• Journal of Broadcast Engineering
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• v.2 no.2
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• pp.156-169
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• 1997

This paper proposes an image segmentation technique using watershed algorithm followed by region merging method. A gradient image is obtained by applying multiscale gradient algorithm to the image simplified by morphological filters. Since the watershed algorithm produces the oversegmented image. it is necessary to merge small segmented regions as wel]' as region having similar characteristics. For region merging. we utilize the merging criteria based on both the mean value of the pixels of each region and the edge intensities between regions obtained by the contour following process. Experimental results show that the proposed method produces meaningful image segmentation results.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.2
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• pp.41.1-41.1
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• 2010

It has been suggested that merging plays an important role in the formation and the evolution of early-type galaxies. Optical-NIR color gradients of early-type galaxies in high density environments are found to be less steep than those in low density environment, hinting frequent merger activities in early-type galaxies in high density environment. In order to confirm if the flat color gradient is the result of dry merger, we decided to look deeply to find merging features and get their relation with color gradient. We selected samples which show extreme values of optical-NIR color gradients based on the data of previous study, and observed them at Maidanak observatory 1.5m telescope with long exposure. After masking out overlaid sources, our analysis reveals that these galaxies do not have extreme color gradient values. High degree sky flat technique was used during observation to aid discovery of faint, extended features. However, flatness of detector (SNUCAM) was good enough, so we could not see any marked improvement in image quality compared to those using normal sky flats. Additionally we noticed a feature that looks like merging tidal tail in the CFHT archival image, but this does not show up on the image we obtained. This demonstrates that flatness and correct sky estimation is very important when we look for faint merging features. In future we plan to enlarge the number of the sample.

• Advances in Energy Research
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• v.6 no.2
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.10C
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• pp.1402-1413
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• 2004

In non-rigid image registration, the Jacobian determinant of the estimated deformation should be positive everywhere since physical deformations are always invertible. We propose a constrained optimization technique at ensures the positiveness of Jacobian determinant for cubic B-spline based deformation. We derived sufficient conditions for positive Jacobian determinant by bounding the differences of consecutive coefficients. The parameter set that satisfies the conditions is convex; it is the intersection of simple half spaces. We solve the optimization problem using a gradient projection method with Dykstra's cyclic projection algorithm. Analytical results, simulations and experimental results with inhale/exhale CT images with comparison to other methods are presented.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.240-241
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• 2007

In general, high temperature superconducting coated conductors have intermediary buffers layer consisting of seed, diffusion barrier and cap layers. Simplification of the oxide materials buffer architecture in the fabrication of high temperature superconducting coated conductors is required because the deposition of multi-layers buffer architecture leads to a longer manufacturing time and a higher cost process of coated conductors. Thus, single buffer layer deposition seems to be important for practical coated conductor manufacturing process. In this study, a single gradient layered buffer deposition process of YZO for low cost coated conductors has been tried using DC reactive sputtering technique. About several thick YZO gradient single buffer layers deposited by DC co-sputtering process were found to act as a diffusion layer.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.178-185
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• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• Structural Engineering and Mechanics
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• v.86 no.6
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• pp.731-738
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• 2023

This article presents an analytical approach to explore the bending behaviour of of two-dimensional (2D) functionally graded (FG) nanobeams based on a two-variable higher-order shear deformation theory and nonlocal strain gradient theory. The kinematic relations are proposed according to novel trigonometric functions. The material gradation and material properties are varied along the longitudinal and the transversal directions. The equilibrium equations are obtained by using the virtual work principle and solved by applying Navier's technique. A comparative evaluation of results against predictions from literature demonstrates the accuracy of the proposed analytical model. Moreover, a detailed parametric analysis checks for the sensitivity of the bending and stresses response of (2D) FG nanobeams to nonlocal length scale, strain gradient microstructure scale, material distribution and geometry.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.901-911
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• 2009

In this paper, we propose a construction technique of two-level composite gradient map to automatically track a face with large movement in successive frames. Our method is composed of three main steps. First, the gradient maps with two-level resolution are generated for fast convergence of active contour. Second, to recognize the variations of face between successive frames and remove the neighbor background, weighted composite gradient map is generated by combining the composite gradient map and difference mask of previous and current frames. Third, to prevent active contour from converging local minima, the energy slope is generated by using closing operation. In addition, the fast closing operation is proposed to accelerate the processing time of closing operation. For performance evaluation, we compare our method with previous active contour model-based face tracking methods using a visual inspection, robustness test and processing time. Experimental results show that our method can effectively track the face with large movement and robustly converge to the optimal position even in frames with complicated background.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.303-308
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• 2010

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.695-699
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• 2009

In this paper, we propose an automatic prostate segmentation technique using image intensity and gradient information. Our method is composed of four steps. First, rays at regular intervals are generated. To minimize the effect of noise, the start and end positions of the ray are calculated. Second, the profiles on each ray are sorted based on the gradient. And priorities are applied to the sorted gradient in the profile. Third, boundary points are extracted by using gradient priority and intensity distribution. Finally, to reduce the error, the extracted boundary points are corrected by using B-spline interpolation. For accuracy evaluation, the average distance differences and overlapping region ratio between results of manual and automatic segmentations are calculated. As the experimental results, the average distance difference error and standard deviation were 1.09mm $\pm0.20mm$. And the overlapping region ratio was 92%.