• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.41-49
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• 2005

We have developed a random heterogeneous velocity model with bimodal distribution in methane hydrate-bearing Bones. The P-wave well-log data have a von Karman type autocorrelation function and non-Gaussian distribution. The velocity histogram has two peaks separated by several hundred metres per second. A random heterogeneous medium with bimodal distribution is generated by mapping of a medium with a Gaussian probability distribution, yielded by the normal spectral-based generation method. By using an ellipsoidal autocorrelation function, the random medium also incorporates anisotropy of autocorrelation lengths. A simulated P-wave velocity log reproduces well the features of the field data. This model is applied to two simulations of elastic wane propagation. Synthetic reflection sections with source signals in two different frequency bands imply that the velocity fluctuation of the random model with bimodal distribution causes the frequency dependence of the Bottom Simulating Reflector (BSR) by affecting wave field scattering. A synthetic cross-well section suggests that the strong attenuation observed in field data might be caused by the extrinsic attenuation in scattering. We conclude that random heterogeneity with bimodal distribution is a key issue in modelling hydrate-bearing Bones, and that it can explain the frequency dependence and scattering observed in seismic sections in such areas.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.177-185
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• 2009

Cluster analysis or clustering is a kind of unsupervised learning method in which a set of data points is divided into a given number of homogeneous groups. Fuzzy clustering method, one of the most popular clustering method, allows a point to belong to all the clusters with different degrees, so produces more intuitive and natural clusters than hard clustering method does. Even more some of fuzzy clustering variants have noise-immunity. In this paper, we improved the Possibilistic Fuzzy C-Means (PFCM), which generates a membership matrix as well as a typicality matrix, using Gath-Geva (GG) method. The proposed method has a focus on the boundaries of clusters, which is different from most of the other methods having a focus on the centers of clusters. The generated membership values are suitable for the classification-type applications. As the typicality values generated from the algorithm have a similar distribution with the values of density function of Gaussian distribution, it is useful for Gaussian-type density estimation. Even more GG method can handle the clusters having different numbers of data points, which the other well-known method by Gustafson and Kessel can not. All of these points are obvious in the experimental results.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.3
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• pp.205-214
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• 2015

Rapid urbanization and industrialization have caused increased demand for underground structures such as cable, and other utility tunnels. Recently, it has become very difficult to construct new underground structures in downtown areas because of civil complaints, and engineering problems related to insufficient information about existing underground structures, cable tunnels in particular. This lack of information about the location and direction-of-travel of cable tunnels is causing many problems. To solve these problems, this study was focused on the use of geophysical exploration of the ground in a way that is theoretically, different from previous electrical resistivity surveys. An electric field analysis was performed on the ground with cable tunnels using Gauss' law and the Laplace equation. The electrical resistivity equation, which is a function of the cable tunnel direction, the cable tunnel location, and the electrical conductivity of the cable tunnel, can be obtained through electrical field analysis. A field test was performed for the verification of this theoretical approach. A field test results provided meaningful data.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

• The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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• v.6 no.3
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• pp.36-54
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• 1995

The error performance of digital radio signals (i.e., M-ary PSK signal, DQPSK signal, MSK signal, GMSK signal) interfered by impulsive noise and electromagnetic interference (EMI) is analyzed and discussed. In analysis at first, the error rate equations have been derived in an electromagnetic interference plus impulsive noise environment. And then, the error performance has been evaluated and shown in figures as a function of carrier-to-noise ratio, carrier-to-interference ratio, impu- lsive index, gaussian noise to impulsive noise power ratio, and interference index to measure the amount of error degradation in digital radio signals. From the obtained results we have known that in the presence of m-distributed tone interference plus inpulsive noise, the more significant the electromagnetic interference amplitude varies, the more significant performance degradation is produced. The listing the digital radio signals from the most degraded to the least is that DQPSK, GMSK, QPSK and MSK signal. In the constant amplitude tone interference plus impulsive noise environment, the effect of in- terference nearly disappears over about 20dB in CIR. The effect of constant tone interference on error rate performance is reduced more remarkably in the region from 10dB to 15dB in CIR. In both enviroments of m-distributed tone interference and constant amplitude tone interference, the more electromagnetic interference amplitude varies and CIR increases, the more error perfor- mance is improved. But it is found out that the performance can not be improved significantly even the electromagnetic interference becomes weak. This describes that the impulsive noise affects dominantly to the performance degradation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2709-2714
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• 2014

This paper has analyzed the change of threshold voltage and conduction path for the ratio of top and bottom gate oxide thickness of asymmetric double gate MOSFET. The asymmetric double gate MOSFET has the advantage that the factor to be able to control the current in the subthreshold region increases. The analytical potential distribution is derived from Poisson's equation to analyze the threshold voltage and conduction path for the ratio of top and bottom gate oxide thickness. The Gaussian distribution function is used as charge distribution. This analytical potential distribution is used to derive off-current and subthreshold swing. By observing the results of threshold voltage and conduction path with parameters of bottom gate voltage, channel length and thickness, projected range and standard projected deviation, the threshold voltage greatly changed for the ratio of top and bottom gate oxide thickness. The threshold voltage changed for the ratio of channel length and thickness, not the absolute values of those, and it increased when conduction path moved toward top gate. The threshold voltage and conduction path changed more greatly for projected range than standard projected deviation.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.4
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• pp.413-420
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• 1994

Dynamics of the river plume is a very complicated non-linear problem with the free boundary changing in time and space. Mixing with the ambient water through the boundary makes the problem more complicated. In this paper we reduced 3-dimensional problem into 1-dimensional one by using the integral analysis method. Basic equations have been integrated over the lateral and vertical variations. For these integrations we adopted the well-established assumption that the flow-axis component of plume velocity and the density difference of the plume with the ambient water have Gaussian distributions in directions which are perpendicular to the flow-axis of the plume. We also used the result of our previous study on the lateral spreading velocity of the plume derived under the same assumption. And entrainment was included as a mixing process. The resultant 1-dimensional equations were solved by Runge-Kutta numerical method. Consequently, comparatively easy method of numerical analysis is presented for the 3-dimensional river plume. The method can also be used for the analysis of the thermal plume of cooling water of power plants.