• (The) Research of the performance art and culture
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• no.33
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• pp.59-95
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• 2016

The purpose of this study was to examine the text and musical characteristic of , a changgeuk (a Korean traditional opera), by the National Theater of Korea, which was performed overseas with the title of and recognized its artistic values home and abroad alike, focusing on the process of its rebirth from a lost pansori. A changgeuk was dramatized from a lost pansori into a Korean traditional opera. In the process of rebirth of , the content of latter half, which is the performance of a funeral service for the deceased Byeongangsoe, was deleted, and the contents of Ongnyeo's fight against jangseungs in order to take back Byeongangsoe was newly inserted, thus creating textual changes. In addition, as the title presents, Ongnyeo is no longer a conventional lewd woman, but a subjective and independent female who is fighting against fate, different from its original perspective in which the leading character is Byeongangsoe. All the sounds of a changgeuk were made by the creative technique of traditional Korean songs through various attempts, such as inserting chords between performers in order to present most appropriate songs for the opera, namely proper sounds for the hidden side of the opera. In addition, according to the change of mind of performers or characters, the tone and vocal sound of the song were different. In particular, a changgeuk attempted a variety of techniques in the accompaniment of music, and used many sound buks or diverse genres such as popular music, waltz, classic and folk songs of every province, thus presenting challenging attempts. These attempts made the opera more abundant and helped it to be expressed realistically and dramatically. As above, the contents of a changgeuk were borrowed from classical narrations, but its musical aspects got off the technique of traditional changgeuk, thus attempting various changes and techniques. In this vein, it presented a novel modality of changgeuk equiping with the characteristic of 'reviewing the old and learning the new,' thus proposing the directivity and possibility of changgeuk in the present society.

• Cross-Cultural Studies
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• v.25
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• pp.695-721
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• 2011

The purpose of this study is to provide the case study of expression education using story-telling about their cultures from which they came to the women who get interracial married and study korean cultures with the pride of their homeland. This research is also for the diverse members of korean society to deeply understand interracial married women, get higher understanding cultural diversities. And it is expected that these women could learn and study more korean cultures, too. In this study, process-based instruction method is used in the first step and second step such as brainstorming, questioning, discussing, investigating, teacher's asking in order to create some ideas about their home countries. Suggesting an example answer by teacher and free-writing are also involved. As the core of the process-based writing activity, the second step is focused on revising and correcting. Through reviewing their own writing task, feedback from teacher, interviewing from the difficulty of writing after this activity to cultural and linguistic backgrounds, they could appreciate their errors or mistakes in writing are natural and this affects their learning abilities positively. In third step which is focused on speaking activities, teacher provides feedback to learners after checking their common errors or habits in speaking. Meanwhile, by evaluating the role of the appraiser, It is helpful for the learners to have self-esteem of their own. When interviewing after fourth step's activities, the teacher compliments each learner's improvement while pointing out some errors. Afterward, We can see they show more positiveness to learn and understand korean cultures and set their identities. And they indicate interests and concerns each other's cultures by story-telling. It means they identify the popularity and interaction which the story-telling contains. Also, they confirm the participation in story-telling by expressing their willingness to revise their stories. After the activities in fifth step, there have been relatively positive changes in establishing identity and cultivating a sense of pride of learner's homeland cultures. Furthermore, we could find the strong will to be a story-teller about their homeland cultures. On this research, the effectiveness of expression education case study using story-telling about local cultures of interracial married women's homeland has been examined centrally focused on popularity, interaction, and participation. Afterward, interracial married women could not only cultivate the understanding about korean cultures but also establish their identity, improve their korean language skills through this education case study. Finally, the studies of the education programs to train interracial married women as story-tellers for their homeland local cultures are expected.

• Journal of Christian Education in Korea
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• v.68
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• pp.375-415
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• 2021

Although the interest in Christian education of spirituality has increased recently, the practice of the education of spirituality in the Korean Church has been fragmented in the contents and methods without any clear educational purpose of the Protestant tradition. This requires a creative study to seek out the contents and method best suited to realizing the educational purpose of the Protestant tradition, through a rigorous academic methodology. This study proposes just such a creative model for the education of spirituality with an educational purpose based on the core ethos of the Protestant spirituality, integrating the long tradition of spiritual practices of Christianity. First, I survey the teachings on 'the life of faith' of the main leaders of the Protestant church, including Martin Luther, John Calvin, and John Wesley. Through this process, I reveal 'liberated-ness' to be the common purpose of the Protestant leaders, and the core of the practices for that purpose are 'the means of grace,' which has a different meaning from that of the Roman Catholic tradition. I construct the meaning of 'liberated-ness' in a dynamic manner, which begins with the 'liberating will' of God, and is followed by the 'self-giving will' of the believer as the response to the 'grace' of the 'liberating will.' The contact point of these two 'wills' is what I call 'the living membrane of faith.' As a creative synthesis of the above discussions, I propose a model of 'the practice of liberated-ness' for an education in spiritual practice. The purpose of this education is for the learner to become a person who continuously experiences ever-increasing 'liberated-ness' through continuous personal 'encounters' with God, and to become ever more faithful in carrying out practices for the 'liberated-ness' of her or his neighbors. The relationship between the teacher and the learner is that of personal 'encounter' as put forth by Sherrill, and also incorporates elements of 'co-authorship' as conceptualized by Kim. I transform and rename major practices of spiritual discipline according to a principle of 'liberated-ness' based on the Protestant tradition, and these comprise the main content of my spirituality education model. They include: 'lectio divina of encounter,' 'prayer facing the Lord,' 'service in liberation,' 'reflection of liberated-ness,' and 'mutual spiritual direction.' The teaching and learning process draws on Dykstra's methods of coaching and mentoring. The key environment is that of a 'sacramental community' as defined by Moore. Evaluation can be performed only by the learner her/himself. The significance of this model is that it creatively inherits and succeeds the tradition of Christian spiritual discipline from the early church onwards by transforming it through a Protestant spirituality of 'liberated-ness.'

• (The) Research of the performance art and culture
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• no.43
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• pp.203-228
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• 2021

This thesis examines the play education method shown in the elementary school textbook 'Enjoy Play'. If the educational methods of the curriculum other than plays were presented in the order of 'Understanding play - Appreciation of Works - Creation of Works', the method of drama education is presented sequentially in the order of 'Understanding play - Creation of Works - Appreciation of Works' in the order of 'Understanding play - Artwork - Appreciation' have. Even if such a curriculum considers the study linked to the subject of 'Plays', students may not feel the 'burden' of 'creation', and by simplifying the understanding of 'spoken language', it is rather the characteristic of 'Korean language'. It may also make it difficult for students to feel the attraction. In addition, empathy through the conflict situation of the play or comparison with the actual conflict is mainly presented through the translation of foreign works or the expression of a fairy tale and fantastic world that is far from reality, so the burden of inferring the right life problems can be confirmed. Theatrical expressions and plays and plays learned through textbooks are partially different depending on the educational goals to be achieved. The result of this study is that the course of textbooks for elementary and upper grades may correspond to the problem of expressing 'Plays', but it is regrettable in leading students to think about ways to solve life problems in detail through 'Plays'. It is also necessary to emphasize the importance of expression that makes students realize how to express themselves autonomously in the way of expressing their feelings, but on the other hand, on the other hand, it is necessary to share empathy with feelings first, understand these feelings, Therefore, it was suggested that training to infer expressions and emotions by learning individual expressions through methods of expressing emotions and a process of educating students to voluntarily accept shared emotions are also necessary. Sharing and expressing emotional emotions through 'play', and participation through cooperation and division of labor through the process of performing.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• 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 Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.91-116
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• 1991

Market liberalization progressing simultaneously with high and rapidly rising domestic wages has created an adverse business environment for domestic firms. Korean firms are losing their international competitiveness in comparison to firms from LDC(Less Developed Countries) in low-tech industries. In high-tech industries, domestic firms without government protection (which is impossible due to the liberalization policy and the current international status of the Korean economy) are in a disadvantaged position relative to firms from advanced countries. This paper examines the division of roles between the private sector and the government in order to achieve a successful structural adjustment, which has become the impending industrial policy issue caused by high domestic wages, on the one hand, and the opening of domestic markets, on the other. The micro foundation of the economy-wide structural adjustment is actually the restructuring of business portfolios at the firm level. The firm-level business restructuring means that firms in low-value-added businesses or with declining market niches establish new major businesses in higher value-added segments or growing market niches. The adjustment of the business structure at the firm level can only be accomplished by accumulating firm-specific managerial assets necessary to establish a new business structure. This can be done through learning-by-doing in the whole system of management, including research and development, manufacturing, and marketing. Therefore, the voluntary cooperation among the people in the company is essential for making the cost of the learning process lower than that at the competing companies. Hence, firms that attempt to restructure their major businesses need to induce corporate-wide participation through innovations in organization and management, encourage innovative corporate culture, and maintain cooperative labor unions. Policy discussions on structural adjustments usually regard firms as a black box behind a few macro variables. But in reality, firm activities are not flows of materials but relationships among human resources. The growth potential of companies are embodied in the human resources of the firm; the balance of interest among stockholders, managers, and workers of the company' brings the accumulation of the company's core competencies. Therefore, policymakers and economists shoud change their old concept of the firm as a technological black box which produces a marketable commodities. Firms should be regarded as coalitions of interest groups such as stockholders, managers, and workers. Consequently the discussion on the structural adjustment both at the macroeconomic level and the firm level should be based on this new paradigm of understanding firms. The government's role in reducing the cost of structural adjustment and supporting should the creation of new industries emphasize the following: First, government must promote the competition in domestic markets by revising laws related to antitrust policy, bankruptcy, and the promotion of small and medium-sized companies. General consensus on the limitations of government intervention and the merit of deregulation should be sought among policymakers and people in the business world. In the age of internationalization, nation-specific competitive advantages cannot be exclusively in favor of domestic firms. The international competitiveness of a domestic firm derives from the firm-specific core competencies which can be accumulated by internal investment and organization of the firm. Second, government must build up a solid infrastructure of production factors including capital, technology, manpower, and information. Structural adjustment often entails bankruptcies and partial waste of resources. However, it is desirable for the government not to try to sustain marginal businesses, but to support the diversification or restructuring of businesses by assisting in factor creation. Institutional support for venture businesses needs to be improved, especially in the financing system since many investment projects in venture businesses are highly risky, even though they are very promising. The proportion of low-value added production processes and declining industries should be reduced by promoting foreign direct investment and factory automation. Moreover, one cannot over-emphasize the importance of future-oriented labor policies to be based on the new paradigm of understanding firm activities. The old laws and instititutions related to labor unions need to be reformed. Third, government must improve the regimes related to money, banking, and the tax system to change business practices dependent on government protection or undesirable in view of the evolution of the Korean economy as a whole. To prevent rational business decisions from contradicting to the interest of the economy as a whole, government should influence the business environment, not the business itself.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.