• Journal of radiological science and technology
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• v.16 no.2
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• pp.19-26
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• 1993

Magnetic Resonance Image represents three-dimensional diagnostic imaging technique using both nuclear magnetic resonance phenomenon and computer. Compared with computed tomography (CT), MRI have advantages harmless to patient's body, three-dimensional image with high resolution and disadvantages long data acquisition time because of long T1 relaxation time, relatively low signal to noise ratio, high cost of setting, also. As physiologic motion of tissue results in motion ghost in MRI, high 2.0Tesla make improve low signal to noise ratio. This study have aim to improve image quality with controling motion ghost of tissue. Supposing a moving pixel in constant frequency, one pixel make two ghosts which are same size and different anti-phase. So, this study will show adjust parameter on locational control of motion ghost. Author made moving phantom replaced by respiratory movement of human, researched change of motion frequency, FOV by location shift, and them decided optimal FOV (field of view). The results are as follows: 1. The frequency content of the motion determines how far the image always appear in phase-encoding direction, the morphology of the ghost image is characteristic of the direction of the motion and its amplitude. 2. Double FOV of fixed signal object for locational control of motion ghost is recommended. Decreasement of spatial resolution by increasing FOV can compensate on increasing of matrix in spite of scan time increasement.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.479-490
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• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.11-21
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• 2021

The causes of noise generation according to the classification of indoor spaces are very diverse. Individual happiness is infringed by this noise. In this paper, We tried to visualize the spatial sound characteristics of public places using sound color to express them so that anyone can sympathize. The noise inside a conference room of a medical device company was measured for 100 minutes, and the frequency band was divided into three different types of existing sound pressure expression units. Because the size of the noise is expressed differently depending on the situation, There are cases where there is a difference of opinion between the measurer and the researcher. This noise measurement experiment was conducted, and the sound color was applied to classify it on a log scale considering auditory characteristics. As a result of comparing this with the result expression for different loudness expression units, A specific table in different units yielded almost similar results. In addition, the sound source section for 100 minutes was divided into three analysis sections, the analysis sections were different, and the size of the energy ratio for each analysis section was divided in the form of an envelope. The characteristics of the low-frequency region of the space have a high energy ratio, and the decrease in the energy ratio according to the increase in frequency is constant and regular. You can see that conversations are possible.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.9-18
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• 2011

In this study the optimal volume for non-point source control retention is estimated considering spatio-temporal variation of land surface. The 3-parameter mixed exponential probability density function is used to represent the statistical properties of rainfall events, and NRCS-CN method is applied as rainfall-runoff transformation. The catchment drainage area is divided into individual $30m{\times}30m$ cells, and runoff curve number is estimated at each cell. Using the derived probability density function theory, the stormwater probability density function at each cell is derived from the rainfall probability density function and NRCS-CN rainfall-runoff transformation. Considering the antecedent soil moisture condition at each cell and the spatial variation of CN value at the whole catchment drainage area, the ensemble stormwater capture curve is established to estimate the optimal volume for an non-point source control retention. The comparison between spatio-temporally varied land surface and constant land surface is presented as a case study for a urban drainage area.

• Journal of IKEEE
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• v.25 no.4
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• pp.650-663
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• 2021

Subsurface topology estimation is an important factor in the geophysical survey. Electrical impedance tomography is one of the popular methods used for subsurface imaging. The EIT inverse problem is highly nonlinear and ill-posed; therefore, reconstructed conductivity distribution suffers from low spatial resolution. The subsurface region can be approximated as piece-wise separate regions with constant conductivity in each region; therefore, the conductivity estimation problem is transformed to estimate the shape and location of the layer boundary interface. Each layer interface boundary is treated as an open boundary that is described using front points. The subsurface domain contains multi-layers with very complex configurations, and, in such situations, conventional methods such as the modified Newton Raphson method fail to provide the desired solution. Therefore, in this work, we have implemented a 7-layer artificial neural network (ANN) as an inverse problem algorithm to estimate the front points that describe the multi-layer interface boundaries. An ANN model consisting of input, output, and five fully connected hidden layers are trained for interlayer boundary reconstruction using training data that consists of pairs of voltage measurements of the subsurface domain with three-layer configuration and the corresponding front points of interface boundaries. The results from the proposed ANN model are compared with the gravitational search algorithm (GSA) for interlayer boundary estimation, and the results show that ANN is successful in estimating the layer boundaries with good accuracy.

• Annual Conference of KIPS
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• 2010.11a
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• pp.613-616
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• 2010

본 논문에서는 새로운 움직임 추정(motion estimation, ME) 방식을 사용한 프레임 비율 변환(frame rate conversion, FRC) 기법에 대해 제안한다. 기존의 프레임 비율 변환을 위한 움직임 추정 방식은 영상 압축에서 사용되고 있는 SAD를 사용하여 블록(block) 단위로 움직임 벡터를 추정하는 방식에 기초를 두고 있다. 그러나 잔여 신호(residual signal)를 저장하는 영상 압축과 달리, 잘못된 움직임 추정은 합성된 출력 영상에서 심각한 품질 저하를 가져올 수 있다. 이를 보완하기 위해 움직임 개선(motion refinement, MR)이 사용되고 있지만, 근본적인 해결을 위해서는 정확한 움직임 추정 알고리즘 사용이 필요하다. 특히 SAD를 통한 움직임 추정은 고르지 못한 움직임 벡터장(motion vector field, MVF)을 형성할 수 있으며, 종래의 연구에서 이를 해결하기 위해 SAD(sum of absolute difference)에 벡터의 공간제약(spatial constraint) 항목을 추가하여 비교적 고른 움직임 벡터장을 형성하는 방식이 제시되었다. SAD와 공간 제약 항목의 반영 비율에 따라 움직임 벡터의 중요성과 움직임 벡터장의 일관성이 서로 상충하는데, 기존의 방식은 이 비율을 일정한 상수(constant)값을 사용하고 있으며, 이러한 방식은 이미지의 특성에 따라 결과가 달라진다. 본 논문에서는 SAD와 공간 제약 항목 사이의 반영 비율을 이미지의 특성에 적응하는 방식을 사용하는 움직임 예측을 제시하고, 수행한 결과를 기존의 방식에 의한 결과와 비교하였다.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.47.4-48
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• 2021

Gravitationally lensed Type Ia supernovae may be the next frontier in cosmic probes, able to deliver independent constraints on dark energy, spatial curvature, and the Hubble constant. Measurements of time delays between the multiple images become more incisive due to the standardized candle nature of the source, monitoring for months rather than years, and partial immunity to microlensing. While currently extremely rare, hundreds of such systems should be detected by upcoming time-domain surveys. Others will have the images spatially unresolved, with the observed lightcurve a superposition of time delayed image fluxes. We investigate whether unresolved images can be recognized as lensed sources given only lightcurve information and whether time delays can be extracted robustly. We develop a method that we show can identify these systems for the case of lensed Type Ia supernovae with two images and time delays exceeding ten days. When tested on such an ensemble the method achieves a false positive rate of ≲5%, and measures the time delays with the completeness of ≳93% and with a bias of ≲0.5% for time delay ≳10 days. Since the method does not assume a template of any particular type of SN, the method has the potential to work on other types of lensed SNe systems and possibly on other transients.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.77-86
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• 1996

In this paper we deal with the problem of recovering 3-D motion and structure from a time-varying 2-D velocity vector field. A great deal has been done on this topic, most of which has concentrated on finding necessary and sufficient conditions for there to be a unique 3-D solution corresponding to a given 2-D motion. While previous work provides useful theoretical insight, in most situations the known algorithms have turned out to be too sensitive to be of much practical use. It appears that any robust algorithm must improve the 3-D solutions over time. As a step toward such algorithm, we present a method for recovering 3-D motion and structure from a given time-varying 2-D velocity vector field. The surface of the object in the scene is assumed to be locally planar. It is also assumed that 3-D velocity vectors are piecewise constant over three consecutive frames (or two snapshots of flow field). Our formulation relates 3-D motion and object geometry with the optical flow vector as well as its spatial and temporal derivatives. The linearization parameters, or equivalently, the first-order flow approximation (in space and time) is sufficient to recover rigid body motion and local surface structure from the local instantaneous flow field. We also demonstrate, through a sensitivity analysis carried out for synthetic and natural motions in space, that 3-D motion can be recovered reliably.

• Steel and Composite Structures
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• v.50 no.1
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• pp.25-43
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• 2024

In this article, a mathematical model is developed to predict the dynamic behavior of bio-inspired composite beam with helicoidal orientation scheme under variable axial load using a unified higher order shear deformation beam theory. The geometrical kinematic relations of displacements are portrayed with higher parabolic shear deformation beam theory. Constitutive equation of composite beam is proposed based on plane stress problem. The variable axial load is distributed through the axial direction by constant, linear, and parabolic functions. The equations of motion and associated boundary conditions are derived in detail by Hamilton's principle. Using the differential quadrature method (DQM), the governing equations, which are integro-differential equations are discretized in spatial direction, then they are transformed into linear eigenvalue problems. The proposed model is verified with previous works available in literatures. Parametric analyses are developed to present the influence of axial load type, orthotropic ratio, slenderness ratio, lamination scheme, and boundary conditions on the natural frequencies of composite beam structures. The present enhanced model can be used especially in designing spacecrafts, naval, automotive, helicopter, the wind turbine, musical instruments, and civil structures subjected to the variable axial loads.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.349-363
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• 2024

Compositional data refers to data where the sum of the values of the components is a constant, hence the sample space is defined as a simplex making it impossible to apply statistical methods developed in the usual Euclidean vector space. A natural approach to overcome this restriction is to consider an appropriate transformation which moves the sample space onto the Euclidean space, and log-ratio typed transformations, such as the additive log-ratio (ALR), the centered log-ratio (CLR) and the isometric log-ratio (ILR) transformations, have been mostly conducted. However, in scenarios with sparsity, where certain components take on exact zero values, these log-ratio type transformations may not be effective. In this work, we mainly suggest an alternative transformation, that is the square-root transformation which moves the original sample space onto the directional space. We compare the square-root transformation with the log-ratio typed transformation by the simulation study and the real data example. In the real data example, we applied both types of transformations to the USG% data obtained from NBA, and used a density based clustering method, DBSCAN (density-based spatial clustering of applications with noise), to show the result.