• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.322-332
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• 2007

We consider a deformation method suitable for fractal. In IFS fractal, the position of a point is characterized by its code as well as by its coordinates. Code has a meaning of address for fractal. If we move a point by changing its code, the resulting movement shows fractal behavior. We propose three deformation methods based on code information. For the deformation vector of a point in fractal, 1) we use the vector of a given vector field at the point obtained by code transformation, 2) we use the vector constructed by adding predefined displacement vectors according to the code information of the point. Both methods show a fractal-like character as well as an ordinary continuous deformation character. Also, 3) we can deform fern-fractal more naturally by restricting its deforming region using code form.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.5
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• pp.404-411
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• 2009

We introduce in this paper a new method for smooth foldover-free warping of images, based on the vector field deformation technique proposed by Von Funck et al. It allows users to specify the constraints in two different ways: positional constraints to constrain the position of a point in the image and gradient constraints to constrain the orientation and scaling of some parts of the image. From the user-specified constraints, it computes in the image domain a C1-continuous velocity vector field, along which each pixel progressively moves from its original position to the target. The target positions of the pixels are obtained by solving a set of partial derivative equations with the 4th order Runge-Kutta method. We show how our method can be useful for texture mapping with hard constraints. We start with an unconstrained planar embedding of a target mesh using a previously known method (Least Squares Conformal Map). Then, in order to obtain a texture map that satisfies the given constraints, we use the proposed warping method to align the features of the texture image with those on the unconstrained embedding. Compared to previous work, our method generates a smoother texture mapping, offers higher level of control for defining the constraints, and is simpler to implement.

• Transactions of Materials Processing
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• v.21 no.7
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• pp.441-446
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• 2012

A sequential coupled field analysis of electromagnetic free bulging was performed by using FEM. A 2D axi-symmetric electromagnetic model based on the magnetic vector potential is proposed for the calculation of magnetic field and Lorentz's forces. The Newmark integration method is used to calculate the transient dynamic plastic deformation of sheet during free bulging. In the finite element model, the effect of sheet deformation on the electromagnetic field analysis is taken into consideration. In order to confirm the sequential electromagnetic-mechanical coupling analysis, an experiment with an electromagnetic forming apparatus was conducted. The results showed that the final bulge height of the sheet predicted from the proposed method is in good agreement with experimentally measured height.

• Journal of Korean Society of Steel Construction
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• v.25 no.5
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• pp.497-507
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• 2013

This study investigated the tension-field action induced strip angle changes and deformed mode shapes of SPSW for high-rise structures subjected to lateral forces. Based on the numerical analysis 3, 9, 14 and 20 story structures, shear and flexural modes were identified by comparing the numerical analysis results to the predicted strength by theory. Shear deformation mode exhibited a constant angle in tension-field; whereas, flexural mode of the numerical results, differed from the tension-field action theory.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.64-74
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• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• Journal of the Korean Geotechnical Society
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• v.22 no.7
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• pp.5-12
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• 2006

In this study, 2D shallow tunnel model test using close range photogrammetric technique was conducted with aluminium rods simulating continuum granular material. Numerical analysis was also carried out in order to identify the behaviour of subsurface deformations caused by shallow tunnelling. Direction and magnitude of displacement vectors from the model test were identical to the result of numerical analysis based on the model data. In particular, it is shown that the vector direction was toward a point below the tunnel invert level. A narrow "chimney or tulip like" pattern of vertical displacement was confirmed by both the model test and numerical analysis. This behaviour is consistent with the field data. In addition to the qualitative comparison, the quantitative result of subsurface settlements according to 2D volume loss showed good agreement between the model test and numerical analysis. Therefore, close range photogrammetric technique applied in the model test may be used to validate the result from the continuum numerical analysis.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.411-417
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• 2021

The field that studies human-computer interaction is called HCI (Human-computer interaction). This field is an academic field that studies how humans and computers communicate with each other and recognize information. This study is a study on hand gesture recognition for human interaction. This study examines the problems of existing recognition methods and proposes an algorithm to improve the recognition rate. The hand region is extracted based on skin color information for the image containing the shape of the human hand, and the center of gravity profile is calculated using principal component analysis. I proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. We proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. The existing center of gravity profile has shown the result of incorrect hand gesture recognition for the deformation of the hand due to rotation, but in this study, the center of gravity profile is used and the point where the distance between the points of all contours and the center of gravity is the longest is the starting point. Thus, a robust algorithm was proposed by re-improving the center of gravity profile. No gloves or special markers attached to the sensor are used for hand gesture recognition, and a separate blue screen is not installed. For this result, find the feature vector at the nearest distance to solve the misrecognition, and obtain an appropriate threshold to distinguish between success and failure.