Pre-equating: a simulation study based on a large scale assessment model

Although post-equating (PE) has proven to be an acceptable method in the scaling and equating of items and forms, there are times when the turn-around period for equating and converting raw scores to scale scores is so small that PE cannot be undertaken within the prescribed time frame. In such cases, pre-equating (PrE) could be considered as an acceptable alternative. Assessing the feasibility of using item calibrations from the item bank (as in PrE) is conditioned on the equivalency of the calibrations and the errors associated with it vis a vis the results obtained via PE. This paper creates item banks over three periods of item introduction into the banks and uses the Rasch model in examining data with respect to the recovery of item parameters, the measurement error, and the effect cut-points have on examinee placement in both the PrE and PE situations. Results indicate that PrE is a viable solution to PE provided the stability of the item calibrations are enhanced by using large sample sizes (perhaps as large as full-population) in populating the item bank.     

POD‐based model reduction with empirical interpolation applied to nonlinear elasticity

The constantly rising demands on finite element simulations yield numerical models with increasing number of degrees‐of‐freedom. Due to nonlinearity, be it in the material model or of geometrical nature, the computational effort increases even further. For these reasons, it is today still not possible to run such complex simulations in real time parallel to, for example, an experiment or an application. Model reduction techniques such as the proper orthogonal decomposition method have been developed to reduce the computational effort while maintaining high accuracy. Nonetheless, this approach shows a limited reduction in computational time for nonlinear problems. Therefore, the aim of this paper is to overcome this limitation by using an additional empirical interpolation. The concept of the so‐called discrete empirical interpolation method is translated to problems of solid mechanics with soft nonlinear elasticity and large deformations. The key point of the presented method is a further reduction of the nonlinear term by an empirical interpolation based on a small number of interpolation indices. The method is implemented into the finite element method in two different ways, and it is extended by using different solution strategies including a numerical as well as a quasi‐Newton tangent. The new method is successfully applied to two numerical examples concerning hyperelastic as well as viscoelastic material behavior. Using the extended discrete empirical interpolation method combined with a quasi‐Newton tangent enables reductions in computational time of factor 10 with respect to the proper orthogonal decomposition method without empirical interpolation. Negligibly, orders of error can be reached. Copyright © 2015 John Wiley & Sons, Ltd.     

On a mesh optimization method based on a minimization of interpolation error

In this article a new mesh optimization technique for finite element methods is presented. The mesh modification strategy is based on a minimization of an interpolation error estimate and can be viewed as a generalization of existing conformai map-type mesh generators. The new mesh results in an essential improvement of finite element approximation which is illustrated in numerical examples.     

Dual-reciprocity BEM based on global interpolation functions

Several global shape functions are introduced to interpolate the body-force term in the dual-reciprocity boundary-element method. These global-interpolation functions, which include polynomial, trigonometric, and hyperbolic series, can be used in place of the locally based radial shape function. For the several examples presented, the global functions have demonstrated superior convergence properties.     

Discovering communities based on mention distance

Scientometrics - Scholarly community detection has important applications in various fields. Current studies rely heavily on structured scholar networks, which have high computational complexity...     

Substructured macro elements based on locally blended interpolation

In this paper we describe a new class of locally refined macro finite elements which are especially amenable to the use of substructuring techniques for the efficient solution of the resulting idealization. The tools and guidelines illustrated by the examples of modelling crack tips, point load singularities and singularities at re-entrant corners should enable an analyst to construct other such blended macro elements specifically tailored to his particular class of problems. The use of such substructured macro elements in finite element calculations permits substantial reduction in the manual effort of data preparation and the computational cost of numerical solution.     

基于B样条插值的局部均值分解方法研究

局部均值分解(Local Mean Decomposition,LMD)方法是一种较新的自适应信号分析方法。LMD算法的核心思想是将原始信号分解为多个乘积函数(Production Function,PF),其中每个PF都是一个包络函数和一个纯调频函数的乘积。在LMD算法中需要提取信号的局部均值函数和包络估计,然而常规的提取方法会带来局部误差且分解速度慢。为了解决此问题,提出了利用三次B样条对信号上、下极值点进行插值得到上、下包络线,进而获取信号局部均值和包络估计的新方法。对仿真信号和机械振动信号的对比实验验证了该方法的优越性。     

利用二维插值核的高分辨力图像重构

利用连续图像采样及其内插公式,提出了一种基于二维插值核重建高分辨力图像的新算法。该算法先使一般低分辨力序列图像具有标准位移关系,再根据高、低分辨力图像之间的关系重构高分辨力图像。同时利用正则化技术导出了一套重复迭代算法,以提高算法的处理效果和克服病态问题。实验表明,该算法使图像的细节和清晰度大大增强,分辨力大幅度提高,明显优于零阶保持插值算法的处理结果,其峰值信噪比提高8-9dB。     

Goodnes-of-fit tests for location and scale families based on a weighted L<Subscript>2</Subscript>-Wasserstein distance measure

In two recent papers del Barrio et al. (1999) and del Barrio et al. (2000) consider a new class of goodness-of-fit statistics based on theL ₂-Wasserstein distance. They derive the limiting distribution of these statistics and show that the normal distribution is the only location-scale family for which this limiting distribution has the “loss of degrees of freedom” property, due to the estimation of the unknown parameters. In this paper a weightedL ₂-Wasserstein distance is considered and it is proven that these statistics retain the loss of degrees of freedom property for general classes of distributions if applied separately to the location family and to the scale family and if the “right” weight function is used. These weight functions are such that the corresponding minimum distance estimators for the location parameter and the scale parameter are asymptotically efficient. Examples are discussed for both location and scale families.