• Journal of Chest Surgery
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• v.32 no.7
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• pp.628-636
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• 1999

Background: Recent advances in understanding the anatomy of the complete atrioventricular septal defect(including right-dominant unbalanced atrioventricular septal defect) have led to alternative methods of repairing these defects. Material and Method: From May 1997 to July 1998, 8 consecutive infants(age range, 2 to 28 months, mean body weight 6.0$\pm$2.2 kg) received a single-stage intracardiac repair of the complete atrioventricular septal defect with modified surgical methods. Depending on the specific anatomic structure, the procedure was simplified in 3 patients by a direct closure of the ventricular element of the defect(Group I). Two patients judged unsuitable for direct closure due to a potential left ventricular outflow tract obstruction had received a standard two-patch repair(Group II). The remaining 3 patients with right-dominant unbalanced complete atrioventricular septal defect underwent biventricular repair; to enlarge the orifice of the left atrioventricular valve, the ventricular septal patch was placed slightly more to the right of the ventricular crest, a left sided bridging leaflet was augmented with an autologous pericardial patch, and the leaflet was repaired with a double- orifice(Group III . Result: In all 8 patients, the postoperative echocardiography demonstrated good hemodynamics. Seven patients were weaned from the ventilators after a mean 3$\pm$1 days, and 1 patient was weaned after 24 days due to a reoperation and emphysematous lung problem. A reoperation was performed in 1 patient for progressive left atrioventricular valve regurgitation due to leaflet tearing. There were no early and late mortalities. At the time of the latest review, judging from the echocardiographic criteria, left atrioventricular valve stenosis was mild in 1 patient(mean pressure gradient 6.5 mmHg, 13.5%), left atrioventricular valve regurgitation was absent or grade I in 7 patients(87.5%). The right atrioventricular valve regurgitation was absent or grade I in all 8 patients(100%). Conclusion: Infants with complete atrioventricular septal defect were treated with either a simplified approach with direct closure of the ventricular element of the defect or a modified surgical technique for a right-dominant unbalanced atrioventricular septal defect, depending on the anatomic structure. The results were no operative mortalities and low morbidity.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Korean journal of applied entomology
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• v.12 no.3
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• pp.93-107
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• 1973

Garlic (Allium sativum L.) is an important vegetable crop for the Korean people and has long been cultivated extensively in Korea. More recently it has gained importance as a source of certain pharmaceuticals. This additional use has also contributed to the increasing demand for Korean garlic. Garlic has been propagated vegetatively for a long time without control measures against virus diseases. As a result it is presumed that most of the garlic varieties in Korea may have degenerated. The production of virus-free plants offers the most feasible way to control the virus diseases of garlic. However, little is known about garlic viruses both domestically and in foreign countries. More basic information regarding garlic viruses is needed before a sound approach to the control of these diseases can be developed. Currently garlic mosaic disease is most prevalent in plantings throughout Korea and is considered to be the most important disease of garlic in Korea. Because of this importance, studies were initiated to isolate and characterize the garlic mosaic virus. Symptom expression in test plants, physical properties, purification, serological reaction and morphological characteristics of the garlic mosaic virus were determined. Results of these studies are summarized as follows. 1. Surveys made throughout the important garlic growing areas in Korea during 1970-1972 revealed that most of the garlic plants were heavily infected with mosaic disease. 2. A strain of garlic mosaic virus was obtained from infected garlic leaves and transmitted mechanically to Chenopodium amaranticolor by single lesion isolation technique. 3. The symptom expression of this garlic mosaic virus isolate was examined on 26 species of test plants. Among these, Chenopodium amaranticolor, C. quince, C. album and C. koreanse expressed chlorotic local lesions on inoculated leaves 11-12 days after mechanical inoculation with infective sap. The remaining 22 species showed no symptoms and no virus was recovered from them whet back-inoculated to C. amaranticolor. 4. Among the four species of Chtnopodium mentioned above, C. amaranticolor and C. quinoa appear to be the most suitable local lesion test plants for garlic mosaic virus. 5. Cloves and top·sets originating from mosaic infected garlic plants were $100\%$ infected with the same virus. Consequently the garlic mosaic virus is successively transmitted through infected cloves and top-sets. 6. Garlic mosaic virus was mechanically transmitted to C, amaranticolor when inoculations were made with infective sap of cloves and top-sets. 7. Physical properties of the garlic mosaic virus as determined by inoculation onto C. amaranticolor were as follows. Thermal inactivation point: $65-70^{\circ}C$, Dilution end poiut: $10^-2-10^-3$, Aging in vitro: 2 days. 8. Electron microscopic examination of the garlic mosaic virus revealed long rod shaped particles measuring 1200-1250mu. 9. Garlic mosaic virus was purified from leaf materials of C. amaranticolor by using two cycles of differential centrifugation followed by Sephadex gel filtration. 10. Garlic mosaic virus was successfully detected from infected garlic cloves and top-sets by a serological microprecipitin test. 11 Serological tests of 150 garlic cloves and 30 top-sets collected randomly from seperated plants throughout five different garlic growing regions in Korea revealed $100\%$ infection with garlic mosaic virus. Accordingly it is concluded that most of the garlic cloves and top-sets now being used for propagation in Korea are carriers of the garlic mosaic virus. 12. Serological studies revealed that the garlic mosaic virus is not related with potato viruses X, Y, S and M. 13. Because of the difficulty in securing mosaic virus-free garlic plants, direct inoculation with isolated virus to the garlic plants was not accomplished. Results of the present study, however, indicate that the virus isolate used here is the causal virus of the garlic mosaic disease in Korea.

• 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.