• Composites Research
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• v.18 no.4
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• pp.44-51
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• 2005

In this paper, we developed the direct numerical simulation(DNS) model considering the geometry of yams which consist of 3D orthogonal woven composite materials, and using this model, the dynamic behavior of under transverse low-velocity impact has been studied. To build up the micromechanical model considering tow spacing and waviness, an accurate unit structure is presented and used in building structural plate model based on DNS. For comparison, DNS results are compared with those of the micromechanical approach which is based on the global equivalent material properties obtained by DNS static numerical tests. The effects with yarn geometrical irregularities which are difficult to consider in a macroscopic approach are also investigated by the DNS model. Finally, the multiscale model based on the DNS concepts is developed to enhance efficiency of analysis with real sized numerical specimen and macro/micro characteristics are presented.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.66-75
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• 2010

In Augmented Reality, it needs restoration and tracking of a real-time scene structure for the augmented 3D model from input video or images. Most of the previous approaches construct accurate 3D models in advance and try to fit them in real-time. However, it is difficult to measure 3D model accurately and requires long pre-processing time to construct exact 3D model specifically. In this research, we suggest a real-time scene structure analysis method for the wide indoor mobile augmented reality, using only generic models without exact pre-constructed models. Our approach reduces cost and time by removing exact modeling process and demonstrates the method for restoration and tracking of the indoor repetitive scene structure such as corridors and stairways in different scales and details.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.608-620
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• 2021

Recent deep learning-based face super-resolution (FSR) works showed significant performances by utilizing facial prior knowledge such as facial landmark and dictionary that reflects structural or semantic characteristics of the human face. However, most of these methods require additional processing time and memory. To solve this issue, this paper propose an efficient FSR models using knowledge distillation techniques. The intermediate features of teacher network which contains dictionary information based on major face regions are transferred to the student through adversarial multi-scale features distillation. Experimental results show that the proposed model is superior to other SR methods, and its effectiveness compare to teacher model.

• 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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.5
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• pp.461-467
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• 2012

An experimental study was carried out to measure frictional pressure drop in flow boiling to deionized water in a microchannel having a hydraulic diameter of $500{\mu}m$. Tests were performed in the ranges of heat fluxes from 100 to $400kW/m^2$, vapor qualities from 0 to 0.2 and mass fluxes of 200, 400 and $600kg/m^2s$. The frictional pressure drop during flow boiling is predicted by using two models; the homogeneous model that assumes equal phase velocity and the separate flow model that allows a slip velocity between two phases. From the experimental results, it is found that the two phase multiplier decreases with an increase in mass flux. Measured data of pressure drop are compared to a few available correlations proposed for macroscale and mini/microscale. The homogeneous model well predicted frictional pressure drop within MAE of 29.4 % for the test conditions considered in this work.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.29-37
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• 2021

In this paper, we propose a deep learning-based retinal vessel segmentation model for handling multi-scale information of fundus images. we integrate the selective kernel convolution into U-Net-based convolutional neural network. The proposed model extracts and segment features information with various shapes and sizes of retinal blood vessels, which is important information for diagnosing eye-related diseases from fundus images. The proposed model consists of standard convolutions and selective kernel convolutions. While the standard convolutional layer extracts information through the same size kernel size, The selective kernel convolution extracts information from branches with various kernel sizes and combines them by adaptively adjusting them through split-attention. To evaluate the performance of the proposed model, we used the DRIVE and CHASE DB1 datasets and the proposed model showed F1 score of 82.91% and 81.71% on both datasets respectively, confirming that the proposed model is effective in segmenting retinal blood vessels.

• Composites Research
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• v.19 no.1
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• pp.1-8
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• 2006

Prediction of damage caused by low-velocity impact in laminated composite plate is an important problem faced by designers using composites. Not only the inplane stresses but also the interlaminar normal and shear stresses playa role in estimating the damage caused. But it is well known that the conventional approach based on the homogenization has the limit in description of damage. The work reported here is an effort in getting better predictions of dynamic behavior and damage in composite plate using DNS approach. In the DNS model, we discretize the composite plates through separate modeling of fiber and matrix for the local microscopic analysis. In the view of microscopic mechanics with DNS model, interlaminar stress behaviors in the inside of composite materials are investigated and compared with the results of the homogenized model which has been used in the conventional approach to impact analysis. Also the multiscale model based on DNS concept is developed in order to enhance the effectiveness of impact analysis, and we present the results of multiscale analysis considering micro and macro structures simultaneously.

• Composites Research
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• v.36 no.1
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• pp.16-24
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• 2023

This paper presents a multi-scale progressive fatigue damage model incorporating the model for interfacial debonding between fibers and matrix. The micromechanics model for the progressive interface debonding was adopted, which defined the four different interface phases: (1) perfectly bonded fibers; (2) mild imperfect interface; (3) severe imperfect interface; and (4) completely debonded fibers. As the number of cycles increases, the progressive transition from the perfectly bonded state to the completely debonded fiber state occurs. Eshelby's tensor for each imperfect state is calculated by the linear spring model for a damaged interface, and effective elastic properties are obtained using the multi-phase homogenization method. The fatigue damage evolution formulas for fiber, matrix and interface were proposed to demonstrate the fatigue behavior of CFRP laminates under cyclic loading. The material parameters for the fiber/matrix fatigue damage were characterized using the chaotic firefly algorithm. The model was implemented into the UMAT subroutine of ABAQUS, and successfully validated with flat-bar UD laminate specimens ([0]₈,[90]₈, [30]₁₆) of AS4/3501-6 graphite/epoxy composite.

• Journal of Korea Multimedia Society
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• v.15 no.1
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• pp.63-70
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• 2012

In this paper an extension to an original active shape model(ASM) for facial feature extraction is presented. The original ASM suffers from poor shape alignment by aligning the shape model to a new instant of the object in a given image using a simple similarity transformation. It exploits only informations such as scale, rotation and shift in horizontal and vertical directions, which does not cope effectively with the complex pose variation. To solve the problem, new shape alignment with 6 degrees of freedom is derived, which corresponds to an affine transformation. Another extension is to speed up the calculation of the Mahalanobis distance for 2-D profiles by trimming the profile covariance matrices. Extensive experiment is conducted with several images of varying poses to check the performance of the proposed method to segment the human faces.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.98-101
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• 2013

스마트폰과 스마트TV와 같은 다양한 콘텐츠 소비 단말들이 급속히 보급되는 최근의 IT환경에서는 콘텐츠 제작, 유통, 소비에 있어서 예전과는 다른 패러다임을 요구하게 되었다. 예를 들어 각기 다른 해상도, 연산량, 사용자환경 등의 다양한 조건들에 대한 고려가 중요한 문제로 부각되고 있다. 이를 해결하고자 단일 서비스 콘텐츠 패키지로 여러 단말들에게 적응적인 서비스를 제공하는 Scalable Application Description Language(SADL)에 기반한 스케일러블 어플리케이션 프레임워크가 제안되었다. 본 논문에서는 이러한 스케일러블 어플리케이션 프레임워크에서 적응형 서비스 콘텐츠를 쉽게 생산하고 패키징 할 수 있도록 이클립스 공개소스 플랫폼 기반의 저작도구를 제안 및 개발하였다. 개발한 저작도구는 유지 보수 및 확장성을 고려하여 모델-뷰-컨트롤러에 기반한 Graphical Editing Framework(GEF)를 이용했다. 제안하는 저작도구는 다양한 운영체제에서 작동하며, 유지보수 및 확장성이 우수하다. 또한 서비스 콘텐츠 개발을 위해 이클립스 플랫폼이 지원하는 다양한 플러그인 도구들을 그대로 활용 가능하다는 장점을 갖는다. 뿐만 아니라 본 저작도구는 기능의 추가적인 확장을 통해 홈 내 가상화 장치들을 위한 응용 프로그램 제작에도 활용할 수 있다.