• 한국측량학회지
• /
• 제34권6호
• /
• pp.597-607
• /
• 2016

The goal of this study was to analyze landslide susceptibility using two different models and compare the results. For this purpose, a landslide inventory map was produced from a field survey, and the inventory was divided into two groups for training and validation, respectively. Sixteen landslide conditioning factors were considered. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the FR (Frequency Ratio) and EBF (Evidential Belief Function) models. The LSI (Landslide Susceptibility Index) maps that were produced were validated using the ROC (Relative Operating Characteristics) curve and the SCAI (Seed Cell Area Index). The AUC (Area under the ROC Curve) values of the FR and EBF LSI maps were 80.6% and 79.5%, with prediction accuracies of 72.7% and 71.8%, respectively. Additionally, in the low and very low susceptibility zones, the FR LSI map had higher SCAI values compared to the EBF LSI map, as high as 0.47%p. These results indicate that both models were reasonably accurate, however that the FR LSI map had a slightly higher accuracy for landslide susceptibility mapping in the study area.

• 한국해양공학회지
• /
• 제35권3호
• /
• pp.183-190
• /
• 2021

Tomographic particle image velocimetry (PIV) is a widely used method that measures a three-dimensional (3D) flow field by reconstructing camera images into voxel images. In 3D measurements, the setting and calibration of the camera's mapping function significantly impact the obtained results. In this study, a camera self-calibration technique is applied to tomographic PIV to reduce the occurrence of errors arising from such functions. The measured 3D particles are superimposed on the image to create a disparity map. Camera self-calibration is performed by reflecting the error of the disparity map to the center value of the particles. Vortex ring synthetic images are generated and the developed algorithm is applied. The optimal result is obtained by applying self-calibration once when the center error is less than 1 pixel and by applying self-calibration 2-3 times when it was more than 1 pixel; the maximum recovery ratio is 96%. Further self-correlation did not improve the results. The algorithm is evaluated by performing an actual rotational flow experiment, and the optimal result was obtained when self-calibration was applied once, as shown in the virtual image result. Therefore, the developed algorithm is expected to be utilized for the performance improvement of 3D flow measurements.

• Investigative Magnetic Resonance Imaging
• /
• 제25권4호
• /
• pp.300-312
• /
• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• Journal of Mechanical Science and Technology
• /
• 제17권10호
• /
• pp.1496-1506
• /
• 2003

The SIMP (solid isotropic material with penalization) approach is perhaps the most popular density variable relaxation method in topology optimization. This method has been very successful in many applications, but the optimization solution convergence can be improved when new variables, not the direct density variables, are used as the design variables. In this work, we newly propose S-shape functions mapping the original density variables nonlinearly to new design variables. The main role of S-shape function is to push intermediate densities to either lower or upper bounds. In particular, this method works well with nonlinear mathematical programming methods. A method of feasible directions is chosen as a nonlinear mathematical programming method in order to show the effects of the S-shape scaling function on the solution convergence.

• 대한기계학회논문집A
• /
• 제23권6호
• /
• pp.1018-1025
• /
• 1999

For the effective analysis of two dimensional plane problems with geometrical discontinuities, singular finite element has been proposed. The element matrix equation was formulated on the basis of hybrid variational principle and Trefftz function sets derived consistently from the complex theory of plane elasticity by introducing a conformal mapping function. In order to suggest the accuracy characteristics of the proposed singular finite element, typical plane problems were analyzed and these results were compared with exact solutions. The singular finite element gives the comparatively exact values of stress concentration factors or stress intensity factors and can be effectively used for the analysis of mechanical structures containing various geometrical discontinuities.

• 대한수학회보
• /
• 제42권4호
• /
• pp.757-776
• /
• 2005

In this paper we establish the stability of a Jensen type functional equation, namely f(xy) - f($xy^{-1}$) = 2f(y), on some classes of groups. We prove that any group A can be embedded into some group G such that the Jensen type functional equation is stable on G. We also prove that the Jensen type functional equation is stable on any metabelian group, GL(n, $\mathbb{C}$), SL(n, $\mathbb{C}$), and T(n, $\mathbb{C}$).

• 융복합기술연구소 논문집
• /
• 제12권1호
• /
• pp.1-5
• /
• 2022

This paper describes an algorithm for generating a terrain using a noise function and a height map as one of the procedural terrain generation methods. The polygon mesh data structure to represent the generated terrain concisely and render it is also described. The Perlin noise function is used as the noise technique for terrain mesh, and the height data of the terrain is generated by combining the four noise waves. In addition, the terrain height information can be also obtained from actual image data taken from the satellite. The algorithm presented in this paper generates the geometry part of the polygon topography from the height data obtained, and generated a material for texture mapping with two textures, that is, a diffuse texture and a normal texture. The validity of the terrain method proposed in this paper is verified through application examples, and its possibility can be confirmed through performance verification.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2023년도 학술발표회
• /
• pp.160-160
• /
• 2023

Generally, Global Climate Models (GCM) cannot be used directly due to their inherent error arising from over or under-estimation of climate variables compared to the observed data. Several bias correction methods have been devised to solve this problem. Most of the traditional bias correction methods are one dimensional as they bias correct the climate variables separately. One such method is the Quantile Mapping method which builds a transfer function based on the statistical differences between the GCM and observed variables. Laux et al. introduced a copula-based method that bias corrects simulated climate data by employing not one but two different climate variables simultaneously and essentially extends the traditional one dimensional method into two dimensions. but it has some limitations. This study uses objective functions to address specifically, the limitations of Laux's methods on the Quantile Mapping method. The objective functions used were the observed rank correlation function, the observed moment function and the observed likelihood function. To illustrate the performance of this method, it is applied to ten GCMs for 20 stations in South Korea. The marginal distributions used were the Weibull, Gamma, Lognormal, Logistic and the Gumbel distributions. The tested copula family include most Archimedean copula families. Five performance metrics are used to evaluate the efficiency of this method, the Mean Square Error, Root Mean Square Error, Kolmogorov-Smirnov test, Percent Bias, Nash-Sutcliffe Efficiency and the Kullback Leibler Divergence. The results showed a significant improvement of Laux's method especially when maximizing the observed rank correlation function and when maximizing a combination of the observed rank correlation and observed moments functions for all GCMs in the validation period.

• 한국통신학회논문지
• /
• 제30권3C호
• /
• pp.125-132
• /
• 2005

본 논문에서는 실시간 시선 식별을 위한 능동적 적외선 조명을 기반으로 한 컴퓨터 비전 시스템을 제안하고자 한다. 현존하는 대다수의 시선 식별 방법은 고정된 얼굴에서만 원활하게 동작하고 개개인에 대한 교정 절차를 필요로 한다. 그러므로 본 논문에서는 교정 작업 없이 얼굴 움직임이 있는 경우에도 견실하고 정확하게 동작하는 시선 식별 시스템을 제안하고 있다. 제안된 시스템은 추출된 변수로부터 시선을 스크린 좌표로 매핑하기 위해 GRNN을 이용하였다. 동공을 추적하여 GRNN을 사용함으로서, 시선 매핑에 분석적 기능이나 얼굴 움직임에 대한 계산이 필요 없을 뿐 아니라 다른 개개인에 대하여 학습과정에서 매핑 기능을 일반화 할 수 있었다. 시선 예측 정확도를 개선하고자 계층적 식별을 제안함으로써 오분류를 줄일 수 있었고, 공간 시선 해상도는 스크린에서 1m 정도 떨어진 거리에서 수평으로 10cm, 수직으로 약 13cm, 즉 수평으로 8도 수직으로 5도 이었다. 실험 결과, 재식별 하였을 경우 1차 시선 식별시 84%보다 약 9% 정도 정확성이 향상되어 93%를 나타냄으로써 제안된 시스템의 유효성을 증명하였다.

• 한국정보통신학회논문지
• /
• 제9권2호
• /
• pp.322-329
• /
• 2005

본 논문에서는 실시간 시선 식별을 위한 능동적 적외선 조명을 기반으로 한 컴퓨터 비전 시스템을 제안하고자 한다. 현존하는 대다수의 시선 식별 방법은 고정된 얼굴에서만 원활하게 동작하고 개개인에 대한 교정 절차를 필요로 한다. 그러므로 본 논문에서는 교정 작업 없이 얼굴 움직임이 있는 경우에도 견실하고 정화하게 동작하는 시선 식별 시스템을 제안하고 있다. 제안된 시스템은 추출된 변수로부터 시선을 스크린 좌표로 매핑하기 위해 GRNN을 이용하였다. GRNN을 사용함으로서, 시선 매핑에 분석적 기능이나 얼굴 움직임에 대한 계산이 필요 없을 뿐 아니라 다른 개개인에 대하여 학습과정에서 매핑 기능을 일반화 할 수 있었다. 시선 예측 정확도를 개선하고자 계층적 식별을 제안함으로써 오분류를 줄일 수 있었고, 공간 시선 해상도는 스크린에서 Im 정도 떨어진 거리에서 수평으로 10cm, 수직으로 약 13cm, 즉 수평으로 8도 수직으로 5도 이었다 실험 결과, 재식별 하였을 경우 1차 시선 식별시 84$\%$보다 약 9$\%$ 정도 정확성이 향상되어 93%를 나타냄으로써 제안된 시스템의 유효성을 증명하였다.