• Tuberculosis and Respiratory Diseases
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• v.53 no.2
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• pp.173-182
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• 2002

Background : Nontuberculous mycobacteria (NTM) have usually been considered to be contaminants of colonizers when isolated from respiratory specimens in Korea, where there is a high prevalence of tuberculosis and a low rate of HIV infections. Therefore, there has been few studies on the clinical significance of NTM species in immunocompetent patients were investigated. Methods : Thirty-five NTM isolates, for which species identification was requested by the treating physicians during 1999 at the Asan Medical Center, were retrospectively analyzed. They were identified to the species level by mycolic acid analysis using high-performance liquid chromatography. The medical records of the patients with the NTM isolates were reviewed to identify those patients who met the American Thoracic Society (ATS)'s criteria for mycobacterial pulmonary infection. Their antimicrobial susceptibility data were compared with the clinical outcomes. Results : The NTM were identified as M. intracellulare (6 isolates), M. avium (5), M. abscessus (5), M. gordonae (5), M. terrae complex (4), M. szulgai (2), M. kansasii (2), M. fortuitum (2), M. peregrinum (1), M. mucogenicum (1), M. celatum (1), and M. chelonae (1). All 35 patients showed clinical symptoms and signs of chronic lung disease, but none had a HIV infections; 16 (45.7%) patients were found to be compatible with a NTM pulmonary infection according to the ATS criteria, 5 and 4 cases were affected with M. intracellulare and M. abscessus, respectively; 8 patients had a history of pulmonary tuberculosis. 13 patients received antimycobacterial therapy for an average of 21 months and 9 patients were treated with second-line drugs. Only 4 patients had improved radiologically. Conclusion : A NTM should be considered a potential pathogen of pulmonary infections in immunocompetent patients with chronic pulmonary diseases. Most NTM infections were left untreated for a prolonged period and showed a poor outcome as a result, M. intracellulare and M. abscessus were the two most frequent causes of NTM pulmonary infections in this study. Species identification and antimycobacterial susceptibility tests based on the species are needed for the optimum management of a NTM pulmonary infection in patients.

• Fashion & Textile Research Journal
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• v.1 no.3
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• pp.252-258
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• 1999

The aims of this study is to classify the lower somatotype of adult women and appearance analysis on the shape of flare skirts by using the image processing. Also We have made skirts in order to analyze the various types of appearance of flare skirts by using the image processing. The subjects for our wear test lower somatotype, who were controlled in their waist, abdomen and hip shapes. The flare skirts used for wear test were 112 types(combinated 14 fabric type and 8 lower somatotype). The effect of lower somatotype on the shape of flare skirts was determined by the horizontally hem line section shape and the silhouette of flare skirts with image processing. The data were analyzed by using analysis of variance and Turkey, Duncan multiple range test. The results obtained are summarized as follows: It is shown that the fabric weight elongation differs in fabrics properties, in direction of textures. The shape horizontal section of flare skirt hem line has differed with the number of nodes, wave-height of nodes and breadth of silhouette by fabrics properties and lower somatotype. It is noticed that the breadth of flare skirts by the silhouette has high correlation with the drape ability of fabrics and lower somatotype. Results for our flare skirts show changes in amplitude and mean with fabrics, somatotype. Therefore we can say that gray-level histograms are correlated with changes in appearance, differences in drape spacing and related fabric properties and their somatotype.

• 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.