• Korean Journal of Optics and Photonics
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• v.27 no.1
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• pp.1-17
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• 2016

Over the past two decades, fiber-based lasers have made remarkable progress, now having reached power levels exceeding kilowatts and drawing a huge amount of attention from academy and industry as a replacement technology for bulk lasers. In this paper we review the significant factors that have led to the progress of fiber lasers, such as gain-fiber regimes based on ytterbium-doped silica, optical pumping schemes through the combination of laser diodes and double-clad fiber geometries, and tandem schemes for minimizing quantum defects. Furthermore, we discuss various power-limitation issues that are expected to incur with respect to the ultimate power scaling of fiber lasers, such as efficiency degradation, thermal hazard, and system-instability growth in fiber lasers, and various relevant methods to alleviate the aforementioned issues. This discussion includes fiber nonlinear effects, fiber damage, and modal-instability issues, which become more significant as the power level is scaled up. In addition, we also review beam-combining techniques, which are currently receiving a lot of attention as an alternative solution to the power-scaling limitation of high-power fiber lasers. In particular, we focus more on the discussion of the schematics of a spectral beam-combining system and their individual requirements. Finally, we discuss prospects for the future development of fiber laser technologies, for them to leap forward from where they are now, and to continue to advance in terms of their power scalability.

• Cartoon and Animation Studies
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• s.28
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• pp.1-25
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• 2012

The rapid digitalization across all aspects of society since 1990 led to the digitalization of cartoons. As the medium of cartoons moved from paper to the web, a powerful visual culture emerged. An encounter between cartoons and multimedia technologies has helped cartoons evolve into a video culture. Today cartoons are no longer literate culture. It is critical to pay attention to cartoons as an "expanded fest" and as visual and video culture with much broader significance. In this paper, the investigator set out to diagnose the current position of cartoons changing in the rapidly changing digital age and talk about future directions that they should pursue. Thus she discussed cases of changes from 1990 when colleges began to provide specialized education for cartoons and animation to the present day when cartoon and Multimedia Comics fests exist in addition to the digitalization of cartoons. The encounter between new technologies and cartoons broke down the conventional forms of cartoons. The massive appearance of artists that made active use of new technologies in their works, in particular, has facilitated changes to the content and forms of cartoons and the expansion of character uses. The development of high technologies extends influence to the roles of appreciators beyond the artists' works. Today readers voice their opinions about works actively, build a fan base, promote the works and artists they favor, and help them rise to stardom. As artist groups of various genres were formed, the possibilities of new stories and texts and the appearance of diverse styles and world views have expanded the essence of cartoon texts and the overall cartoon system of cartoon culture, industry, education, institution, and technology. It is expected that cartoons and Multimedia Comics will continue to make a contribution as a messenger to reflect the next generation of culture, mediate it, and communicate with it. Today there is no longer a distinction between print and video cartoons. Cartoons will expand in every field through a wide range of forms and styles, given the current situations involving installation concept cartoons, blockbuster digital videos, fancy items, and characters at theme parks based on a narrative. It is therefore necessary to diversify cartoon and Multimedia Comics education in diverse ways. Today educators are faced with a task to bring up future generations of talents who are capable of leading the culture of overall senses based on literate and video culture by incorporating humanities, social studies, and new technology education into their creative artistic abilities.

• Journal of Distribution Science
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• v.10 no.5
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• pp.7-17
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• 2012

The recent announcement of the National Research Foundation of Korea (NRF) to cease journal accreditation operations as of the end of the year 2014 can easily influence the future of many research journals in Korea. Although this plan has not yet been formalized or structured, its facilitation would be the major turning point for the current Korean research and scholarly journals and publications. In addition, the NRF's plan to select and fund top 20 or more research journals over the five year period beginning 2015 suggests that the competition will most likely increase among Korean journals. Each journal would need to develop its unique strategy to improve and strengthen its competitiveness to become or maintain its position as a major research journal in Korea. The association of Korean Distribution of Science (KODISA) and its research journal, Journal of Distribution Science (JDS), has been continuously improving its reputation as a reputable journal in the distribution and related fields since its establishment in 1999. Due to demand, JDS has had to undergo several changes in its publication cycle first from semiannual publication to quarterly, then finally to monthly publications in 2012, and has become one of the major social science journals in Korea. Furthermore, with the redesigning of its webpage with English language in July of 2011, KODISA has made the published journals freely accessible and available to both domestic and foreign researchers, scholars, practitioners, and learners. These changes have resulted in the rapid increase in the bounce rate and the number of journal submissions by foreign scholars, with four research articles having been submitted by foreign scholars just in March of 2012 alone. However, although the changes and outcomes have resulted in a reasonable success so far, the achievement may only become a short-term success without continuously developing, improving, and implementing both effective and efficient strategies through critical, thorough, and frequent examinations and evaluations of both KODISA and JDS. As such, the purpose of this research is to carefully examine both KODISA and JDS to identify problematic factors and to develop appropriate strategies to change or modify those problems for further strengthening and improving their reputation and status. The paper examines and analyzes the past, present, and future of KODISA and JDS and their managerial, operational, and systematic procedures and operations. The narrow scope of research and inefficiencies in promoting the association and the journal and the improvement of impact factors are identified as the notable problems that could hinder JDS from being included in SCOPUS or SSCI in the near future. This type of examination and exploration has not been previously conducted, so the major limitation of this paper can be identified as not meticulously elaborating on the problems nor proving detailed recommendations based on the existing researches. This article asserted that solving the problem of the narrow scope of research would lead to facilitation of resolving other inefficient problems. Inclusion of international academic disciplines to the distribution and their related fields would be the viable initiation of expanding the research area, and this strategy could promote the journal as well as improve its impact factors. The narrow scope of research seems to be a good research topic and merit further exploration as an individual research project, because this kind of research could yield the creation of new understandings or theories.

• KDI Journal of Economic Policy
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• v.14 no.2
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• pp.29-54
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• 1992

Recent studies on the economic effects of trade liberalization and economic integration have emphasized the significant gains associated with product differentiation and scale economies. Securing access to markets in other countries will make it possible to increase product variety and capture scale economies, thus, expanding the gains from trade. Liberalization is also expected to introduce foreign competition into the previously closed market. Concurrently, the liberalization will improve the competitive market environment for firms selling in the domestic market. Firms will be pressed to either exit or reduce cost. The output per firm, then, will increase due to the exit of rival firms, and the average total cost will decline due to the economies of scale. 'Rationalization' of the production process will eventually follow. This paper addresses the economic effects of (counterfactual) bilateral tariff elimination between Korea and Japan. It computationally assesses the gains from liberalization as well as the resource allocations and welfare effects associated with the tariff reduction. The endogenous determination of the key parameters distinguishes this paper from others. The firm's perceived elasticity of demand and elasticity of substitution in the present model are calibrated to be consistent with the base year data. Korea, Japan, and the rest of the world are modeled explicitly. The sectoral coverage of the model includes twenty-three tradable product categories based on three-digit SITC industries and seven nontradable categories based on one-digit SITC industries. Product categories are also classified into perfectly competitive and imperfectly competitive ones. In the imperfectly competitive industries, product differentiation exists at the firm level, while the perfectly competitive industries are characterized by national product differentiation. The simulation results of bilateral tariff reduction are reported. Tariff elimination tends to increase intra-industry trade flows so that the total amount of exports and imports of both countries expand. Yet, Japan is expected to increase the bilateral trade surplus in the wake of the mutual tariff reduction. Terms-of-trade for Korea will not change, while for Japan it will deteriorate. Equivalent variations reflecting the change in consumer surplus (welfare) will favor Korean consumers. Total output, however, will not change substantially, recording 0.5 and 0.6% for Japan and Korea, respectively. An interesting finding in the analysis is that the gains from increased competition and scale efficiency are not as prevailing as expected in theory.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.121-134
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• 2019

This study was conducted to review the trends of forest meteorological studies based on the publications for last 20 years in Korean Journal of Agricultural and Forest Meteorology (KJAFM), and to provide insight for future prospect for researches in the field of forest meteorology. A total of 220 papers related to forest meteorology were published in KJAFM for the last 20 years. That corresponds to 33.5% out of all the papers including agricultural meteorology papers. To review the trends of forest meteorology studies, the 220 published papers were classified into seven categories. They are forest meteorology and forest fire, forest meteorology and tree physiology, forest meteorology and forest protection, micrometeorology in mountain area, climate and forest growth, climate and forest vegetation distribution, and climate change and forest ecosystem. Even if there were differences in paper numbers among the seven categories, it was found that various and very specific studies were conducted in the field of forest meteorology for the last 20 years. It was also expected that the accumulation and utilization of various and accurate forest meteorological information would bring remarkable progress of forest meteorological studies in the near future.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Industry Promotion Research
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• v.7 no.3
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• pp.115-124
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• 2022

The most difficult things, when we study the future-telling science of human destiny, are in case of what one's individual's fate is bad which is shown by Saju-Palza(四柱八字), In that case, we have faced the problems on how we live ; to follow or to deny our fate under the brief of improving our lives by trying to make hard efforts, regardless of the bad Saju-Palza(四柱八字). However, we can hardly find the clear answer to those questions. 『Liao Fan 4 lessons(了凡四訓)』 shows that one's destiny can be improved by accumulating good deeds despite of the bad Saju-Palza(四柱八字). Someone says that future can be created, not be foreseen. As well, Dr. Steven Coby says that the best definite way to forecast future is in creating the future. Anyhow, the strong desire and curiosity to know one's individual's future is having been lasted until now since the Genesis. we guess these desires may be one of our basic instinct. If then, the function and role of the future-telling science will be to increase the accuracy of future prediction, whether our fate has been fixed or been able to be changeable. Therefore, this study summarizes the definition of confusing terms, focusing on Gyeokguk(格局) and Sangshin(相神), the core of Myeongrihak(命理學), which is considered to be one of the most popular future-telling science. Concering Gyeok(格), in this paper, Nae-Gyeok(內格) has been mainly considered and Oi-Gyeok(外格) or Special-Gyeok(別格) have not been addressed. Specifically, it summarized the views of the classical Myeongri(命理) books and modern scholars on Gyeokguk(格局) and Yongshin(用神). In particular, it also summarized the comparison of various concepts of Gyeokguk(格局), the advantages and disadvantages of each Nae-Gyeok(內格)'s characteristic, the determination order of Nae-Gyeok(內格) and the good case and bad case of it's Gyeok(格). In addition, it was necessary to summarize the concept of Sangshin(相神), which was talked about in 『Japyeongjinjeon』 and to briefly summarize Heeshin(喜神) with a broader concept than Sangshin(相神). The different usage of Sangshin(相神) was also analyzed, between the priority interpretation of Cheongan(天干) in Day-Column(日柱) and the interpretation based on Jijee(地支) in Month-Column(月柱). Finally, this paper was completed, leaving it later as a research task, the confusion that comes from the scholars' acceptance of the comprehensive diversity on the same term.

• Journal of International Area Studies (JIAS)
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• v.14 no.4
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• pp.79-106
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• 2011

The cultural contents industries have moved closer to the centre of the economic action in many countries and across much of the world. For this reason, the concern with the development of glocal cultural contents has also been growing. According to Goldman Sock's BRICs report, Indian economy will be the engine of global economy with China. In addition, India will be a new blue chip country for large consumer market of cultual contents. The most important point for the development of glocal cultural contents is a systematic and in-depth analysis of other culture. India is a complex and multicultural country compared with Korea which is a nation-state. Therefore, this paper is intended as an understanding about India appropriately and suggestion for a strategy to enter cultural industry in India. As the purpose of this paper is concerned, we will take a close look at 9 Indian culture codes which can be classified into three main groups: 1) political, social and cultural codes 2) economic codes 3) cultural contents codes. Firstly, political, social and cultural codes are i) consistent democracy and saving common people, ii) authoritarianism which appears an innate respect for authority of India, iii) Collective-individualism which represents collectivist and individualistic tendency, iv) life-religion, v) carpe diem. Secondly, economic culture codes are vi) 1.2billion Indian people's God which represents money and vii) practical purchase which stands for a reasonable choice of buying products. Lastly, viii) Masala movie and ix) happy ending that is the most popular theme of Masala movies are explained in the context of cultural content codes. In conclusion, 3 interesting cases , , will be examined in detail. From what has been discussed above, we suggest oversea expansion strategy based on these case studies. Eventually, what is important is to understand what Indian society is, how Indian society works and what contents Indian prefers.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.63-75
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• 2023

Due to the development of industry, interest in air pollutants has increased. Air pollutants have affected various fields such as environmental pollution and global warming. Among them, environmental diseases are one of the fields affected by air pollutants. Air pollutants can affect the human body's skin or respiratory tract due to their small molecular size. As a result, various studies on air pollutants and environmental diseases have been conducted. Asthma, part of an environmental disease, can be life-threatening if symptoms worsen and cause asthma attacks, and in the case of adult asthma, it is difficult to cure once it occurs. Factors that worsen asthma include particulate matter and air pollution. Asthma is an increasing prevalence worldwide. In this paper, we study how air pollutants correlate with the number of emergency room admissions in asthma patients and predict the number of future asthma emergency patients using highly correlated air pollutants. Air pollutants used concentrations of five pollutants: sulfur dioxide(SO₂), carbon monoxide(CO), ozone(O₃), nitrogen dioxide(NO₂), and fine dust(PM₁₀), and environmental diseases used data on the number of hospitalizations of asthma patients in the emergency room. Data on the number of emergency patients of air pollutants and asthma were used for a total of 5 years from January 1, 2013 to December 31, 2017. The model made predictions using two models, Informer and LTSF-Linear, and performance indicators of MAE, MAPE, and RMSE were used to measure the performance of the model. The results were compared by making predictions for both cases including and not including the number of emergency patients. This paper presents air pollutants that improve the model's performance in predicting the number of asthma emergency patients using Informer and LTSF-Linear models.