• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.962-975
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• 2020

The accurate segmentation of infant brain MR image into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is very important for early studying of brain growing patterns and morphological changes in neurodevelopmental disorders. Because of inherent myelination and maturation process, the WM and GM of babies (between 6 and 9 months of age) exhibit similar intensity levels in both T1-weighted (T1w) and T2-weighted (T2w) MR images in the isointense phase, which makes brain tissue segmentation very difficult. We propose a deep network architecture based on U-Net, called Triple Residual Multiscale Fully Convolutional Network (TRMFCN), whose structure exists three gates of input and inserts two blocks: residual multiscale block and concatenate block. We solved some difficulties and completed the segmentation task with the model. Our model outperforms the U-Net and some cutting-edge deep networks based on U-Net in evaluation of WM, GM and CSF. The data set we used for training and testing comes from iSeg-2017 challenge (http://iseg2017.web.unc.edu).

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.

• Transactions of Materials Processing
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• v.26 no.5
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• pp.286-292
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• 2017

Magnesium alloy shows strong anisotropy and asymmetric behavior in tension and compression curve, especially at room temperature. These characteristics limit the application of finite element method (FEM) which is based on conventional continuum mechanics. To accurately predict the material behavior of magnesium alloy at microstructural level, a methodology of fully coupled multiscale simulation is presented and a crystal plasticity model as a constitutive equation in the simulation of metal forming process is introduced in this study. The existing constitutive equation for rigid plastic FEM is modified to accommodate deviatoric stress component and its derivatives with respect to strain rate components. Viscoplastic self-consistent (VPSC) polycrystal model was selected as a constitutive model because it was regarded as the most robust model compared to Taylor model or Sachs model. Stiffness matrix and load vector were derived based on the new approach and implemented into $DEFORM^{TM}-3D$ via a user subroutine handling stiffness matrix at an elemental level. The application to extrusion and rolling process of pure magnesium is presented in this study to assess the validity of the proposed multiscale process.

• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.569-575
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• 2017

One of the important requirements for the application of reduced-activation ferritic/martensitic (RAFM) steel is to retain proper mechanical properties under irradiation and high-temperature conditions. To simulate the yield strength and stress-strain curve of steels during high-temperature and irradiation conditions, a multiscale simulation method consisting of both microstructure and strengthening simulations was established. The simulation results of microstructure parameters were added to a superposition strengthening model, which consisted of constitutive models of different strengthening methods. Based on the simulation results, the strength contribution for different strengthening methods at both room temperature and high-temperature conditions was analyzed. The simulation results of the yield strength in irradiation and high-temperature conditions were mainly consistent with the experimental results. The optimal application field of this multiscale model was 9Cr series (7-9 wt.%Cr) RAFM steels in a condition characterized by 0.1-5 dpa (or 0 dpa) and a temperature range of $25-500^{\circ}C$.

• Interaction and multiscale mechanics
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• v.4 no.2
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• pp.139-153
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• 2011

In this paper we develop a fully anisotropic pressure and temperature dependent model to investigate the effect of the microstructure on the shock response of ${\beta}$-HMX molecular single and polycrystals. This micromechanics-based model can account for crystal orientation as well as crystallographic twinning and slip during deformation and has been calibrated using existing gas gun data. We observe that due to the high degree of anisotropy of these polycrystals, certain orientations are more favorable for plastic deformation - and therefore defect and dislocation generation - than others. Loading along these directions results in highly localized deformation and temperature fields. This observation confirms that most of the temperature rise during high rates of loading is due to plastic deformation or dislocation pile up at microscale and not due to volumetric changes.

• East Asian mathematical journal
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• v.34 no.3
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• pp.239-248
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• 2018

In this paper, we derive a closed-form formula of a second-order approximation for a European corrected option price under stochastic elasticity of variance model mentioned in Kim et al. (2014) [1] [J.-H. Kim, J Lee, S.-P. Zhu, S.-H. Yu, A multiscale correction to the Black-Scholes formula, Appl. Stoch. Model. Bus. 30 (2014)]. To find the explicit-form correction to the option price, we use Mellin transform approaches.

• Advances in nano research
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• v.7 no.6
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• pp.419-429
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• 2019

The effect of an increasing percentage of nanofiller (glass cenosphere) with Glass/Epoxy hybrid composite curved panels modeled mathematically using the multiscale concept and subsequent numerical eigenvalues of different geometrical configurations (cylindrical, spherical, elliptical, hyperboloid and flat) predicted in this research article. The numerical model of Glass/Epoxy/Cenosphere is derived using the higher-order polynomial type of kinematic theory in association with isoparametric finite element technique. The multiscale mathematical model utilized for the customized computer code for the evaluation of the frequency data. The numerical model validation and consistency verified with experimental frequency data and convergence test including the experimental elastic properties. The experimental frequencies of the multiscale nano filler-reinforced composite are recorded through the impact hammer frequency test rig including CDAQ-9178 (National Instruments) and LABVIEW virtual programming. Finally, the nano cenosphere filler percentage and different design associated geometrical parameters on the natural frequency data of hybrid composite structural configurations are illustrated through a series of numerical examples.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.40-48
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• 2007

This paper presents a novel texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields in multiscale Bayesian framework. Multiscale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Texture classification at each scale is performed by the posterior probabilities from MLP networks and MAP (maximum a posterior) classification. Then, in order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multiscale MAP classifications sequentially from coarse to fine scales. This process is done by computing the MAP classification given the classification at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale classification. In this fusion process, the MRF (Markov random field) prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. The proposed segmentation method shows better performance than texture segmentation using the HMT (Hidden Markov trees) model and HMTseg.

• Nuclear Engineering and Technology
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• v.41 no.1
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• pp.11-20
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• 2009

Radiation effects on materials are inherently multiscale phenomena in view of the fact that various processes spanning a broad range of time and length scales are involved. A multiscale modeling approach to embrittlement of pressure vessel steels is presented here. The approach includes an investigation of the mechanisms of defect accumulation, microstructure evolution and the corresponding effects on mechanical properties. An understanding of these phenomena is required to predict the behavior of structural materials under irradiation. We used molecular dynamics (MD) simulations at an atomic scale to study the evolution of high-energy displacement cascade reactions. The MD simulations yield quantitative information on primary damage. Using a database of displacement cascades generated by the MD simulations, we can estimate the accumulation of defects over diffusional length and time scales by applying kinetic Monte Carlo simulations. The evolution of the local microstructure under irradiation is responsible for changes in the physical and mechanical properties of materials. Mechanical property changes in irradiated materials are modeled by dislocation dynamics simulations, which simulate a collective motion of dislocations that interact with the defects. In this paper, we present a multi scale modeling methodology that describes reactor pressure vessel embrittlement in a light water reactor environment.

• Composite Materials and Engineering
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• v.3 no.3
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• pp.221-239
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• 2021

This paper presents a multiscale modeling method for sheet molding compound (SMC) composites through a novel bundle packing reconstruction algorithm based on a micro-CT (Computed Tomography) image processing. Due to the complex flow pattern during the compression molding process, the SMC composites show a spatially varying orientation and overlapping of fiber bundles. Therefore, significant inhomogeneity and anisotropy are commonly observed and pose a tremendous challenge to predicting SMC composites' properties. For high-fidelity modeling of the SMC composites, the statistical distributions for the fiber orientation and local volume fraction are characterized from micro-CT images of real SMC composites. After that, a novel bundle packing reconstruction algorithm for a high-fidelity SMC model is proposed by considering the statistical distributions. A method for evaluating specimen level's strength and stiffness is also proposed from a set of high-fidelity SMC models. Finally, the proposed multiscale modeling methodology is experimentally validated through a tensile test.