• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.5
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• pp.1005-1015
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• 2000

In this paper, the performance of Hybrid CDU MFSK system has been analyzed in the environment that narrowband Class-A impulsive noise include Gaussian noise in wireless communication channel and Nakagami fading. The performance of system improved to adopting both of technique MRC diversity and BCH channel coding. The results show that there is substantial degraded in Hybrid CDMA system performance by impulse index is stronger and the fading index is smaller. But also improvement can be obtained when BCH coding techniques are adopted. Additional MRC diversity techniques is more improvement than BCH coding techniques when degraded environment with the low fading index. But BCH coding techniques is more improvement than MRC diversity techniques when fading index has been increased. The Hybrid CDMA MFSK system is incomplete to voice communication standard BER$(10^{-3})$ in Rayleigh fading by independent each adopting techniques BCH coding and MRC diversity. But it's satisfy(improvement can be obtained) to voice communication standard BER in strong impulse noise(24dB over) by adopting techniques BCH coding and MRC diversity at a time. Additional it's a substantial enhancement $(10^{-3})$satisfaction) in fading index (m=3, 22dB) in addition to strong impulse noise and 24dB over with data service standard $(10^{-5})$ BER satisfaction. This result show that there is error performance improved by diversity branch and coding advantage.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.257-267
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• 2013

3-D ore modeling was performed to understand the configuration of ore bodies by integrated analysis of borehole and geophysical data in iron-mine area. Five representative indices of rocks were designated, which were obtained from geological survey and borehole. The five indices of rocks were geostatistically simulated by Sequential Indicator Simulation method to delineate boundary of the ore bodies. And Ordinary Kriging and Sequential Gaussian Simulation was applied to make secondary information using resistivity data from magnetotellurics and DC resistivity survey, and this information was used for simple kriging with local varying means, one of integrated kriging techniques. From the correlation analysis between each properties, it was found that high grade of ore is characterized by increased density, whereas the electrical resistivity decreases. With the integrated results of geophysical and borehole data, it was also found that the real configuration of ore body was similar to the modeled result and information about ore grade in 3-D space was obtained.

• Journal of the Korean Geotechnical Society
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• v.30 no.4
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• pp.35-45
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• 2014

Limit equilibrium methods of slope stability analysis have been widely adopted mainly due to their simplicity and applicability. However, the conventional methods may not give reliable and convincing results for various geological conditions such as nonhomogeneous and anisotropic soils. Also, they do not take into account soil slope history nor the initial state of stress, for example excavation or fill placement. In contrast to the limit equilibrium analysis, the analysis of deformation and stress distribution by finite element method can deal with the complex loading sequence and the growth of inelastic zone with time. This paper proposes a technique to determine the critical slip surface as well as to calculate the factor of safety for shallow failure on partially saturated soil slope. Based on the effective stress field in finite element analysis, all stresses are estimated at each Gaussian point of elements. The search strategy for a noncircular critical slip surface along weak points is appropriate for rainfall-induced shallow slope failure. The change of unit weight by seepage force has an effect on the horizontal and vertical displacements on the soil slope. The Drucker-Prager failure criterion was adopted for stress-strain relation to calculate coupling hydraulic and mechanical behavior of the partially saturated soil slope.

• Korean Journal of Materials Research
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• v.8 no.10
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• pp.884-889
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• 1998

Amorphous silicon films of large area have been crystallized by a line shape excimer laser beam of one dimensional scanning with a gaussian profile in the scanning direction. In order to characterize the crystalline phase transition of thickness variables in excimer laser annealing(ELA), angle wrapping method was used. And also to characterize the residual stresses of crystalline phase transition in the case of angle wrapped-crystalline silicon on corning 7059 glass, polarized raman spectroscopies were measured at various laser energy density and substrate temperature. The residual stress varies from $9.0{\times}10^9$ to $9.9{\times}10^9$, and from $9.9{\times}10^9$ to $1.2{\times}10^10$dyne/${cm}^2$ of the substrate temperature at room temperature and varies from $8.1{\times}10^9$ to $9.0{\times}10^9$, and from $9.0{\times}10^9$ to $9.9{\times}10^9$dyne/${cm}^2$ of the substrate temperature at $400^{\circ}C$ as a function of direction from surface to substrate. According to the direction from the surface in liquid phase to the interface and from the interface to near the substrate in solid phase of recrystallized Si thin film, respectively. Thus, the stress is increased from(Liquid phase to solid phase) with phase transition.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.321-326
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• 2019

Image denoising methods based on block matching are founded on the experimental observations that neighboring patches or blocks in images retain similar features with each other, and have been proved to show superior performance in denoising different kinds of noise. The methods, however, take into account only neighboring blocks in searching for similar blocks, and ignore the characteristic features of the reference block itself. Consequently, denoising performance is negatively affected when outliers of the Gaussian distribution are included in the reference block which is to be denoised. In this paper, we propose an expanded block matching method in which noisy images are first decomposed into a number of bit-planes, then the range of true signals are estimated based on the distribution of pixels on the bit-planes, and finally outliers are replaced by the neighboring pixels belonging to the estimated range. In this way, the advantages of the conventional Gaussian filter can be added to the blocking matching method. We tested the proposed method through extensive experiments with well known test-bed images, and observed that performance gain can be achieved by the proposed method.

• 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.