• Journal of Digital Convergence
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• v.17 no.3
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• pp.79-92
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• 2019

This study analyzes the effects of freight transportation income, capital, asset, non-operating expenses, and debt ratio on the debts of inner port freight transportation businesses through the GLS of panel regression analysis and the estimation of fixed effects model. The factors and hypotheses were established through a theoretical background review, and the financial statement and profit and loss data of inner port freight transportation businesses for 10 years from 2006 to 2015 were analyzed. The results showed that assets had positive effects on debts, and negative effects on capital, non-operating expenses, and debt ratio, but no effect on freight transportation income. This result empirically demonstrates the tendency of inner port freight transportation businesses to secure assets by increasing debts, creation of debt reduction leverage effect using non-operating expenses such as interest expenses through bank borrowing, and the adoption of management characteristics and financial operation method to lower the debt ratio by reducing capital more than debts. In future studies, it is necessary to analyze coastal port freight transportation business by industry (oil tankers, cargo ships, and barge ships), and regions such as East, West and South sea.

• Journal of Science and Technology Studies
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• v.19 no.2
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• pp.1-40
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• 2019

Built upon ethnographic method such as participant observation and in-depth interview, this study analyzes the material culture of electronic flower auctions at Yangjae Flower Market. From the viewpoint of Actor-Network Theory(ANT), this research examines how human actors like dealers and auctioneers interact with nonhuman actors such as market devices and these interactions form networks called "agencement." This research is focused on three main objectives: first, to study how the performance of auctions - i.e. the interactions between auctioneers and dealers - change in the wake of new market devices in the auctions; secondly, to look into what changes artifacts bring to the social relationships between auctioneers and dealers; lastly, to analyze the influence of new market devices on auction price in the market. The results of this research are as follows. First, the appearance of new market devices generates changes in the performance of auctions, which means the change of 'agencement' of flower auctions. Direct interactions between auctioneers and dealers turned into indirect interactions through new market devices. Moreover, the changes in the agencement brought changes to the identity of auctioneers and dealers. Secondly, the new agencement caused by the inflow of new market devices formed the trust between the devices and human actors, which gave rise to the trust in electronic auction and in counterpart actors as well. In addition, new market devices lowered direct interactions between auctioneers and dealers and thus made more equal relationships between the two than before. Lastly, market devices like trading screen reduced the leverage of auctioneers by providing dealers with bidding information previously possessed by auctioneers much openly and dealers were able to decide auction prices in more reasonable and dispassionate manner. Economic agency, power, trust, price, and information in the market is material and sensory.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.352-362
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• 2019

The study investigates one of the long-standing, but still controversial issues in modern finance from the international and domestic perspectives. That is, financial components and differences on corporate profitability are identified and compared under the primary hypotheses. Empirical research settings include the sample data as KOSPI-listed chaebol firms, time reference covering the post-era of the global financial turmoil and two differently defined profitability indices measured by the market- and the book-value bases. A majority of total 7 explanatory variables except firm size and leverage ratio reveal their statistically significant power to explain profitability indices for the chaebol firms in the first hypothesis. The results are generally compatible with those obtained from their counterparts of non-chaebol firms. In the second hypothesis applying multinomial logistic model, the chaebol firms are classified into three groups according to the level of profitability. It is then confirmed that variables to represent the market-valued debt ratio, business risk and growth potential are financially discriminating factors among the three groups. The study may provide a new vision to identify financial factors of corporate profitability for Korean chaebol firms after the global financial crisis, which can enhance the benefits of interested parties at the government or corporate level in a virtuous cycle.

• International Area Studies Review
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• v.21 no.1
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• pp.163-179
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• 2017

Iran has been focused on FDI by global automobile companies after the economic sanction on Iran was removed except primary sanction. In this paper, some strategies for Korean Automobile Industry to branch out into Iran are suggested. For the purpose, Iran's automobile industry and characteristics are examined. The market situation is analyzed qualitatively and quantitatively. In passenger cars sector, Korean automobile companies would be better to wait and see the development of US-Iran relationships while exporting CKD sets of cars to Iran. It can be a good strategy, however, to put parts companies into Iran first because of Iran Government could be displeasing with exporting CKD only. FDI, licensing, and joint venture are all available for the parts companies. Motor companies can clear the regulation of auto-parts localization proportion by the method. The parts companies will be able to do key roles as supply chains after OEM branch out into Iran. It is also advisable to upgrade outpost in Iran into frontline for exporting cars to MENA area. In such a case it will be a prerequisite to develop a role-division model with facilities in East Europe. It could be called Parts first-then cars strategy. In commercial cars sector, it can be suggested to leverage natural gas as a link to branch out into Iran. Iran government wishes to develop natural gas resources. The strategy can be summarized that automobile companies carry out producing CNG buses in Iran while energy companies are drilling and producing natural gas.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Journal of Life Science
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• v.31 no.6
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• pp.587-594
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• 2021

By 2050, 70% more food will be needed to fulfill the demands of a growing population. Among the solutions, cultured meat or clean meat is presented as a sustainable alternative for consumers. Scientists have begun to leverage knowledge and tools accumulated in the fields of stem cell and tissue engineering in efforts aimed at the development of cell-based meat. Cultured meat has to recreate the complex structure of livestock muscles with a few cells. Cells start to divide after they are cultured in a culture medium, which provides nutrients, hormones, and growth factors. An initial problem with this type of culture is the serum used, as in vitro meat aims to be slaughter free. Thus, it is contradictory to use a medium made from the blood of dead calves. The serum is expensive and affects to a large extent the production cost of the meat. A positive aspect related to the safety of cultured meat is that it is not produced from animals raised in confined spaces and slaughtered in inhumane conditions. Thus, the risk of an outbreak is eliminated, and there is no need for vaccinations and animal welfare issues. The production of cultured meat is presented as environmentally friendly, as it is supposed to produce less greenhouse gas, consume less water, and use less land in comparison to conventional meat production.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.341-352
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• 2022

Medical image segmentation is the most important task in radiation therapy. Especially, when segmenting medical images, the liver is one of the most difficult organs to segment because it has various shapes and is close to other organs. Therefore, automatic segmentation of the liver in computed tomography (CT) images is a difficult task. Since tumors also have low contrast in surrounding tissues, and the shape, location, size, and number of tumors vary from patient to patient, accurate tumor segmentation takes a long time. In this study, we propose a method algorithm for automatically segmenting the liver and tumor for this purpose. As an advantage of setting the boundaries of the tumor, the liver and tumor were automatically segmented from the CT image using the 2D CoordConv DeepLab V3+ model using the CoordConv layer. For tumors, only cropped liver images were used to improve accuracy. Additionally, to increase the segmentation accuracy, augmentation, preprocess, loss function, and hyperparameter were used to find optimal values. We compared the CoordConv DeepLab v3+ model using the CoordConv layer and the DeepLab V3+ model without the CoordConv layer to determine whether they affected the segmentation accuracy. The data sets used included 131 hepatic tumor segmentation (LiTS) challenge data sets (100 train sets, 16 validation sets, and 15 test sets). Additional learned data were tested using 15 clinical data from Seoul St. Mary's Hospital. The evaluation was compared with the study results learned with a two-dimensional deep learning-based model. Dice values without the CoordConv layer achieved 0.965 ± 0.01 for liver segmentation and 0.925 ± 0.04 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.927 ± 0.02 for liver division and 0.903 ± 0.05 for tumor division. The dice values using the CoordConv layer achieved 0.989 ± 0.02 for liver segmentation and 0.937 ± 0.07 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.944 ± 0.02 for liver division and 0.916 ± 0.18 for tumor division. The use of CoordConv layers improves the segmentation accuracy. The highest of the most recently published values were 0.960 and 0.749 for liver and tumor division, respectively. However, better performance was achieved with 0.989 and 0.937 results for liver and tumor, which would have been used with the algorithm proposed in this study. The algorithm proposed in this study can play a useful role in treatment planning by improving contouring accuracy and reducing time when segmentation evaluation of liver and tumor is performed. And accurate identification of liver anatomy in medical imaging applications, such as surgical planning, as well as radiotherapy, which can leverage the findings of this study, can help clinical evaluation of the risks and benefits of liver intervention.

• Korean Association of Arts Management
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• no.53
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• pp.153-177
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• 2020

Arts organizations commonly face a range of operational challenges, from a lack of skilled workers to limited financial resources and thus are dependent on subsidies from the government. Yet, to fully realize their mission arts organizations must both develop strategies to effectively utilize government support and seek a way forward that does not depend on public subsidies. Business diversification, a strategy from corporate management, entails the expansion of products and services, and entry into new industries, enabling companies to disperse risks and increase profits. We propose that business diversification can be effectively applied to arts organization to address the myriad operational difficulties they face. To understand how an arts organization might deploy business diversification we conducted a case study of an organization that is actively pursuing the strategy: Sanwoollim Theater. We interviewed staff members of Sanwoollim including the executive director, as well as selected audiences, to understand how the business diversification model was being applied at Sanwoollim. Our findings indicate that, in a complex arts and cultural space, business diversification is a fresh and flexible new strategy that can enable private cultural arts organizations to thrive sustainably. It is also evident that government support in the initial stages of the process encourages diversification and that successful private arts organizations will leverage government subsidies into a sustainable business plan.

• Informatization Policy
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• v.29 no.3
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• pp.82-104
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• 2022

One of the interesting social phenomena in short-form video platforms is the hashtag challenge wherein ordinary users are encouraged to create by imitating short viral videos on a particular theme. Despite the increasing popularity of hashtag challenges, theoretical discussion on related user behavior is still very insufficient. In this study, we attempted to examine the impact of micro-influencers in order to understand users' willingness to participate in hashtag challenges. For this purpose, the para-social interaction theory and imitation behavior literature were adopted as key theoretical basis. In an empirical investigation using 243 survey data from TikTok users, our study found that a user's illusion of intimacy with a micro-influencer (i.e., para-social interaction) had significant positive impact on the intention to participate in a hashtag challenge. This study also showed that the degree of para-social interaction in a short-form video platform was determined by both media content-related factors and media character-related factors (i.e., content attractiveness, physical attractiveness, and attitude homophily). Our work in this study provided significant theoretical and practical implications on how to leverage micro-influencers for the success of hashtag challenges in a short-form video platform.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.