• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1075-1085
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• 2012

In clinical research using medical images, the image segmentation is one of the most important processes. Especially, the hippocampal atrophy is helpful for the clinical Alzheimer diagnosis as a specific marker of the progress of Alzheimer. In order to measure hippocampus volume exactly, segmentation of the hippocampus is essential. However, the hippocampus has some features like relatively low contrast, low signal-to-noise ratio, discreted boundary in MRI images, and these features make it difficult to segment hippocampus. To solve this problem, firstly, We selected region of interest from an experiment image, subtracted a original image from the negative image of the original image, enhanced contrast, and applied anisotropic diffusion filtering and gaussian filtering as preprocessing. Finally, We performed an image segmentation using two level set methods. Through a variety of approaches for the validation of proposed hippocampus segmentation method, We confirmed that our proposed method improved the rate and accuracy of the segmentation. Consequently, the proposed method is suitable for segmentation of the area which has similar features with the hippocampus. We believe that our method has great potential if successfully combined with other research findings.

• Journal of Korean Society of Disaster and Security
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• v.6 no.2
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• pp.23-30
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• 2013

A dynamic analysis of random vibration processes is concerned with the first excursion probability based on first passage time during some specified lifetime or duration of the excitation. This study is concerned with the estimation of first-passage probability for hazard fluctuate wind velocity in the major cities reflecting the recent meteorological with largest data samples (yearly 2003-2012). The basic wind speeds were standardized homogeneously to the surface roughness category C, and to 10m above the ground surface. In this paper, the hazard fluctuate wind velocities are treated as a time-independent (stationary) random process and Gaussian random processes. The first excursion probability were calculated from Poisson model based on the independent event of level crossing & two-state Markov model based on the envelopes of level crossing.

• Structural Engineering and Mechanics
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• v.49 no.2
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• pp.203-223
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• 2014

The extensive use of prestressed reinforced concrete (PSC) highway bridges in marine environment drastically increases the sensitivity to both fatigue-and corrosion-induced damage of their critical structural components during their service lives. Within this scenario, an integrated method that is capable of evaluating the fatigue reliability, identifying a condition-based maintenance, and predicting the remaining service life of its critical components is therefore needed. To accomplish this goal, a procedure for fatigue reliability prediction of PSC highway bridges is proposed in the present study. Vehicle-bridge coupling vibration analysis is performed for obtaining the equivalent moment ranges of critical section of bridges under typical fatigue truck models. Three-dimensional nonlinear mathematical models of fatigue trucks are simplified as an eleven-degree-of-freedom system. Road surface roughness is simulated as zero-mean stationary Gaussian random processes using the trigonometric series method. The time-dependent stress-concentration factors of reinforcing bars and prestressing tendons are accounted for more accurate stress ranges determination. The limit state functions are constructed according to the Miner's linear damage rule, the time-dependent S-N curves of prestressing tendons and the site-specific stress cycle prediction. The effectiveness of the methodology framework is demonstrated to a T-type simple supported multi-girder bridge for fatigue reliability evaluation.

• The Journal of the Acoustical Society of Korea
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• v.9 no.2
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• pp.61-69
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• 1990

We have investigated the digital bit synchronization problem in baseband communication receiver systems using the Gauss-Markov estimation technique which is equivalent to the weighted least square method. The realized bit synchronizer, including the data detector, processes the input signal two dimensionally into the transition phase and data level under the white Gaussian noise environment. We have confirmed the relization of the bit synchronizer via computer simulation. In addition, we have compared and evaluated the estimation error performance of the proposed method with that of the conventional DTTL method and of the minimum likelihood method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.7 s.262
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• pp.792-799
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• 2007

A newly developed cellular metal based on kagome lattice is an ideal candidate for multifunctional materials achieving various optimal properties. Intensive efforts have been devoted to develop efficient techniques for mass production due to its wide potential applications. Since a variety of imperfections would be inevitably included in the realistic fabrication processes, it is highly important to examine the correlation between the imperfections and material strengths. Previous performance tests were mostly done by numerical simulations such as finite element method (FEM), but only for perfect structures without any imperfection. In this paper, we developed an efficient numerical framework using nonlinear random network analysis (RNA) to verify how the statistical imperfections (geometrical and material property) contribute to the performance of general truss structures. The numerical results for kagome truss structures are compared with experimental measurements on 3-layerd WBK (wire-woven bulk kagome). The mechanical strength of the kagome structures is shown relatively stable with the Gaussian types of imperfections.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.78-83
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• 2007

A newly developed cellular metal based on kagome lattice is an ideal candidate for multifunctional materials achieving various optimal properties. Intensive efforts have been devoted to develop efficient techniques for mass production due to its wide potential applications. Since a variety of imperfections would be inevitably included in the realistic fabrication processes, it is highly important to examine the correlation between the imperfections and material strengths. Previous performance tests were mostly done by numerical simulations such as finite element method (FEM), but only for perfect structures without any imperfection. In this paper, we developed an efficient numerical framework using nonlinear random network analysis (RNA) to verify how the statistical imperfections (geometrical and material property) contribute to the performance of general truss structures. The numerical results for kagome truss structures are compared with experimental measurements on 3-layerd WBK (wire-woven bulk kagome). The mechanical strength of the kagome structures is shown relatively stable with the Gaussian types of imperfections.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.5 no.4
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• pp.302-306
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• 1993

Assuming Gaussian distribution of the density difference between a turbulent jet river plume and its ambient saline water, a hydrodynamic solution for the lateral spreading of a river plume is developed. Two advantages can be expected from the assumption we made. Firstly, we need not consider mixing processes in the plume in dealing with this Problem. Secondly, by Putting pressure gradients which can be obtained from the density distribution, into the equation of motion, we can solve them easily. We compared the analytic solution with the fold data of the Nakdong river plume and found reasonably good correspondence.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.155-164
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• 1999

In this paper, the prototype of SIS(Simulated Implantation System) for human femoral head is introduced. SIS is a software which carries on a virtual femoral head replacement surgery including 3d visualization as well as various numeric analyses between a patient's femur and artificial femur through certain stages of the image processing and of the computer graphics. Also, processes required after acquiring consecutive CT-images and projected image of an artificial femur are discussed, and the corresponding results including prototype of SIS are given.

• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.