• Journal of the Korean Society of Costume
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• v.51 no.2
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• pp.181-192
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• 2001

Men study the nature in two ways. Scientists and mathematicians inquire a branch of those two ways. Mathematical formulations are the tools and the expressions of their nature. Meanwhile, the other branch, the art, alms for different inquiry. Instead of formulating the nature, the artists create their masterpieces from their ultimate source, the Mother Nature. For thousands of years these two branches have grown together, influencing each others work. Some mathematicians find that formulation, are not enough to fully express the beauty of nature. It is believed that such a simple expression, formula, easily omits the careful details of nature. The nature is simply too chaotic to be shaped with a formula. Of those mathematicians, Mandelbrot, one of the first to realize this matter, introduced the world of fractal geometry. Fractals give new possibilities. It allows us not to limit ourselves to linear prospect, rather a whole new view of this chaotic beauty of the nature. A popular practice to understand fractals is in costume design. The artistic characteristic and organization mechanism is appalled to costumes. Meanwhile, another practice, rather aggressive, is using computer to create an image of fractals. This image is then used for motives to generate artistic expressions. Computer and paper ironing technique is used for fashion illustration in this research. The works were synthesized arid transformed from computer programs. To add more traditional painting touch to this work, Paper ironing technique was used. Since the of effect of this technique is so random, irregular, and unordered, it corresponds to fractal consideration. This thesis asserts an another prospect to fractal as a structural way of describing nature ailed fashion illustration, rather than restricting it to only mathematical theory.

• Tunnel and Underground Space
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• v.22 no.3
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• pp.196-204
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• 2012

Quantification of roughness plays an important role in modeling strength deformability and fluid flow behaviors of rock joints. A procedure was suggested to simulate joint roughness, and characteristics of the roughness was investigated in this study. Stationary fractional Brownian profiles with known input values of the fractal parameter and other profile properties were generated based on random midpoint displacement method. Also, a procedure to simulate three dimensional roughness surface was suggested using the random midpoint displacement method. Selected statistical roughness parameters were calculated for the generated self-affine profiles to investigate the attribute of roughness. Obtained results show that statistical parameters applied in this study were able to consider correlation structure and amplitude of the profiles. However, effect of data density should be tackled to use statistical parameters for roughness quantification.

• Journal of the Korean GEO-environmental Society
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• v.7 no.5
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• pp.79-87
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• 2006

This paper presents the numerical model developed to simulate the solute transport in rough and smooth single fractures. The roughness of these fractures is represented by using the fractal surface method. In this study, the 3D transport model, which is based on the random walk technique, is used to simulate the dispersion process of a solute which is represented by numerical particles. As the simulation results, it can be observed that the dispersion of solute in the fracture is significantly affected by the fracture roughness and particle size.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.365-372
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• 2000

he paper introduces evolutionary computing to Fractal Image Compression(FIC). In Fractal Image Compression(FIC) a partitioning of the image into ranges is required. As a solution to this problem there is a propose that evolution computation should be applied in image partitionings. Here ranges are connected sets of small square image blocks. Populations consist of $N_p$ configurations, each of which is a partitioning with a fractal code. In the evolution each configuration produces $\sigma$ children who inherit their parent partitionings except for two random neighboring ranges which are merged. From the offspring the best ones are selected for the next generation population based on a fitness criterion Collage Theorem. As the optimum image includes duplication in image data, it gets smaller in saving space more efficient in speed and more capable in image quality than any other technique in which other coding is used. Fractal Image Compression(FIC) using evolution computation in multimedia image processing applies to such fields as recovery of image and animation which needs a high-quality image and a high image-compression ratio.

• Journal of the Mineralogical Society of Korea
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• v.23 no.4
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• pp.367-375
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• 2010

Understanding the interactions between earth materials and fluids is essential for studying the diverse geological processes in the Earth's surface and interior. In order to better understand the interactions between earth materials and fluids, we explore the effect of specific surface area and porosity on structural parameters of pore structures. We obtained 3D pore structures, using random packing simulations of porous media composed of single sized spheres with varying the particle size and porosity, and then we analyzed configurational entropy for 2D cross sections of porous media and cube counting fractal dimension for 3D porous networks. The results of the configurational entropy analysis show that the entropy length decreases from 0.8 to 0.2 with increasing specific surface area from 2.4 to $8.3mm^2/mm^3$, and the maximum configurational entropy increases from 0.94 to 0.99 with increasing porosity from 0.33 to 0.46. On the basis of the strong correlation between the liquid volume fraction (i.e., porosity) and configurational entropy, we suggest that elastic properties and viscosity of mantle melts can be expressed using configurational entropy. The results of the cube counting fractal dimension analysis show that cube counting fractal dimension increases with increasing porosity at constant specific surface area, and increases from 2.65 to 2.98 with increasing specific surface area from 2.4 to $8.3mm^2/mm^3$. On the basis of the strong correlation among cube counting fractal dimension, specific surface area, and porosity, we suggest that seismic wave attenuation and structural disorder in fluid-rock-melt composites can be described using cube counting fractal dimension.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.897-899
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• 1997

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault does not make enough current to cause conventional protective devices. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. This paper describes an algorithm using back-propagation neural network for pattern recognition and detection of high impedance faults. Fractal dimensions are estimated for distinction between random noise and chaotic behavior in the power system. The fractal dimension of the line current is also used as a indication of the high impedance fault.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.379-388
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• 2000

Previous studies on high impedance faults assumed that the erratic behavior of fault current would be random. In this paper, we prove that the nature of the high impedance faults is indeed a deterministic chaos, not a random motion. Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents in order to evaluate the orbital instability peculiar to deterministic chaos dynamically, and fractal dimensions of fault currents, which represent geometrical self-similarity are calculated. In addition, qualitative analysis such as phase planes, Poincare maps obtained from fault currents indicate that the irregular behavior is described by strange attractor.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.545-549
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• 1999

Previous studies on high impedance faults assumed that the erratic behavior of fault current would be random. In this paper we prove that the nature of the high impedance faults is indeed a deterministic chaos not a random motion. Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents in order to evaluate the orbital instability peculiar to deterministic chaos dynamically and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. In addition qualitative analysis such a s phase planes Poincare maps obtained from fault currents indicate that the irregular behavior is described by strange attractor.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.899-914
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• 2003

It is generally accepted that self-similar (or fractal) processes may provide better models for teletraffic in modern telecommunication networks than Poisson Processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. Three generators of pseudo-random self-similar sequences, based on the FFT〔20〕, RMD〔12〕 and SRA methods〔5, 10〕, are compared and analysed in this paper. Properties of these generators were experimentally studied in the sense of their statistical accuracy and times required to produce sequences of a given (long) length. While all three generators show similar levels of accuracy of the output data (in the sense of relative accuracy of the Horst parameter), the RMD- and SRA-based generators appear to be much faster than the generator based on FFT. Our results also show that a robust method for comparative studies of self-similarity in pseudo-random sequences is needed.