• Journal of Nutrition and Health
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• v.51 no.6
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• pp.538-555
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• 2018

Purpose: This study examined the association of the type and frequency of coffee consumption with the health-related quality of life and metabolic bio-markers in adult men and women from the 2013 ~ 2016 Korea National Health and Nutrition Examination Survey (KNHANES). Methods: A total of 11,201 subjects (4,483 men and 6,718 women) were classified according to the type of coffee consumption (non-coffee, black coffee, 3-in-1 coffee) and type and frequency of coffee consumption (non-coffee, ${\leq}2$ times/day of black coffee, > 2 times/day of black coffee, ${\leq}2$ times/day of 3-in-1 coffee, > 2 times/day of 3-in-1 coffee) using food frequency questionnaires. Dietary nutrient intake data were assessed using food frequency questionnaires. The health-related quality of life was measured using the EuroQol-5 dimension (EQ-5D) and EQ-5D index score. Data on metabolic bio-markers were obtained from a health examination. Results: Among men and women, the proportion of subjects with an energy intake below the estimated energy requirement (EER) was lower among the 3-in-1 coffee consumption group, and the proportion of subjects with iron intakes below the estimated average requirements (EAR) was lower among the 3-in-1 coffee consumption group. Women (OR: 0.810, 95% CI: 0.657 ~ 0.998) with the ${\leq}2$ times/day of 3-in-1 coffee had a lower risk of impaired health-related quality of life (lowest 20% level in the EQ-5D score) compared to the non-coffee consumers after a multivariable adjustment. In both men and women, the type and frequency of coffee consumption was not associated with metabolic bio-markers risk after multivariable adjustment. Conclusion: These results suggest that 3-in-1 coffee consumption may be associated with a lower risk of impaired health-related quality of life and may not be associated with the metabolic bio-markers risk in adult men and women.

• Cross-Cultural Studies
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• v.44
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• pp.211-237
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• 2016

This paper seeks to explore the new relationship between music and poetry established in the beginning of the Modern Era. This was a period when reading silently was the dominant culture rather than reading aloud and orality was limited due to the emergence of literacy and print culture. A poet sensitive to the characteristics of the period, $Mallarm{\acute{e}}$ created his own concept of music and new creative principles of poetry from it. We analyze his "Divigation" and letters, in particular, the "Crisis of vers", "Music and Literature", "Mystery in the letters", and "About the book." Firstly, $Mallarm{\acute{e}}$ connects music with the mystery and the sacred: the mystery surrounds the music and the music is oriented with the sacred. The sanctity is that of the human race and has existed within humans since the beginning. Transposing the characteristics of this music to the poetry is his first creative principle of poetry. However, $Mallarm{\acute{e}}$ called music a totality of relationships that exist between objects without reducing the dimension to only the instruments or the sound. His definition is abstract, regarding music as a complete rhythm, the atmosphere and the air. Secondly, we have the question of how to realize music in a poem. As the music is surrounded by the mystery, $Mallarm{\acute{e}}$ can transpose the sacred to a poem in mysterious ways. This leads to his second principle of poetry: make a poem as a structure. In other words, 'musically', based on the disappearance of real objects and the initiative of the poet, he created a structure with only the words. We can create an acoustic structure but $Mallarm{\acute{e}}$ created a visible structure to overcome the incompleteness of the sound of a word in the diffusion of print culture. In this manner, the use of silence as much as sound and the use of visual as much as aural components were introduced in poetry as important motifs and the essentials of creation. This new relationship between poetry and music and the creative principles drawn from it appear to be the areas to which attention should be focused in the research of poetry.

• Korean Journal of Heritage: History & Science
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• v.46 no.2
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• pp.70-95
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• 2013

The Tejaprabha Buddha painting, located in the Korai Museum in Kyoto, Japan, was made in 1569 when Joseon Dynasty was in his $14^{th}$ year under SeonJo's ruling, and is only one of Tejaprabha Buddha paintings from the early Chosun dynasty. With its well preserved state, the painting allows clear indications of all icons and list of names that were written, and the record region also has minimal deterioration. This Buddhist painting is a GumSeonMyoHwa which is drawn with gold lining on red hemp cloth and has a relatively small dimension of $84.8{\times}66.1cm$. With the Tejaprabha Buddha in the center, the painting has two unidentified Bodhisattvas, Navagrabha, Rahu, Keto, YiSipPalSoo (28 constellation of the eastern philosophy), SipYiGoong (12 zodiacs of the western philosophy), SamDaeYookSung, and BookDooChilSung (the Big Dipper), all of which provide resourceful materials for constellation worshipin the Joseon era. This painting has a crucial representation of the overall Tejaprabha Buddhism - a type of constellation worships - from the early Joseon dynasty. Even though the composition does seem to be affiliated with the paintings from the Koryo dynasty, there are meaningful transformations that reflect changes in content into constellation worship in Joseon dynasty. As a part of the Tejaprabha Buddha, SipIlYo has become a center of the painting, but with reduced guidance and off-centered 'Weolpe (star)', the painting deteriorates the concept of SipIlYo's composition. Furthermore, addition of Taoistic constellation beliefs, such as JaMiSung (The purple Tenuity Emperor of the North Pole), OkHwangDaeChae, and CheonHwangJae, eliminates the clear distinction between Taoistic and Buddhist constellation worships. Unlike the Chinese Tejaprabha Buddha painting, the concept of YiSipPalSoo (28 constellation of eastern philosophy) in this painting clearly reflects Korean CheonMoonDo's approach to constellation which can be applied to its uniqueness of the constellation worships. The fact that the Big Dipper and ChilWonSungKoon (Buddha of the Root Destiny Stars of the Northern and central Dipper) are simultaneously drawn can also be interpreted as the increase in importance of the constellation worship at the time as well.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.

• Korean Journal of Heritage: History & Science
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• v.37
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• pp.243-266
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• 2004

This paper is based on fieldwork conducted from July 6, 2003 to July 24 of 2003 among the Tungusgroups Hezhe, Daur, Oloqun, Owenke, and Mongolian in the areas of Heilongjiang and Inner Mongolia Provinces. Recognizing the need for more in-depth study among these groups, the present research shows that the Tungus people are archeologically, historically, and linguistically different from Korean Han ethnic group and challenges the link between Korean and Tungus groups since the Bronze Age. The comparison between the "House Spirit" belief of the Tungus people and Koreans reveals certain commonalities in the "Maru," "Kitchen," and "Samshin Spirit" practices. There are two possible reasons for such commonalities. Historically, the Korean Han ethnic group and the Tungus people were geographically intimate, and contact or transmission between the two groups occurred naturally. Also, immigration of refugees from the fallen Koguryo and Puyo to the Tungus region added another dimension of cultural contact. In contrast to the common features shared between the two groups, there also exists differences between the two groups House Spirit blief. The Korean Han group's "House Spirit" belief is based on the agricultural practices that separates the inside sacred and outside secular world of the houses, whereas the Tungus ethnic group's "House Spirit" belief is based on mobile herding life style with a less distinction between in and outside of house. Additionally, each Korean "House Spirit" has its own distinctive personality, and each spirit is placed and worshipped according to its function. In the Tungus group, all the "House Spirits" are located and worshipped in "malu," and some of the spirits are non-conventional house spirits. Moreover, Korean "House Spirits" form a kinship structure, placing Songju, the highest spirit, at the center. In the Tungus practice, such structure is not found. The tight cohesive family formation among the house spirits in the Korean "House Spirit" belief is also the most distinctive feature in its comparison with Chinese belief. In China, the highest spirit is Jiang Taigong or Qiwu, and the house spirits do not have kinship relations. Korean's Outhouse Spirit and Chowangshin are related to the Han Chinese's counterpart on certain levels? however, their basic structures are different. It is clear that the correlation of "Malu" "Chowangshin" and "Samshin" between Korea and Tungus indicate important role of Tungus cultural elements within Korea's "House Spirit" belief.

• Journal of Nutrition and Health
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• v.52 no.1
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• pp.58-72
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• 2019

Purpose: This study examined the food and nutrient intake of Korean elderly according to the anxiety and depressive condition using the data from the Korea National Health and Nutrition Survey (KNHANES) from 2013 to 2015. Methods: The participants were 3,504 elderly people over 65 years of age (1,523 in men and, 1,981 in women). The dietary information was analyzed using the 24-hour recall data. The anxiety and depressive state was assessed using the self-reported scale EQ-5D in the quality of life dimension. The subjects were divided into the anxiety depression group (AD) and non-anxiety depression group (NAD) according to their anxiety and depressive conditions. Results: In the male elderly, the AD group had a significantly lower education and economic level and higher proportion in living alone than the NAD group. The percentage of eating lunch and dinner alone in the male AD group was higher than that of the NAD group. The female AD group showed less a lower frequency of dinner than the NAD group. The male AD group had a lower consumption of total foods, fish and shellfishes, seaweeds, mushrooms, oils and fats, and seasonings than the NAD group. With regard to the nutrient intake, the male elderly NAD group had more sufficient nutrient intakes than the AD group. In particular, the daily intakes of dietary fiber, riboflavin, niacin, potassium and iron were significantly lower in the AD group. To compare with the nutrient density of the two groups, the vitamin C and niacin intakes were lower in the AD group than in the NAD group. Overall, the nutritional status of the male AD group was significantly lower than that of the NAD group. Meanwhile, the female elderly had showed a smaller difference in nutrient intake according to their anxiety and depressive condition. Conclusion: These results of this study show that more nutritional education and emotional support are needed to improve the nutritional status and health of the male elderly with anxiety or depression.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.47-59
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• 2019

Various studies have been carried out on the subject of entrepreneurship, which is required to create new businesses and organizations during the early process of startups based on innovative technologies and ideas. At the same time, the concept of organizational entrepreneurial orientation, which explains how to manage enterprises in the process of pioneering new products and markets, is drawing more and more attention for the purpose of continuously creating and maintaining a competitive edge of startups. This study focused on the relationship between entrepreneurial orientation and startup performance and the role of technological orientation and social capital. An empirical research was conducted on 144 different startup companies residing in startup supporting institutions. To evaluate the suitability of the research model, a PLS-based structural equation model was used. The research results are as follows: First, the entrepreneurial orientation of startups was found to have a positive effect on startup performance. Second, it was shown that entrepreneurial orientation had a positive effect on all three dimensions of social capital and technological orientation. Third, it has been shown that technological orientation and the cognitive dimension of social capital mediates the relationship between entrepreneurial orientation and startup performance. Through this, it was confirmed that entrepreneurial orientation directly affects startup performance, and it even influences the growth of startups by increasing technological superiority and social capital which is inherent in the network. Also, the research identified the need for additional research on the relationship between the strengthening of technological orientation and strategical orientation in startups. This study is expected to expand the discussion about social capital in the field of startup related research by affirming the role and importance of the cognitive system embedded in the network as well as the connectivity of networks, which has been already emphasized in previous startup related studies. Finally, the results of this study were reflected to present new practical implications.