• Journal of the Daesoon Academy of Sciences
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• v.43
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• pp.139-177
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• 2022

The only text that aids in the understanding of Songs of the Dao of Great Gods (大化神道歌詞), which was established by Yun Jin in 1984, is Mok-wun daegyeong (木運大經, The Great Scripture of Wood-Destiny) published by Oh Yeol-gyun in 1976. This scripture includes five songs: Wun-hoe dongbang-ga (運回東方歌, Songs of Destiny-Returning to the East), Gung-eul-ga (弓乙歌, Songs of Gung-eul), Dodeok-sa (道德詞, Morality Poems), Palguae-gugung byeon-yeok-ga (八卦九宮變易歌, Songs on the Changes of the Eight Trigrams and Nine Palaces), and Nakdang-ga (樂堂歌, Songs of the Paradisiacal Lands). Songs of the Dao of Great Gods, which is prone to embracing Daoist characteristics, is meant to be sung upon the realization of the Later World, the ideal earth. This is expressed as spring. In addition, we can easily find key terms such as Sampung-ga (三豊歌), Yangbaek-segye (兩白世界), Gung-eul-ga (弓乙歌), Gunggung-euleul (弓弓乙乙), Yanggung (兩弓), Euleul (乙乙), Gung-eul seonin (弓乙仙人), Samin-ilseok (三人一夕), Yijae-jeonjeon (利在田田), Gung-eul jiri (弓乙之理), Naenggeum-bugeum (冷金浮金), Seokjeong-gon (石井昆), Yangbaek (兩白), Sampung (三豊), and Sodumujok (小頭無足), all of which appear frequently in traditional prophecies and the faiths they have inspired. The precise meaning of these terms has yet to be revealed. Furthermore, Songs of the Dao of Great Gods contains lyrics prophesying that the return of the wood-destiny of the East and emphasizing the destiny of 3-8 wood as based on the Yellow River Chart (河圖). Songs of the Dao of Great Gods, originated the term, the World of Paradisiacal Lands (樂堂世界), and prophesyed that the wood-destiny of the East would return to create a new world that took Korea as its center. The text emphasized wood-destiny, symbolized by spring, and argued that the Dao of Great Gods could be ascetained from the principle of water-producing wood (水生木) found in the Eastern study of changes (易學) as approached by Choi Su-Wun (水雲), the founder of Donghak (東學).

• Journal of The Korean Association For Science Education
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• v.29 no.8
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• pp.990-1010
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• 2009

To derive brain-based evolutionary educational principles, this study examined the studies on the structural and functional characteristics of human brain, the biological evolution occurring between- and within-organism, and the evolutionary attributes embedded in science itself and individual scientist's scientific activities. On the basis of the core characteristics of human brain and the framework of universal Darwinism or universal selectionism consisted of generation-test-retention (g-t-r) processes, a Model of Brain-based Evolutionary Scientific Teaching for Learning (BEST-L) was developed. The model consists of three components, three steps, and assessment part. The three components are the affective (A), behavioral (B), and cognitive (C) components. Each component consists of three steps of Diversifying $\rightarrow$ Emulating (Executing, Estimating, Evaluating) $\rightarrow$ Furthering (ABC-DEF). The model is 'brain-based' in the aspect of consecutive incorporation of the affective component which is based on limbic system of human brain associated with emotions, the behavioral component which is associated with the occipital lobes performing visual processing, temporal lobes performing functions of language generation and understanding, and parietal lobes, which receive and process sensory information and execute motor activities of the body, and the cognitive component which is based on the prefrontal lobes involved in thinking, planning, judging, and problem solving. On the other hand, the model is 'evolutionary' in the aspect of proceeding according to the processes of the diversifying step to generate variants in each component, the emulating step to test and select useful or valuable things among the variants, and the furthering step to extend or apply the selected things. For three components of ABC, to reflect the importance of emotional factors as a starting point in scientific activity as well as the dominant role of limbic system relative to cortex of brain, the model emphasizes the DARWIN (Driving Affective Realm for Whole Intellectual Network) approach.

• 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.