Proper Orthogonal Decomposition Closure Models For Turbulent Flows: A Numerical Comparison

This paper puts forth two new closure models for the proper orthogonal decomposition reduced-order modeling of structurally dominated turbulent flows: the dynamic subgrid-scale model and the variational multiscale model. These models, which are considered state-of-the-art in large eddy simulation, together with the mixing length and the Smagorinsky closure models, are tested in the numerical simulation of a 3D turbulent flow around a circular cylinder at Re = 1,000. Two criteria are used in judging the performance of the proper orthogonal decomposition reduced-order models: the kinetic energy spectrum and the time evolution of the POD coefficients. All the numerical results are benchmarked against a direct numerical simulation. Based on these numerical results, we conclude that the dynamic subgrid-scale and the variational multiscale models perform best.Comment: 28 pages, 6 figure

Training verbal working memory in children with mild intellectual disabilities: effects on problem-solving

This multiple case study explores the effects of a cognitive training program in children with mild to borderline intellectual disability. Experimental training effects were evaluated comparing pre-post-test changes after (a) a baseline phase versus a training phase in the same participant, (b) an experimental training versus either a no intervention phase or a control training in two pairs of children matched for cognitive profile. Key elements of the training program included (1) exercises and card games targeting inhibition, switching, and verbal working memory, (2) guided practice emphasizing concrete strategies to engage in exercises, and (3) a variable amount of adult support. The results show that both verbal working memory analyzed with the listening span test and problem-solving tested with the Raven’s matrices were significantly enhanced after the experimental trainin

Bandwidth selection for kernel estimation in mixed multi-dimensional spaces

Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwidth selection. The bandwidth can be fixed for all the data set or can vary at each points. Automatic bandwidth selection becomes a real challenge in case of multidimensional heterogeneous features. This paper presents a solution to this problem. It is an extension of \cite{Comaniciu03a} which was based on the fundamental property of normal distributions regarding the bias of the normalized density gradient. The selection is done iteratively for each type of features, by looking for the stability of local bandwidth estimates across a predefined range of bandwidths. A pseudo balloon mean shift filtering and partitioning are introduced. The validity of the method is demonstrated in the context of color image segmentation based on a 5-dimensional space

Applying model predictive control to power system frequency control

Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) controller, and simulations show that the MPC improves frequency deviation and control performance. © 2013 IEEE

Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters

As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For radio INterferometry). In particular, we focus on the challenging task of automatically finding the optimal regularization parameter values. In practice, finding the regularization parameters using classical grid search is computationally intensive and nontrivial due to the lack of ground- truth. We adopt a greedy strategy where, at each iteration, the optimal parameters are found by minimizing the predicted Stein unbiased risk estimate (PSURE). The proposed self-tuned version of MUFFIN involves parallel and computationally efficient steps, and scales well with large- scale data. Finally, numerical results on a 3D image are presented to showcase the performance of the proposed approach

Specification and estimation of spatial econometric models : A discussion of alternative strategies for spatial economic modelling

The semantical insufficiency of (spatial) economic theories necessitates the making of additional assumptions — thereby introducing substantial specification uncertainty — in order to arrive at a fully specified econometric model. The traditional or current approach to econometric modelling treats specification uncertainty inadequately. This proposition is illustrated by two well-known examples from the spatial economic literature. Two alternative specification strategies for spatial economic modelling — designed to improve the current spatial econometric modelling approach — are proposed. One of these strategies is used for a specification analysis of agricultural output in Eire