• The Journal of Art Theory & Practice
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• no.4
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• pp.77-104
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• 2006

The propaganda paintings in oil colors or in forms of posters made from 1949 to 1966 have gone through some changes experiencing the influence of the Soviet Union Art and discussion of nationalization, while putting political messages of the time in the picture planes. The propaganda paintings which have been through this process became an effective means of encouraging the illiterate people in political ideologies, production, and learning. Alike other propaganda paintings in different mediums, the ones which were painted in oil colors and in the form of posters have been produced fundamentally based on Mao Zedong's intensification of the literary art on the talks on literature at Yenan. Yet, the oil paintings and posters were greatly influenced by the socialist realism and propaganda paintings of the Soviet Union, compared to other propaganda paintings in different mediums. Accordingly, they were preponderantly dealt in the discussions of nationalization of the late '50s. To devide in periods, the establishment of People's Republic of China in 1949 as a diverging point, the propaganda paintings made before and after 1949 have differences in subject matters and styles. In the former period, propaganda paintings focused on the political lines of the Communists and enlightenment of the people, but in the latter period, the period of Cultural Revolution, the most important theme was worshiping Mao Zedong. This was caused by reflection of the social atmosphere, and it is shown that the propaganda painters had reacted sensitively to the alteration of politics and the society. On the side of formalities, the oil paintings and posters made before the Cultural Revolution were under a state of unfolding several discussions including nationalization while accepting the Soviet Union styles and contents, and the paintings made afterwards show more of unique characteristics of China. In 1956, the discussion about nationalization which had effected the whole world of art, had strongly influenced the propaganda paintings in oil colors more than anything. There were two major changes in the process of making propaganda paintings in oil colors. One was to portray lives of the Chinese people truthfully, and the other was to absorb the Chinese traditional styles of expression. After this period, the oil painters usually kept these rules in creating their works, and as a result, the subject matters, characters, and backgrounds have been greatly Sinicized. For techniques came the flat colored surface of the new year prints and the traditional Chinese technique of outlining were used for expressing human figures. While the propaganda paintings in oil colors achieved high quality and depth, the posters had a very direct representation of subject matters and the techniques were unskilled compared to the oil paintings. However, after the establishment of People's Republic of China, the posters were used more than any other mediums for propagation of national policy and participation of the political movements, because it was highly effective in delivering the policies and political lines clearly to the Chinese people who were mostly illiterate. The poster painters borrowed techniques and styles from the Soviet Union through books and exhibitions on Soviet Union posters, and this relation of influences constantly appears in the posters made at the time. In this way, like the oil paintings, the posters which have been made with a direct influence of the Soviet Union had developed a new, sinicised process during the course of nationalization. The propaganda paintings in oil colors or in forms of posters, which had undergone the discussion of nationalization, had put roots deep down in the lives of the Chinese people, and this had become another foundation for the amplification of influences of political propaganda paintings in the following period of Cultural Revolution.

• 대한공업교육학회지
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• v.36 no.1
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• pp.75-94
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• 2011

The purpose of this study is to suggest the basic data in order to examine and perform the plan for activating the team teaching between industry-educational adjunct teachers and vocational teachers in technical high school. The research results are as follows. First, It is found that both teacher groups acknowledge the necessity of the team teaching, but vocational teachers are less likely to recognize the necessity than industry-educational adjunct teachers. Second, In the preparation of team teaching, both two groups of the teachers believe that the preliminary interchange and training between them are to be highly supportive for the activities expected to help teachers. Therefore, it is necessary to have opportunity of communication and narrow the difference of opinions between them by promoting the deep interest about applicable field and sharing the mutual idea between the teachers in the preparation of team teaching. Third, And the two groups recognize that the cooperation and joint establishment of design of team teaching and the individual process or joint progress of class activity are desirable for the proper design of team teaching. Therefore, it is necessary to establish the class environment for the interaction between teachers and students through not only the reciprocal activities between teachers but the interest class by systematically preparing the class design and role division clearly. Fourth, In the practice of team teaching, the two groups believe that the teaching activities can be usually divided and progressed, but it is desirable to work together in the related contents. The vocational teachers recognize that it is necessary to actively interact with students by connecting with the case of industry field. On the other side, industry-educational adjunct teachers think that the learning contents should be selected and organized according to the interests of students by associating with the case of industry field. Fifth, And two groups of teachers recognize that it is desirable to evaluate the grade by reflecting on the assessment by vocational teachers(50%), industry-educational adjunct teachers(50%).

• Sleep Medicine and Psychophysiology
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• v.11 no.1
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• pp.29-36
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• 2004

Objectives: Sound and light (SL) stimulation has been used as a method to induce some useful mental states in the fields of psychology and psychiatry. It is believed that sound and light entrainment device (SLED) has some specific effects through synchronization of EEG in patients who use it. Theta frequency is believed to stimulate deep relaxation and short term memory processing. This study was conducted to evaluate if 4-10 Hz SL stimulation can induce relaxation and improve short term memory function. Methods: Ten medical students with no medical or psychiatric problems participated in this study. Subjects were randomly divided into two groups. One group was applied with real SLED was applied to one group (R group) and pseudo SLED to the other group (P group). The two groups were exposed to SL stimulation with SLED 15 minutes a day for 5 days, and after two days rest the two groups were switched over. The Korean Wechsler Adult Intelligence Scale (K-WAIS), Academic Motivation Tests (AMT), Test Anxiety Scale (TAS), Korean Auditory Verbal Learning Test (K-AVLT), and digit span were used to evaluate short term memory. Spielberger's State-Trait anxiety inventory and heart rate variability (HRV) test were used to evaluate degree of relaxation. Results: Compared with S group, R group showed a significant improvement in K-AVLT and digit span after a single application of SL stimulation. But 5-day long application did not reveal any differences between the two groups. A significant change in HRV was observed in 5-day long application of SL stimulation after being switched over to other SLED. Conclusion: This pilot study suggests that 4-10 Hz SL stimulation has some positive influences on short term memory and relaxation.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• (The)Study of the Eastern Classic
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• no.49
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• pp.357-392
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• 2012

An author is bound to reflect his or her own thinking and inclination in his or her works. The previous studies on Changam(蒼巖), however, mostly discussed the aesthetics in the forms of his introductions and works, hardly addressing his thinking reflected in his works. Recognizing that he had the "reactionism tendency" unlike the Bukhak-School(北學派), which was the cultural mainstream of the days, this study examined the specific patterns of the reactionism calligraphy style in his learning and calligraphy processes and works. He loved to write xing-cao-shu(行草書) with a focus on the materials written in one's own calligraphy, but he also emphasized that one should obtain the force of his or her calligraphy style by mastering kai shu before calligraphy xing cao shu. He thus left a lot of works in the xiao kai(小楷) of the Wang Xzhi(王羲之) calligraphy style throughout his life, which is attributed to the influences of the calligraphers of dong-guk-jin-che(東國眞體) in the latter half of Joseon(朝鮮) and those of Lee Gwang-sa(李匡師), his master in spirit. He is distinguished from the other calligraphers of the times in that he made lifelong efforts to compensate for the lacking stroke of the pen in the model calligraphy of Wang Xzhi. In the calligraphy theory, he put importance on the traditional method of Han-Wei(漢魏) and took Cai Yong(蔡邕) and Zhong Yao(鍾繇) as the fundamentals. For da kai(大楷), he constantly practiced the with the stroke of the pen by added to it, the letters of Wei(魏) Wudi(武帝), by Yan Zhenqing(顔眞卿), and letters of Kim Saeng(金生). His late works using the intended conception of and , in particular, present his unique calligraphy style that added the crooked forms of to the shapes of characters of that were in the kai-shu(楷書) style. It is a limitation that a considerable number of calligraphy materials Changam studied or consulted were either reprint copy or block book rather than original rubbing edition due to time and space restrictions. However, it is also true that those restrictions made an important contribution to his creation of his unique calligraphy style with deep local colors at the result of his constant efforts.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Research in Plant Disease
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• v.28 no.3
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• pp.113-121
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• 2022

Rice blast is a major plant disease that occurs worldwide and significantly reduces rice yields. Rice blast disease occurs periodically in Korea, causing significant socio-economic damage due to the unique status of rice as a major staple crop. A disease outbreak prediction system is required for preventing rice blast disease. Epidemiological investigations of disease outbreaks can aid in decision-making for plant disease management. Currently, plant disease prediction and epidemiological investigations are mainly based on quantitatively measurable, structured data such as crop growth and damage, weather, and other environmental factors. On the other hand, text data related to the occurrence of plant diseases are accumulated along with the structured data. However, epidemiological investigations using these unstructured data have not been conducted. The useful information extracted using unstructured data can be used for more effective plant disease management. This study analyzed news articles related to the rice blast disease through text mining to investigate the years and provinces where rice blast disease occurred most in Korea. Moreover, the average temperature, total precipitation, sunshine hours, and supplied rice varieties in the regions were also analyzed. Through these data, it was estimated that the primary causes of the nationwide outbreak in 2020 and the major outbreak in Jeonbuk region in 2021 were meteorological factors. These results obtained through text mining can be combined with deep learning technology to be used as a tool to investigate the epidemiology of rice blast disease in the future.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.