• Journal of the Korean Chemical Society
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• v.64 no.6
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• pp.416-428
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• 2020

This study analyzed the uses of external representations presented in the matter units of the 7th-grade science digital textbooks developed under the 2015 revised national curriculum. The level, form, presentation, and interactivity of external representations presented in 5 types of digital textbooks were analyzed. As for the level, the macroscopic level of representations was mainly presented. The macroscopic level and microscopic level of representations were presented together in the particle description. As for the form, visual-verbal and visual-nonverbal representations were usually presented across the board. Very few audial-verbal and audial-nonverbal representations were presented. Visual-verbal and audial-verbal representations were mostly presented in formal form, and visual-nonverbal representations were mostly presented in illustration without movement. The presentation of representations was analyzed in three aspects. First, visual-verbal and visual-nonverbal representations were mainly presented together and none of audial-verbal and visual-nonverbal representations were presented together. When the representations of the audial-verbal, visual-nonverbal, and visual-verbal were presented together, some of the information presented in audial-verbal representations was repeatedly presented in the visual-verbal representations. Second, audial-nonverbal representations not related to learning content were presented along with other representations. Third, there were few cases of arranging visual-verbal and visual-nonverbal representations on the next pages. Audialverbal and visual-nonverbal representations were always presented synchronized. As for the interactivity, the manipulation level was mainly presented in the main area, and the feedback level was mainly presented in the activity area. The adaptation level and the communication level of interactivity were presented very few. Based on the results, the implications for the direction of constructing digital textbooks were discussed.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• 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.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Journal of Korean Classical Literature and Education
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• no.34
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• pp.115-147
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• 2017

This paper compared and reviewed the poetic theories and Chinese poems of the Korean author Kim Chang Heup and his Japanese counterpart, Kan Chazan. Kim Chang Heup and Kan Chazan shared largely the same opinions on poetry, and both rejected archaism. First, they did not just copy High Tang poetry. Instead, they focused on the (sometimes trivial) scenery right in front of them, and described the calm feelings evoked by what they had seen. They also adopted a sincere tone, instead of an exaggerated one, because both believed that poetry should be realistic. However the differences between the two poets are also noteworthy. Kim Chang Heup claimed that feelings and scenery meet each other within a literary work through Natural Law, and the linguistic expressions that mediate the two are philosophical in nature. However, Kan Chazan did not use Natural Law as a medium between feelings and scenery. Instead the Japanese writer said the ideal poetical composition comes from a close observation and detailed description of scenery. In sum, while Kim Chang Heup continued to express reason through scenery, Kan Chazan did not go further than depicting the scenery itself. In addition, Kim Chang Heup believed poetry was not only a representation of Natural Law, but also a high-level linguistic activity that conveys a poetic concern about national politics. As a sadaebu (scholar-gentry), he held literature in high esteem because he thought that literature could achieve important outcomes. On the other hand, Kan Chazan regarded it as a form of entertainment, thereby insisting literature had its own territory that is separate from that of philosophy or politics. In other words, whereas Kim Chang Heup considered literature as something close to a form of learning, Kan Chazan viewed it as art. One might wonder whether the poetics of Kim Chang Heup and Kan Chazan reflect their individual accomplishments, or if the characteristics of Chinese poetry that Korean and Japanese poets had long sought after had finally surfaced in these two writers. This paper argued that the two authors' poetics represent characteristics of Chinese poetry in Korea and Japan, or general characteristics of Korean and Japanese literatures in a wider sense. Their request to depict actual scenery in a unique way, free from the ideal model of literature, must have facilitated an outward materialization of Korean and Japanese literary characteristics that had developed over a long time.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.5
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• pp.14-27
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• 2019

This study presents a plan for a memorial park that respects the characteristics based on the historical facts for the concept of space of the Gwangju Hospital of Korea Army and the location of the 505 Security Forces, which were designated as historic sites after the 5-18 Democratization Movement. The Gwangju Metropolitan City as it is the location of the 5-18 historic sites, is taking part in the 5-18 Memorial Project, and plans to establish a city park recognizing the historic site of the 5-18 Democratization Movement, which has been preserved only as a memory space to this point. The park is promoting a phased development plan. This study suggests that the 5-18 historic sites can be modernized and that social consensus can establish the framework of the step-by-step planning and composition process to ensure the plans for the space heals wounds while preserving the history. In this paper, we propose a solution to a problem. We solve the approach for space utilization through an analysis of precedent research and planning cases related to park planning at historical sites. In addition to exploring the value of the site, we also describe the space utilization strategy that covers the historical characteristics and facts while maintaining the concept of park planning. As a result of the research, the historic site of the Gwangju Hospital of Korea Army is planned as a park of historical memory and healing in order to solve the problems left behind by the 5-18 Democratization Movement. The historic site of the 505 Security Forces was selected as an area for historical experiences and a place for learning that can be sympathized with by future generations of children and adolescents in terms of expanding and sustaining the memory of the 5-18 Democratization Movement. In the planning stage, the historical sites suggested the direction of space utilization for representation as did the social consensus of citizens, related groups, and specialists. Through this study, we will contribute to construction of a memorial park containing historical values in from 5-18 historic sites. It is meaningful to suggest a direction that can revitalize the life of the city as well as its citizen and can share with the history with future generations beyond being a place to heal wounds and keep alive the memory of the past.