• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3866-3873
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• 2000

In this paper, we investigates the long-term memory and the Correct answer rate of the foreign exchange data (Yen/Dollar) that is one of economic time series, There are many cases where two kinds of fractal dimensions exist in time series generated from dynamical systems such as AR models that are typical models having a short terrr memory, The sample interval separating from these two dimensions are denoted by kcrossover. Let the fractal dimension be $D_1$ in K < $k^{crossover}$,and $D_2$ in K > $k^{crossover}$ from the statistics mode. In usual, Statistic models have dimensions D1 and D2 such that $D_1$ < $D_2$ and $D_2\cong2$ But it showed a result contrary to this in the real time series such as NIKKEL The exchange data that is one of real time series have relation of $D_1$ > $D_2$ When the interval between data increases, the correlation between data increases, which is quite a peculiar phenomenon, We predict exchange data by neural networks, We confirm that $\beta$ obrained from prediction errors and D calculated from time series data precisely satisfy the relationship $\beta$ = 2-2D which is provided from a non-linear model having fractal dimension, And We identified that the difference of fractal dimension appeaed in the Correct answer rate.

• The Journal of Engineering Geology
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• v.20 no.2
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• pp.183-189
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• 2010

Fault gouge samples were collected from the fault cores of the boundary faults between the Cretaceous Basement and the Tertiary Waeup Basin. Fractal dimensions (D) were obtained by using survivor grains which were analysed from six thin sections of the gouges under the optical microscope. The elliptical survivor grains show a shape preferred orientation almost parallel to clay foliation in matrix, suggesting that it was formed by the rotation of the survivor grains in abundant fine-grained matrix during repeated fault slips. The size distributions of the survivor grains follow power-laws with fractal dimensions in the 2.40-3.02 range. D values of all samples but one are higher than a specific D value equal to 2.58 which predicts the self similarity of fragmentation process in constrained comminution model (Sammis et al., 1987), which indicates large fault slip and multiple faulting. Probably the higher D values than 2.58 mean the non-self-similar evolution of cataclastic rocks where fragmentation mechanism changed from constrained comminution to the grain abrasion accompanying selective fracture of larger grains.

• Journal of the Korean Society of Radiology
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• v.10 no.4
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• pp.263-270
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• 2016

This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, There applied to 24-bpp color image compression and image techniques. The result did not occur a loss in the image quality of the image when using the encoding method, such as almost to the color in the YIQ image compression rate and image quality, such as RGB images and showed good.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.895-907
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• 2006

In this study, in order to maximize the accuracy and efficiency of the existing interpolation method fractal methods are applied. Developed FEDISA model revives the irregularity of the real terrain with only a few information about base terrain, which can produce almost complete geographic information. The area of the model is set to $150m{\times}150m$, $300m{\times}300m$, $600m{\times}600m$, $1,200m{\times}1,200m$ to compare the real data with the data of the existing interpolation method and FEDISA model. By statistical verification of the results, the adaptability and efficiency of FEDISA model are investigated. It seems that FEDISA model will help a lot to obtain the terrain information about the changed terrain, such as the bottom of reservoirs and dams as well as large amount of destruction due to cutting and banking.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.426-429
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• 2011

광범위한 지형을 배경으로 하는 게임과 같은 실시간 시뮬레이션 시스템에서 사실적 표현을 높이는 중요한 요소 중 하나가 나무와 같은 자연물 표현이다. 하지만 시스템에 적합한 나무 모델을 매번 새롭게 제작하고 표현하는 일은 다소 어려움이 따른다. 본 연구는 이러한 문제를 해결하기 위하여 실시간 시스템에 적합한 다양하고 사실적인 나무를 모델링하는 방법을 설계한다. 이는 프랙탈 기반의 재귀적 계층 구조를 바탕으로 가지 성장의 자기조직화 처리를 결합하여 나무 성장 과정을 단순화시킴으로써 실시간 시스템에서 직관적이고 효율적으로 활용가능하게 한다. 또한 다양한 나무 모델을 자연스럽게 생성할 수 있도록 인터렉티브 제어 요소를 정의함은 물론 실시간 시스템 내 많은 수의 복잡한 나무 모델을 효율적으로 렌더링하기 위한 GPU를 기반으로 한 가지 표면에 대한 LOD 설정과 인스턴싱 방법을 추가하여 그 결과를 함께 보인다.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of the Korean Geotechnical Society
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• v.20 no.9
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• pp.47-56
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• 2004

A new fragmentation model, called the GRS (the generation model of weathered residual soils) model, was proposed in this study, This model could identify the formation of a residual soil. This model is based on the phenomena that as the soil was weathered more highly, soil particles were smaller and pores were more expanded simultaneously. The possibility of fragmentation, $P_F,$ which was based on the fractal theory, was introduced in this model. There were some fundamental notions in the GRS model that soil particles were generated as the rock is fragmented, and the fragmentation of the rock was performed step by step. The $P_F,$ of the rock was not constant at each fragmentation steps. As a result of application on the GRS model, there were more residue where $P_{Fi}s$ were small at any particle size. There was a S-shape of PSD curve at the concave shape of $P_{Fi},$ and the PSD curve goes to a gaped graded curve at the convex shape of $P_{Fi}.$ The shape of PSD curve was concave in the case of small $P_{Fi}s.$ The value of $P_{Fi}$ increased with the coefficient of uniformity $(C_u)$ and the fragmentation fractal dimension $(D_r),$ but had no relation with the coefficient of gradation $(C_C)$.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of the Mineralogical Society of Korea
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• v.22 no.4
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• pp.313-324
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• 2009

We explore the effect of particle shape and size on 3-dimensional (3D) network and pore structure of porous earth materials composed of glass beads and silica gel using NMR micro-imaging in order to gain better insights into relationship between structure and the corresponding hydrologic and seismological properties. The 3D micro-imaging data for the model porous networks show that the specific surface area, porosity, and permeability range from 2.5 to $9.6\;mm^2/mm^3$, from 0.21 to 0.38, and from 11.6 to 892.3 D (Darcy), respectively, which are typical values for unconsolidated sands. The relationships among specific surface area, porosity, and permeability of the porous media are relatively well explained with the Kozeny equation. Cube counting fractal dimension analysis shows that fractal dimension increases from ~2.5－2.6 to 3.0 with increasing specific surface area from 2.5 to $9.6\;mm^2/mm^3$, with the data also suggesting the effect of porosity. Specific surface area, porosity, permeability, and cube counting fractal dimension for the natural mongolian sandstone are $0.33\;mm^2/mm^3$, 0.017, 30.9 mD, and 1.59, respectively. The current results highlight that NMR micro-imaging, together with detailed statistical analyses can be useful to characterize 3D pore structures of various porous earth materials and be potentially effective in accounting for transport properties and seismic wave velocity and attenuation of diverse porous media in earth crust and interiors.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.49-60
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• 2024

Ground-penetrating radar (GPR) enables rapid data acquisition over extensive areas, but interpreting the obtained data requires specialized knowledge. Numerous studies have utilized numerical analysis methods to examine GPR signal characteristics under various conditions. To develop more realistic numerical models, the heterogeneous nature of the ground, which causes clutter, must be considered. Clutter refers to signals reflected by objects other than the target. The Peplinski material model and fractal techniques can simulate these heterogeneous characteristics, yet there is a shortage of research on the necessary input parameters. Moreover, methods for quantitatively evaluating the similarity between field and analytical data are not well established. In this study, we calculated the autocorrelation coefficient of field data and determined the correlation length using the autocorrelation function. The correlation length represented the temporal or spatial distance over which data exhibited similarity. By comparing the correlation length of field data with that of the numerical model incorporating fractal weights, we quantitatively evaluated a numerical model for heterogeneous ground. Consequently, the results of this study demonstrated a numerical modeling technique that reflected the clutter characteristics of the field through correlation length.