• Horticultural Science & Technology
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• v.32 no.6
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• pp.819-826
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• 2014

This study was carried out to investigate the effects of night-time far-infrared irradiance quality of red pepper dried in greenhouses. This study involved 4 treatments: sunlight alone (control), or sunlight plus nightly far-infrared irradiation at $250W{\cdot}6.6m^{-2}$ ($250W{\cdot}6.6m^{-2}$), far-infrared irradiation at $250W{\cdot}3.3m^{-2}$ ($250W{\cdot}3.3m^{-2}$), or far-infrared irradiation $500W{\cdot}3.3m^{-2}$ ($500W{\cdot}3.3m^{-2}$). The drying periods were 12 days in $500W{\cdot}3.3m^{-2}$ and $250W{\cdot}3.3m^{-2}$, and 14 days in $250W{\cdot}6.6m^{-2}$, and 15 days in the control. The daytime temperature was same among the treatments. The lowest temperature was at $23.8^{\circ}C$ in control, and $29.5-37.2^{\circ}C$ in far-infrared irradiation treatments. The marketable yield was 7-14% higher in far-infrared irradiation treatments compared to the control. The rate of marketability was higher in far-infrared irradiation treatments (93.6-96.3%) than in the control (87.0-87.5%). The American Spice Trade Association (ASTA) value was greatest in the $250W{\cdot}3.3m^{-2}$ treatment, followed by $250W{\cdot}6.6m^{-2}$, then $500W{\cdot}3.3m^{-2}$, and finally the control. Capsaicinoid content showed no regular trend among the treatments. Our results provide an optimized method for reducing drying time of red pepper under sunlight, and improving the quality of dried red pepper.

• Korean Journal of Heritage: History & Science
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• v.45 no.3
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• pp.114-159
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• 2012

The Northern Song Dynasty Period (北宋時代) was a drastic transitional era in all aspect of Chinese society including the politico-economic system, ideology and cultural trait. These changes that began in the late Tang (唐) Dynasty Period accomplished in the Northern Song Dynasty. In this phase, the fundamental change influenced in all institutional area; and among them, the capital city planning and its associating building technology to pile stone walls shows one of the significant change of those time. Based on the geographical factor, confluences of many rivers, the Kaifeing (開封) area where the BianLieng palace had developed as a political and economical centre since the Tang Dynasty when the Grand Canal was constructed. According to archaeological researches, the central city structure of Dongjing Walled Town was begun to plan in the late Tang Dynasty and formed in Five Dynasties. The fundamental functional change of city completed in the Midnorthern Song Dynasty. In spite of the relatively late beginning of archaeological investigations to Kaifeng Walled Town and Dongjing Walled Town due to unfavourable natural environment, excavations inaugurated since 1981 have achieved the significant investigations including the actual measurement and excavation to the outer wall, the preliminary excavation to the inner city area, the investigation and excavation to the royal palace of Song and the survey to the royal palace of King Zho in the Ming (明) Dynasty. These surveys have provide important data to reconstruct the 변량 palace, and elucidate the characteristics of city plan in the Dongjing Walled Town and the institutional change of capital city plan of the Northern Song Dynasty. The basic layout of Dongjing Walled Town reflect the realisation of ideality of the late Chinese medieval capital city structure that establish the commercial and economic centre based on the intensification of emperor's power by means of the organisation of ethical institution and the development of commercial economy. Firstly, the central place of the Kaifeng area is encircled with triple walls. This emphasise the authority of emperor located on the summit in the hierarchical ethic system succeeding to the main capital city plan of the late phase of ancient China. Secondly, the location of Dongjing Walled Town was decided by the transport network and the commercial function and defence function. Thirdly, this site shows the change of city structure and landscape of the Northern Song Dynasty. The closed Fengri (坊里: block) system transferred the open Jiexiang (街巷: road) system. Fourthly, the capital city was characterised by the free market trade and the diversification of market place. Fifthly, a convenient transport network in the Bian River, a centre of the Grand Canals, enabled to construct the Kaifeng Walled Town. Therefore, the Northern Song Dynasty continuously accomplished the developed water system as concerning about the utilisation of waterways after the construction of city.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.5
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• pp.572-580
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• 2019

In view of the numerous discussions on global environmental issues, policies have been implemented to limit emissions in the field of marine transport, which accounts for a major part of international trade. In this study, a ship's emissions were calculated by applying the engine load factor to determine the total quantity of emissions based on the ship's speed reduction. For ships entering and leaving the Busan North Port from 1 January to 31 December 2017, emissions were calculated and analyzed based on the ship's type and its speed in the reduced speed zone (RSZ), which was set to 20 nautical miles. The comparison of the total amount of emissions under all situations, such as cruising, maneuvering, and hotelling modes revealed that the vessels that generated the most emissions were container ships at 76.1 %, general cargo ships at 7.2 %, and passenger ships at 6.8 %. In the cruising and maneuvering modes, general cargo ships discharged a lesser amount of emission in comparison with passenger ships; however, in the hotelling mode, the general cargo ships discharged a larger amount of emission than passenger ships. The total emissions of nitrogen oxides (NOx), sulphur oxides (SOx), particulate matter (PM), and volatile organic compounds (VOC), were 49.4 %, 45 %, 4 %, and 1.6 %, respectively. Furthermore, the amounts of emission were compared when ships navigated at their average service speed, 12, 10, and 8 knots in the RSZ, respectively. At 12 knots, the reduction in emissions was more than that of the ships navigating at their average service speed by 39 % in NOx, 40 % in VOC, 42 % in PM, and 38 % in Sox. At 10 knots, the emission reductions were 52 %, 54 %, 56 %, and 50 % in NOx, VOC, PM, and Sox, respectively. At 8 knots, the emission reductions were 62 %, 64 %, 67 %, and 59 % in NOx, VOC, PM, and Sox, respectively. As a result, the emissions were ef ectively reduced when there was a reduction in the ship's speed. Therefore, it is necessary to consider limiting the speed of ships entering and leaving the port to decrease the total quantity of emissions.

• Journal of the Korean Society of International Agriculture
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• v.30 no.4
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• pp.328-338
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• 2018

Central Asia (Uzbekistan, Kazakhstan, Kyrgyzstan) is a hot and arid continental climate, with most areas (68%) consisting of barren vegetation, desert, and meadows. The main agricultural areas for crop production include irrigated farmland, non-irrigated farmland, grassland, prairie and mountain. We are experiencing climate change with recent climate variability increasing. Agriculture is one of major economic sectors and provides a means of livings for the rural population of Central Asia, especially the poor. In the past two decades, Central Asia has experienced a high population growth rate, with Kazakhstan at 16.8%, Uzbekistan at 34.5% and Kyrgyzstan at 28.4%. As a major industry, Kazakhstan has the largest share of exports of agricultural products followed by petroleum, mineral resources, steel, and chemicals. Uzbekistan is the fifth largest cotton exporter as well as the sixth largest cotton producer in the world. Kyrgyzstan exports ores, stones, cultured pearls, and minerals. These three countries are rich in mineral resources, agricultural products, and energy resources. However, not only do they have difficulties in economic development due to the weakness of logistics and industrial infrastructure, but they also have imperceptible cooperation and investment among countries due to insufficient research and development. Through this study, we will investigate national outlook, economic indicators, major agricultural products, import and export status, and agricultural technology cooperation status, and study how Korean agricultural industry advances into these countries through SWOT analysis. Through this, we hope to contribute to the basic data of Central Asian studies and cooperation and investment in agriculture in each country. In addition, in order to increase cooperative exchange and investment in these countries, we will prepare a Central Asia logistics hub for the rapidly changing interKorean railroad era.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.757-772
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• 2019

The purpose of this paper is to measure the clustering change and analyze empirical results, and choose the clustering ports for Busan, Incheon, and Gwangyang ports by using Artificial Neural Network, Social Network, and Tabu Search models on 38 Asian container ports over the period 2007-2016. The models consider number of cranes, depth, birth length, and total area as inputs and container throughput as output. Followings are the main empirical results. First, the variables ranking order which affects the clustering according to artificial neural network are TEU, birth length, depth, total area, and number of cranes. Second, social network analysis shows the same clustering in the benevolent and aggressive models. Third, the efficiency of domestic ports are worsened after clustering using social network analysis and tabu search models. Forth, social network and tabu search models can increase the efficiency by 37% compared to that of the general CCR model. Fifth, according to the social network analysis and tabu search models, 3 Korean ports could be clustered with Asian ports like Busan Port(Kobe, Osaka, Port Klang, Tanjung Pelepas, and Manila), Incheon Port(Shahid Rajaee, and Gwangyang), and Gwangyang Port(Aqaba, Port Sulatan Qaboos, Dammam, Khor Fakkan, and Incheon). Korean seaport authority should introduce port improvement plans by using the methods used in this paper.

• Korea Science and Art Forum
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• v.30
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• pp.151-161
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• 2017

In the fields of science and engineering, multidisciplinary research is common, and researchers with a diverse range of expertise collaborate to achieve common goals. As the 4th industrial revolution gains currency in society, there is growing demand on talented personnel both with technical knowledge and skills and with communicative skills. That is, future engineers are expected to possess competence in social and artistic skills in addition to specialized knowledge and skills in engineering. In this paper we introduce an emerging field of 3D bioprinting as an exemplary case of interdisciplinary research. We have chosen the case to demonstrate the possibility of cultivating engineers with π-shaped expertise. Building on the concept of T-shaped talent, we define π-shaped expertise as having both technical skills in more than one specialized field and interpersonal/communicative skills. Wtih references to such concepts as trading zones and interactional expertise, we suggest that π-shaped expertise can be cultivated via the creation of multi-level trading zones. Trading zones are referred to as the physical, conceptual, or metaphorical spaces in which experts with different world views trade ideas, objects, and the like. Interactional expertise is cultivated, as interactions between researches are under way, with growing understanding of each other's expertise. Under the support of the university and the government, two researchers with expertise in printing technology and life sciences cooperate to develop a 3D bioprinting system. And the primary investigator of the research laboratory under study has aimed to create multiple dimensions of trading zones where researchers with different educational and cultural backgrounds can exchange ideas and interact with each other. As 3D bioprinting has taken shape, we have found that a new form of expertise, namely π-shaped expertise is formed.

• Journal of Venture Innovation
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• v.4 no.2
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• pp.57-75
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• 2021

KHNP's shared growth activities are based on such public good. Reflecting the characteristics of a comprehensive energy company, a high-tech plant company, and a leading company for shared growth, it presents strategies to link performance indicators with its partners and implements various measures. Key tasks include maintaining the nuclear power plant ecosystem, improving management conditions for partner companies, strengthening future capabilities of the nuclear power plant industry, and supporting a virtuous cycle of regional development. This is made by reflecting the specificity of nuclear power generation as much as possible, and is designed to reflect the spirit of shared growth through win-win and cooperation in order to solve the challenges of the times while considering the characteristics as much as possible as possible. KHNP's shared growth activities can be said to be the practice of the spirit of the times(Zeitgeist). The spirit of the times given to us now is that companies should strive for sustainable growth as social air. KHNP has been striving to establish a creative and leading shared growth ecosystem. In particular, considering the positions of partners, it has been promoting continuous system improvement to establish a fair trade culture and deregulation. In addition, it has continuously discovered and implemented new customized support projects that are effective for partner companies and local communities. To this end, efforts have been made for shared growth through organic collaboration with partners and stakeholders. As detailed tasks, it also presents fostering new markets and new industries, maintaining supply chains, and emergency support for COVID-19 to maintain the nuclear power plant ecosystem. This reflects the social public good after the recent COVID-19 incident. In order to improve the management conditions of partner companies, productivity improvement, human resources enhancement, and customized funding are being implemented as detailed tasks. This is a plan to practice win-win growth with partner companies emphasized by corporate social responsibility (CSR) and ISO 26000 while being faithful to the main job. Until now, ESG management has focused on the environmental field to cope with the catastrophe of climate change. According to KHNP is presenting a public enterprise-type model in the environmental field. In order to strengthen the future capabilities of the nuclear power plant industry as a state-of-the-art energy company, it has set tasks to attract investment from partner companies, localization and new technologies R&D, and commercialization of innovative technologies. This is an effort to develop advanced nuclear power plant technology as a concrete practical measure of eco-friendly development. Meanwhile, the EU is preparing a social taxonomy to focus on the social sector, another important axis in ESG management, following the Green Taxonomy, a classification system in the environmental sector. KHNP includes enhancing local vitality, increasing income for the underprivileged, and overcoming the COVID-19 crisis as part of its shared growth activities, which is a representative social taxonomy field. The draft social taxonomy being promoted by the EU was announced in July, and the contents promoted by KHNP are consistent with this, leading the practice of social taxonomy

• Journal of Venture Innovation
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• v.6 no.4
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• pp.23-40
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• 2023

This study examines the customer value factors affecting the intention to continue using the mobile app of department stores, which are traditional offline retailers, in the retail industry that is rapidly digitalizing and becoming mobile. This study clarifies multidimensional customer value in three dimensions; functional, convenience, and social. Functional value refers to the integrated channel, and consistent customer experience provided between channels in the omnichannel retail environment, while convenience value is the convenience of saving time and effort save while customers use a mobile app. Social value refers to the improvement of social approval or social self-concept occurring due to the use of products or services related to green marketing within the mobile app of the department store. The influence of each on the dependent variable, the mobile app's continuous use intention, was analyzed by using the three dimensions of customer value as independent variables. Data was collected from customers who have a history of using the mobile app of Shinsegae Department Store in Korea, and a confirmatory analysis was conducted using Smart PLS 4.0. The analysis results showed that all three dimensions of customer value; functional value, convenience value, and social value, had a positive (+) influence on customers' intention to continue using the mobile app, and the influence of functional value had the greatest impact. As functional value appears to be the most important influencing factor due to the omnichannel retail trend by advancement of technology, it suggests that it is important for department stores, and offline retailers, to provide integrated channels. This provides insights into the direction of customer-centered strategy formulation for activating department store mobile apps and suggests basic analytical data for customized services and marketing activities that department stores can effectively meet the changing expectations and demands of customers through new mobile channels rather than existing offline channels.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.