accordance with a specified law. The acoustic perturbations generated by this process are investigated on the basis of the complete nonlinear Euler equations using the Godunov method. A parametric analysis of the model is carried out. __________ Translated from Prikladnaya Matematika i Informatika, No. 26, pp. 18–38, 2007.     

Stochastic properties of autowave turbulence elimination

The complex spatio-temporal behavior due to spiral breakup and wave interaction, in a 2D reaction diffusion system is eliminated by spatially uniform perturbation. This autowave turbulence is modeled by a stochastic process of defect production and annihilation. The constructed solution agrees well with probabilities – in particular probability to have no defects – estimated from the reaction diffusion system. The time to extinction, related to the probability of eliminating all defects, depends on the medium size and the applied perturbation amplitude.     

PSOHS: an efficient two-stage approach for data clustering

Cluster analysis is an important task in data mining and refers to group a set of objects such that the similarities among objects within the same group are maximal while similarities among objects from different groups are minimal. The particle swarm optimization algorithm (PSO) is one of the famous metaheuristic optimization algorithms, which has been successfully applied to solve the clustering problem. However, it has two major shortcomings. The PSO algorithm converges rapidly during the initial stages of the search process, but near global optimum, the convergence speed will become very slow. Moreover, it may get trapped in local optimum if the global best and local best values are equal to the particle’s position over a certain number of iterations. In this paper we hybridized the PSO with a heuristic search algorithm to overcome the shortcomings of the PSO algorithm. In the proposed algorithm, called PSOHS, the particle swarm optimization is used to produce an initial solution to the clustering problem and then a heuristic search algorithm is applied to improve the quality of this solution by searching around it. The superiority of the proposed PSOHS clustering method, as compared to other popular methods for clustering problem is established for seven benchmark and real datasets including Iris, Wine, Crude Oil, Cancer, CMC, Glass and Vowel.     

Optical Soliton Bullets in (2+1)D Nonlinear Bragg Resonant Periodic Geometries

We consider light propagation in a Kerr-nonlinear 2D waveguide with a Bragg grating in the propagation direction and homogeneous in the transverse direction. Using Newton's iteration method we construct both stationary and travelling solitary wave solutions of the corresponding mathematical model, the 2D nonlinear coupled mode equations (2D CME). We call these solutions 2D gap solitons due to their similarity with the gap solitons of 1D CME (fiber grating). Long-time stable evolution preserving the solitary fashion is demonstrated numerically despite the fact that, as we show, for the 2D CME no local constrained minima of the Hamiltonian functional exist. Building on the 1D study of [ 1 ], we demonstrate trapping of slow enough 2D gap solitons at localized defects. We explain the mechanism of trapping as resonant transfer of energy from the soliton to one or more nonlinear defect modes. For a special class of defects, we construct a family of nonlinear defect modes by numerically following a bifurcation curve starting at analytically or numerically known linear defect modes. Compared to 1D the dynamics of trapping are harder to fully analyze and the existence of many defect modes for a given defect potential causes that slow solitons store a part of their energy for virtually all of the studied attractive defects.     

基于GIS的长三角城市群经济空间集聚特征研究

本文在界定长三角城市群空间范围的基础上,利用长三角城市群各城市2013年相关数据,应用GIS的空间聚类分析功能分别从全局聚类检验和局部聚类检验两个方面分析了长三角城市群的经济空间集聚特征。研究结果表明长三角城市群的经济产出和经济生产要素在全局上存在空间集聚现象,在局部上,GDP、人均GDP等经济产出在向上海市、苏州市等个别城市高-高集聚,而劳动、资本、技术、能源等经济生产要素也在向上海市、苏州市等个别城市高-高集聚。     

混合优化RBF-BP网络的板形缺陷识别

针对传统板形模式识别方法存在精度低、鲁棒性弱的问题,提出了一种混合优化RBF-BP组合神经网络板形模式识别方法。首先利用自组织映射网络(SOM)对样本聚类,利用聚类后的网络拓扑结构确定RBF的中心,并计算RBF的宽度,克服了传统聚类算法随机选取中心导致聚类结果不稳定的问题。然后利用遗传算法(GA)良好的全局搜索能力优化整个网络的权值。RBF-BP组合神经网络是由一个RBF子网和一BP子网串联构成的,该网络同时具备BP神经网络能较好地预测未知样本的能力以及RBF神经网络的逼近速度快的优点。并以某900HC可逆冷轧机板形识别为应用背景,在MATLAB2010a环境下进行仿真实验,结果表明混合优化RBF-BP组合神经网络的板形模式识别方法能够识别出常见的板形缺陷,提高了板形缺陷识别精度并具有较好的鲁棒性,可以满足板带轧机高精度的板形控制要求。     

Nonconvex Piecewise-Quadratic Underestimation for Global Minimization

Motivated by the fact that important real-life problems, such as the protein docking problem, can be accurately modeled by minimizing a nonconvex piecewise-quadratic function, a nonconvex underestimator is constructed as the minimum of a finite number of strictly convex quadratic functions. The nonconvex underestimator is generated by minimizing a linear function on a reverse convex region and utilizes sample points from a given complex function to be minimized. The global solution of the piecewise-quadratic underestimator is known exactly and gives an approximation to the global minimum of the original function. Successive shrinking of the initial search region to which this procedure is applied leads to fairly accurate estimates, within 0.0060%, of the global minima of synthetic nonconvex functions for which the global minima are known. Furthermore, this process can approximate a nonconvex protein docking function global minimum within four-figure relative accuracy in six refinement steps. This is less than half the number of refinement steps required by previous models such as the convex kernel underestimator (Mangasarian et al., Computational Optimization and Applications, to appear) and produces higher accuracy here.     

The geometrical construction of pointwise distributions on curves

A method is developed to generate desirable pointwise distributions along curves. This is accomplished with a simple geometrical construction which provides a global parameter for curvature clustering together with other parameters for arbitrary local clustering specifications. The level of available precision is considerable in that exact numbers of points can be assigned to both local clusters and to curvature simultaneously with specified spacing from the endpoints. The basic construction simply involves the generation of an auxiliary curve along outward normal directions from the given one. The distribution results when uniform arc-length increments are taken along the auxiliary curve and are projected back along the normals to our given curve. This construction can be applied either directly or in the form of equivalent weight functions. Moreover, it is valid regardless of whether the curve lies in Euclidian space or in surfaces and regardless of the dimensionality of the space in which the curve lies.     

A stochastic method for global optimization

A stochastic method for global optimization is described and evaluated. The method involves a combination of sampling, clustering and local search, and terminates with a range of confidence intervals on the value of the global optimum. Computational results on standard test functions are included as well.     

A critical review of discrete filled function methods in solving nonlinear discrete optimization problems

Many real life problems can be modeled as nonlinear discrete optimization problems. Such problems often have multiple local minima and thus require global optimization methods. Due to high complexity of these problems, heuristic based global optimization techniques are usually required when solving large scale discrete optimization or mixed discrete optimization problems. One of the more recent global optimization tools is known as the discrete filled function method. Nine variations of the discrete filled function method in literature are identified and a review on theoretical properties of each method is given. Some of the most promising filled functions are tested on various benchmark problems. Numerical results are given for comparison.     

Application of honey-bee mating optimization algorithm on clustering

Cluster analysis is one of attractive data mining technique that use in many fields. One popular class of data clustering algorithms is the center based clustering algorithm. K-means used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. Over the last decade, modeling the behavior of social insects, such as ants and bees, for the purpose of search and problem solving has been the context of the emerging area of swarm intelligence. Honey-bees are among the most closely studied social insects. Honey-bee mating may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of marriage in real honey-bee. Honey-bee has been used to model agent-based systems. In this paper, we proposed application of honeybee mating optimization in clustering (HBMK-means). We compared HBMK-means with other heuristics algorithm in clustering, such as GA, SA, TS, and ACO, by implementing them on several well-known datasets. Our finding shows that the proposed algorithm works than the best one.     

Local Sampling and Reconstruction in Shift-Invariant Spaces and Their Applications in Spline Subspaces

The local reconstruction from samples is one of the most desirable properties for many applications in signal processing. Local sampling is practically useful since we need only to consider a signal on a bounded interval and computer can only process finite samples. However, the local sampling and reconstruction problem has not been given as much attention. Most of known results concern global sampling and reconstruction. There are only a few results about local sampling and reconstruction in spline subspaces. In this article, we study local sampling and reconstruction in general shift-invariant spaces and multiple generated shift-invariant spaces with compactly supported generators. Then we give several applications in spline subspaces and multiple generated spline subspaces.     

Burn-in considering yield loss and reliability gain for integrated circuits

This paper presents burn-in effects on yield loss and reliability gain for a lifetime distribution developed from a negative binomial defect density distribution and a given defect size distribution, after assuming that the rate of defect growth is proportional to the power of the present defect size. While burn-in always results in yield loss, it creates reliability gain only if either defects grow fast or the field operation time is long. Otherwise, burn-in for a short time could result in reliability loss. The optimal burn-in time for maximizing reliability is finite if defects grow linearly in time and is infinite if defects grow nonlinearly in time. The optimal burn-in time for minimizing cost expressed in terms of both yield and reliability increases in the field operation time initially but becomes constant as the field operation time is long enough. It is numerically shown that increasing mean defect density or defect clustering increases the optimal burn-in time.     

Partial hybrid finite elements for composite laminates

Three types of partial hybrid finite elements are presented in order to set up a global/local finite element model for analysis of composite laminates. In the global/local model, a composite laminate is divided into three different regions: global, local, and transition regions. These are modeled using three different elements. In the global region, a 4-node degenerated plate/shell element is used to model the overall response of the composite laminate. In the local region, a multilayer element is used to predict detailed stress distribution. In the transition region, a multilayer transition element is used to smoothly connect the two previous elements. The global/local finite element model satisfies the compatibility of displacement at the boundary between the global region and the local region. It also satisfies the continuity of transverse stresses at interlaminar surfaces and traction conditions on the top and bottom surfaces of composite laminates. The global/local finite element model has high accuracy and efficiency for stress analysis of composite laminates. A numerical example of analysis of a laminated strip with free edge is presented to illustrate the accuracy and efficiency of the model.     

On the trend to equilibrium for the Vlasov-Poisson-Boltzmann equation

The dynamics of dilute electrons and plasma can be modeled by Vlasov-Poisson-Boltzmann equation, for which the equilibrium state can be a global Maxwellian. In this paper, we show that the rate of convergence to equilibrium is O(t−∞), by using a method developed for the Boltzmann equation without external force in [L. Desvillettes, C. Villani, On the trend to global equilibrium for spatially inhomogeneous kinetic systems: The Boltzmann equation, Invent. Math. 159 (2005) 245-316]. In particular, the idea of this method is to show that the solution f cannot stay near any local Maxwellians for long. The improvement in this paper is to handle the effect from the external force governed by the Poisson equation. Moreover, by using the macro-micro decomposition, we simplify the estimation on the time derivatives of the deviation of the solution from the local Maxwellian with same macroscopic components.     

Interaction of point defects in ferroelectrics -parameter identification and simulation

Ferroelectric materials are used in a wide field of applications, where they are exposed to a high number of mechanical and electrical load cycles. This involves degradation of the material and a decrease of the electromechanical coupling capability, which is usually called electric fatigue. The causes are assumed to be ionic and electronic charge carriers, which interact with each other, with microstructural elements in the bulk and with interfaces. Accumulation of defects can lead to degradation, mechanical damage and dissociation reactions, for more details see e.g. [3]. In order to get a better understanding of the defect accumulation processes, a model based on material forces is used in [6] to simulate the interaction of defects in periodic and in infinite cells. Applying thermodynamically reasonable kinetic laws, defect migration is simulated in a deterministic way in order to understand the general tendency of defect formations. The transversally isotropic material is modelled with linear electromechanical coupling. Here, the defect parameters used in the continuum model are obtained by fitting the results of molecular dynamics (MD) simulations to the continuous spatial fields. Transferring data from the atomic to the continuum level is a field of active research and no unique solution can be presented. On the atomic level, Coulomb–interaction causes a displacement field incompatible to an elastic solution. To address this difficulty, the volume change of a domain around the defect is used to determine defect parameters. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multistart method with estimation scheme for global satisfycing problems

We present a multistart method for solving global satisfycing problems. The method uses data generated by linearly converging local algorithms to estimate the cost value at the local minimum to which the local search is converging. When the estimate indicates that the local search is converging to a value higher than the satisfycing value, the local search is interrupted and a new local search is initiated from a randomly generated point. When the satisfycing problem is difficult and the estimation scheme is fairly accurate, the new method is superior over a straightforward adaptation of classical multistart methods.