• Coupled systems mechanics
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• v.11 no.5
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• pp.411-438
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• 2022

In this paper, we present the parameter identification for inelastic and multi-scale problems. First, the theoretical background of several fundamental methods used in the upscaling process is reviewed. Several key definitions including random field, Bayesian theorem, Polynomial chaos expansion (PCE), and Gauss-Markov-Kalman filter are briefly summarized. An illustrative example is given to assimilate fracture energy in a simple inelastic problem with linear hardening and softening phases. Second, the parameter identification using the Gauss-Markov-Kalman filter is employed for a multi-scale problem to identify bulk and shear moduli and other material properties in a macro-scale with the data from a micro-scale as quantities of interest (QoI). The problem can also be viewed as upscaling homogenization.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.69-77
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• 2017

This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.264-265
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• 2006

The directions of further developments in the modeling of sintering are pointed out, including multi-scale modeling of sintering, on-line sintering damage criteria, particle agglomeration, sintering with phase transformations. A true multi-scale approach is applied for the development of a new meso-macro methodology for modeling of sintering. The developed macroscopic level computational framework envelopes the mesoscopic simulators. No closed forms of constitutive relationships are assumed for the parameters of the material. The model framework is able to predict the final dimensions of the sintered specimen on a global scale and identify the granular structure in any localized area for prediction of the material properties.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5179-5196
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• 2019

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

• Nuclear Engineering and Technology
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• v.41 no.1
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• pp.1-10
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• 2009

The safe and reliable performance of fusion and fission plants depends on the choice of suitable materials and an assessment of long-term materials degradation. These materials are degraded by their exposure to extreme conditions; it is necessary, therefore, to address the issue of long-term damage evolution of materials under service exposure in advanced plants. The empirical approach to the study of structural materials and fuels is reaching its limit when used to define and extrapolate new materials, new environments, or new operating conditions due to a lack of knowledge of the basic principles and mechanisms present. Materials designed for future Gen IV systems require significant innovation for the new environments that the materials will be exposed to. Thus, it is a challenge to understand the materials more precisely and to go far beyond the current empirical design methodology. Breakthrough technology is being achieved with the incorporation in design codes of a fundamental understanding of the properties of materials. This paper discusses the multi-scale, multi-code computations and multi-dimensional modelling undertaken to understand the mechanical properties of these materials. Such an approach is envisaged to probe beyond currently possible approaches to become a predictive tool in estimating the mechanical properties and lifetimes of materials.

• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.608-619
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• 2010

A multi-scale analysis of water-cooled reactor thermalhydraulics can be used to take advantage of increased computer power and improved simulation tools, including Direct Numerical Simulation (DNS), Computational Fluid Dynamics (CFD) (in both open and porous mediums), and system thermalhydraulic codes. This paper presents a general strategy for this procedure for various thermalhydraulic scales. A short state of the art is given for each scale, and the role of the scale in the overall multi-scale analysis process is defined. System thermalhydraulic codes will remain a privileged tool for many investigations related to safety. CFD in porous medium is already being frequently used for core thermalhydraulics, either in 3D modules of system codes or in component codes. CFD in open medium allows zooming on some reactor components in specific situations, and may be coupled to the system and component scales. Various modeling approaches exist in the domain from DNS to CFD which may be used to improve the understanding of flow processes, and as a basis for developing more physically based models for macroscopic tools. A few examples are given to illustrate the multi-scale approach. Perspectives for the future are drawn from the present state of the art and directions for future research and development are given.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.1
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• pp.20-27
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• 1987

The hierarchical approach method is proposed to sperate each different time scale sub-systems from linear time invariant multi-parameter singular perturbation systems. By means of this proposal, the original multi-parameter singular perturbation systems is completely separated into independent subsystems with each different time scale. It is also investigated that the controllability of the system is invariant. And this paper applies singular perturbation methods to the minimum control effort problem for linear time invariant systems with constrained controls. Also near-optimum control theory, which is based on dividing the total time interval with the time scales respectively, is proposed. As a result, the time scale separation method is show to be particularly useful in a near optimum design which can be otained through a decentralized control structure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3176-3183
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• 2010

A multi-scale fatigue life prediction methodology of composite pressure vessels subjected to multi-axial loading has been proposed in this paper. The multi-scale approach starts from the constituents, fiber, matrix and interface, leading to predict behavior of ply, laminates and eventually the composite structures. The multi-scale fatigue life prediction methodology is composed of two steps: macro stress analysis and micro mechanics of failure based on fatigue analysis. In the macro stress analysis, multi-axial fatigue loading acting at laminate is determined from finite element analysis of composite pressure vessel, and ply stresses are computed using a classical laminate theory. The micro stresses are calculated in each constituent from ply stresses using a micromechanical model. Three methods are employed in predicting fatigue life of each constituent, i.e. a maximum stress method for fiber, an equivalent stress method for multi-axially loaded matrix, and a critical plane method for the interface. A modified Goodman diagram is used to take into account the generic mean stresses. Damages from each loading cycle are accumulated using Miner's rule. Monte Carlo simulation has been performed to predict the overall fatigue life of a composite pressure vessel considering statistical distribution of material properties of each constituent, fiber volume fraction and manufacturing winding angle.

• Coupled systems mechanics
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• v.8 no.2
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• pp.99-109
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• 2019

This work investigates the accuracy and performance of a $FE^2$ multi-scale implementation used to predict the behavior of composite materials. The equations are formulated assuming the small deformations solid mechanics approach in non-linear material models with hardening plasticity. The uniform strain boundary conditions are applied for the macro-to-micro transitions. A parallel algorithm was implemented in order to solve large engineering problems. The scheme proposed takes advantage of the domain decomposition method at the macro-scale and the coupling between each subdomain with a micro-scale model. The precision of the method is validated with a composite material problem and scalability tests are performed for showing the efficiency.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2384-2392
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• 2015

The technical development and practical applications of big-data for health is one hot topic under the banner of big-data. Big-data medical image fusion is one of key problems. A new fusion approach with coding based on Spherical Coordinate Domain (SCD) in Wireless Sensor Network (WSN) for big-data medical image is proposed in this paper. In this approach, the three high-frequency coefficients in wavelet domain of medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on the multi-scale edge of medical image, it can be fused and reconstructed. Experimental results indicate the novel approach is effective and very useful for transmission of big-data medical image(especially, in the wireless environment).