Hyeona Lim

Compressive behavior of a turtle's shell: experiment, modeling, and simulation.

The turtles shell acts as a protective armor for the animal. By analyzing a turtle shell via finite element analysis, one can obtain the strength and stiffness attributes to help design man-made armor. As such, finite element analysis was performed on a Terrapene carolina box turtle shell. Experimental data from compression tests were generated to provide insight into the scute through-thickness behavior of the turtle shell. Three regimes can be classified in terms of constitutive modeling: linear elastic, perfectly inelastic, and densification regions, where hardening occurs. For each regime, we developed a model that comprises elasticity and densification theory for porous materials and obtained all the material parameters by correlating the model with experimental data. The different constitutive responses arise as the deformation proceeded through three distinctive layers of the turtle shell carapace. Overall, the phenomenological stress-strain behavior is similar to that of metallic foams.

A hybrid level set segmentation for medical imagery

This article is concerned with a level set segmentation (active contour) algorithm for medical imagery. Due to difficulties such as noise and unclear edges, it is often challenging to obtain a reliable segmentation for medical images. In addition to introducing a new hybrid model which combines a gradient-based model and the Mumford-Shah (gradient-free) method, we study the so-called method of background subtraction (MBS) in order to improve reliability of the new model. A linearized alternating direction implicit method is applied for an efficient time integration. For a fast convergence, we also suggest effective initialization strategies for the level set function. The resulting algorithm has proved to locate the desired edges in 2-4 iterations.

Simulation of a hybrid model for image denoising

We propose a new model for image denoising which is a hybrid of the total variation model and the Laplacian mean-curvature model. An efficient numerical procedure to compute the hybrid model is also presented. The hybrid model and its computational procedure introduce a number of parameters. As a preliminary step to the synthesis of a method for selecting optimal parameters, the hybrid model was simulated on a number of known images with synthetically added noise. The parallel simulation code was easily composed from existing serial code and a dynamic load balancing tool. The estimated parallel efficiency of the simulation is in excess of 96% on 32 processors of a general-purpose Linux cluster.

Nonlocal Speckle Denoising Model Based on Non-linear Partial Differential Equations

Image denoising is among the most fundamental problems in image processing. A large range of methods covering various fields of mathematics are available for denoising an image. The initial denoising models are derived from energy minimization using nonlinear partial differential equations (PDEs). The filtering based models have also been used for quite a long time where the denoising is done by smoothing operators. The most successful among them was the very recently developed nonlocal means method proposed by Buades, Coll and Morel in 2005. Though the method is very accurate in removing noise, it is very slow and hence quite impractical. In 2008, Gilboa and Osher extended some known PDE and variational techniques in image processing to the nonlocal framework. The motivation behind this was to make any point interact with any other point in the image. Using nonlocal PDE operators, they proposed the nonlocal total variation method for Gaussian noise. In this paper, we develop a nonlinear PDE based accelerated diffusion speckle denoising model. For faster convergence, we use the Split Bregman scheme to find the solution to this new model. The new model shows more accurate results than the existing speckle denoising model. It is also faster than the original nonlocal means method.

Structure-property responses of bio-inspired synthetic foams at low and high strain rates

Abstract Various aluminum foams were fabricated with a structure comparable to the Terrapene carolina (box turtle) shell hierarchy as a synthetic means of attaining the lightweight, yet impact-resistive, nature of the biological counterpart. Each foam was constructed from a single aluminum alloy but with different morphologies and foam densities. By borrowing from the sophistication of biological design, the aluminum foams were shown to exhibit robust mechanical performance. High strain rate experimentation, via split Hopkinson pressure bar, was utilized to reveal the strain rate sensitivity of the foams as well as a metric to compare impact performance. The structure-property relations, necessary for accurate material modeling, were also characterized by way of optical microscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, and nanoindentation tests. The robust varying mechanical performance was attributed to the biologically inspired materials design.

Nonlocal Total Variation Based Speckle Denoising Model

A large range of methods covering various fields of mathematics are available for denoising an image. The initial denoising models are derived from energy minimization using nonlinear partial differential equations (PDEs). The filtering models based on smoothing operators have also been used for denoising. Among them the recently developed nonlocal means method proposed by Buades, Coll and Morel in 2005 is quite successful. Though the method is very accurate, it is very slow and hence quite impractical. In 2008, Gilboa and Osher extended some known PDE and variational techniques in image processing to the nonlocal framework and proposed the nonlocal total variation method for Gaussian noise. We used this idea to develop a nonlocal model for speckle noise. Here we have extended the speckle model introduced by Krissian et al. in 2005 to the nonlocal framework. The Split Bregman scheme is used solve this new model.

A parameter study of a hybrid Laplacian mean-curvature flow denoising model

This article presents results of a parameter study for a new denoising model, using parallel computing and advanced dynamic load balancing techniques for performance improvement of implementations. A denoising model is suggested hybridizing total variation and Laplacian mean-curvature; the fourth-order model and its numerical procedure introduce a number of parameters. As a preliminary step in the model development, a parameter study has been undertaken in order to discover solitary and interactive effects of the parameters on model accuracy. Such a parameter study is necessarily time-consuming due to the huge number of combinations of the parameter values to be tested. In addition, the study has to be performed on various images, thereby increasing the overall investigation time. The performance of this first parallel implementation of a new hybrid model for image denoising is evaluated when the application is running on heterogeneous environments. The hybrid model is simulated on a general-purpose Linux cluster for which the parallel efficiency exceeds 96%.