• Journal of Climate Change Research
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• v.3 no.2
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• pp.117-128
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• 2012

The Government of South Korea has continued its effort to fixate virtuous circle of economic growth and climate change response to cope with international demands and pressure to commitment for greenhouse gas reduction effectively. Nationally, Korean Government has established "Enforcement of the Framework Act on Low carbon, Green Growth"(2010. 4. 13) to implement national mid-term GHG mitigation goal(30% reduction by 2020 compare to BAU), which established the foundation for phased GHG mitigation by setting up the sectoral and industrial goal, adopting GHG and Energy Target Management System. Also, follow-up measures are taken such as planning and control of mid-term and short-term mitigation target by detailed analysis of potential mitigation of sector and industry, building up the infrastructure for periodic and systematic analysis of target management. Likewise, it is required to establish more accurate, reliable and detailed sectoral GHG inventory for successfully establishment and implement the frame act. In comparison to the $CO_2$ emission, Especially fluorinated greenhouse gases (HFCs, PFCs, $SF_6$) are lacking research to build the greenhouse gas inventories to identify emissions sources and collection of the applicable collection activities data. In this study, with the refrigeration and air conditioning sector being used to fluorine refrigerant(HFCs) as the center, greenhouse gas emission estimation methodology for evaluating the feasibility of using this methodology look over and mobile air conditioning, fixed air conditioning, household refrigeration equipment, commercial refrigeration equipment for the greenhouse gas emissions were calculated. First look at in terms of methodology, refrigeration and air conditioning sector GHG emissions in developing country-specific emission factors and activity data of the industrial sector the construction of the DB is not enough, it's 2006 IPCC Guidelines Tier 2a (emission factor approach) rather than the Tier 2b (mass balance approach) deems appropriate, and each detail by process, sectoral activity data more accurate, if DB is built Tier 2a (emission factor approach) can be applied will also be judged. Refrigeration and air conditioning sector in 2009 due to the use of refrigerant greenhouse gas emissions ($CO_2eq.$) assessment results, portable air conditioner 1,974,646 ton to year, fixed-mount air conditioner 1,011,754 ton to year, household refrigeration unit 4,396 ton to year, commercial refrigeration equipment 1,263 ton to year was estimated to total 2,992,037 tons.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.613-621
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• 2019

The purpose of this study is to evaluate the effectiveness of supportive devices which are for minimizing the patient's movement during lower extremity angiography and to verify image quality of phantom by analyzing of Mask image, DSA image and Roadmap image into SNR and CNR. As a result of comparing SNR with CNR of mask image obtained by DSA technique using the phantom alone and phantom placed on the supportive devices, there was no significant difference between about 0~0.06 for SNR and about 0~0.003 for CNR. The study showed about 0.11~0.35 for SNR and 0.016~0.031 for CNR of DSA imaging by DSA technique about only water phantom of the blood vessel model and the water phantom placed on the device. Analyzing SNR and CNR of Roadmap technique about water phantom on the auxiliary device (hardboard paper, pomax, polycarbonate, acrylic) and water phantom alone, there was no significant difference between 0.02~0.05 for SNR and 0.002~0.004 for CNR. In conclusion, there was no significant difference on image quality by using supportive devices made by hardboard paper, pomax, polycarbonate or acryl regardless of whether using supportive devices or not. Supportive devices to minimize of the patient's movement may reduce the total amount of contrast, exam-time, radiation exposure and eliminate risk factors during angiogram. Supportive devices made by hardboard paper can be applied easily during angiogram due to advantages of reasonable price and simple processing. It is considered that will be useful to consider cost efficiency and types of materials and their properties in accordance with purpose and method of the study when the operator makes and uses supportive devices.

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

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

• Journal of Life Science
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• v.29 no.8
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• pp.861-870
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• 2019

This study was undertaken to establish optimum medium compositions for cost-effective mannitol production by Leuconostoc mesenteroides SRCM201425 isolated from kimchi. L. mesenteroides SRCM21425 from kimchi was selected for efficient mannitol production based on fructose analysis and identified by its 16S rRNA gene sequence, as well as by carbohydrate fermentation pattern analysis. To enhance mannitol production by L. mesenteroides SRCM201425, the effects of carbon, nitrogen, and mineral sources on mannitol production were first determined using Plackett-Burman design (PBD). The effects of 11 variables on mannitol production were investigated of which three variables, fructose, sucrose, and peptone, were selected. In the second step, each concentration of fructose, sucrose, and peptone was optimized using a central composite design (CCD) and response surface analysis. The predicted concentrations of fructose, sucrose, and peptone were 38.68 g/l, 30 g/l, and 39.67 g/l, respectively. The mathematical response model was reliable, with a coefficient of determination of $R^2=0.9185$. Mannitol production increased 20-fold as compared with the MRS medium, corresponding to a mannitol yield 97.46% when compared to MRS supplemented with 100 g/l of fructose in flask system. Furthermore, the production in the optimized medium was cost-effective. The findings of this study can be expected to be useful in biological production for catalytic hydrogenation causing byproduct and additional production costs.

• Journal of Korean Society of Disaster and Security
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• v.12 no.1
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• pp.11-21
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• 2019

This study assessed the risk of disaster by using QRE(Quick Risk Estimation - UNISDR Roll Model City of Basic Evaluation Tool) tools for three natural disasters and sixteen social disasters managed by the Seoul Metropolitan Government. The criteria for selecting 19 disaster types in Seoul are limited to disasters that occur frequently in the past and cause a lot of damage to people and property if they occur. We also considered disasters that are likely to occur in the future. According to the results of the QRE tools for disaster type in Seoul, the most dangerous type of disaster among the Seoul city disasters was "suicide accident" and "deterioration of air quality". Suicide risk is high and it is not easy to take measures against the economic and psychological problems of suicide. This corresponds to the Risk ratings(Likelihood ranking score & Severity rating) "M6". In contrast, disaster types with low risk during the disaster managed by the city of Seoul were analyzed as flooding, water leakage, and water pollution accidents. In the case of floods, there is a high likelihood of disaster such as localized heavy rains and typhoons. However, the city of Seoul has established a comprehensive plan to reduce floods and water every five years. This aspect is considered to be appropriate for disaster prevention preparedness and relatively low disaster risk was analyzed. This corresponds to the disaster Risk ratings(Likelihood ranking score & Severity rating) "VL1". Finally, the QRE tool provides the city's leaders and disaster managers with a quick reference to the risk of a disaster so that decisions can be made faster. In addition, the risk assessment using the QRE tool has helped many aspects such as systematic evaluation of resilience against the city's safety risks, basic data on future investment plans, and disaster response.

• Korean Journal of Breeding Science
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• v.50 no.4
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• pp.424-433
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• 2018

Watermelon (Citrullus lanatus) is an economically important vegetable crop all over the world, which has functional compounds such as lycopene and citrulline. Gummy stem blight caused by Didymella bryoniae is one of the most devastative diseases in watermelon. Single nucleotide polymorphisms (SNPs), which are genetic variations occurring between individuals with respect to a single base, were often used to construct genetic linkage maps and develop molecular markers linked to a variety of horticultural traits and resistance to several diseases. In this study, we developed high-resolution melting (HRM) markers based on SNPs generated from NGS resequencing of two parents in watermelon. Plant materials were C. lanatus '920533' (female and susceptible parent), C. amarus 'PI 189225' (male and resistant parent), and their $F_1$ and $F_2$ progenies. A total of 13.6 Gbp ('920533') and 13.1 Gbp ('PI 189225') of genomic sequences were obtained using NGS analysis. A total of 6.09 million SNPs between '920533' and 'PI 189225' were detected, and 354,860 SNPs were identified as potential HRM primer sets. From these, a total of 330 primer sets for HRM analysis were designed. As a result, a total of 61 HRM markers that have polymorphic melting curves were developed. These HRM markers can be used for the construction of SNP-based linkage maps and for the analysis of quantitative trait loci (QTLs) related to gummy stem blight resistance.

• Journal of Technology Innovation
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• v.28 no.3
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• pp.1-28
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• 2020

This study examines whether the firm with high innovation efficiency realizes high operating performance. We measured innovation efficiency by the ratio of patent applications for R&D expenditure or R&D stock and measured operating performance by the ratio of operating income or operating cash flow to total assets for the following year. The sample consists of 1,880 manufacturing firm-years, which listed on the Korean Exchange between 2014 and 2017. We analyze the effect of innovation efficiency on operating performance using a model of Hirshleifer et al. (2013) results show that both innovation efficiency variables have a significantly positive relationship with the total asset operating margin. Besides, the following year's performance, measured by the total asset operating cash flow ratio, also shows a positive relationship with the two innovation efficiency variables at the 5% and 1% significance levels, respectively. The results indicate that high innovation efficiency firms that link the outcomes of R&D to more patent applications realize higher operating performance. Also, we divided the R&D-intensive and non-R&D-intensive industries and performed the same analysis. As a result, the innovation efficiency has a significant positive effect on operating margin in both industries. However, the effect of innovation efficiency on the operating cash flow is only significant in R&D-intensive industries. This study suggests that the effects of innovation efficiency are more consistent in the R&D-intensive industry. Additionally, we divided the high patent application and low patent applications industries and performed the same analysis. As a result, the innovation efficiency has a significant positive effect on operating margin in both industries. This study suggests that the effects of innovation efficiency are more consistent in the high patent application industry. We show that a firm's innovation efficiency is a critical factor for a firm's performance, while prior studies on the R&D performance have not considered the innovation efficiency of each firm. The evidence suggests that firms not only consider R&D expenditures but also improve the performance of companies by increasing innovation efficiency. Investors need to consider their innovation efficiency when evaluating the value of firms.

• (The)Study of the Eastern Classic
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• no.32
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• pp.425-441
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• 2008

Most of Korea's Kyungmaedorock(auction book: 競賣圖錄) and pictorial record of exhibitions in the modern times were usually published in the 1930s. Although 1930s were periods of the Great Depression when economic slump continued because of the aftereffect of the slump in the stocks issued by the US in 1929, during this period, Japan began regular continental invasion starting from invasion of the northeastern area of China. To curio dealers, the 1930s were 'boom period of curio transaction' and in urban cultural aspects, the period is evaluated as the one when the first step of modernism was formed. Collection, photo-printing and arrangement of the data related to modern exhibitions including the Auction Book being published at that time are very important because they enable us to know characteristics of fine arts in the transition period from paintings & writings to fine arts in addition to enabling us to revert the circulation history of our paintings & writings and curios. Furthermore, these data will become important data for reconstitution of the circulation history of the Eastern Asia's modern art works. Although the pictorial record of Joseon's Exhibitions of Chinaware and Wooden Works(朝鮮陶磁木工展) is a small and thin one, it records our country's high level chinaware and wooden works. Although we can't know the exact time for 'Joseon's exhibitions of chinaware and wooden works', they are assumed to have been held in Tokyo, Japan in the 1930s and there seems to have been sale of works, too. As such, studies of the books such as the auction book and exhibitions under Japanese imperialism have the first importance in the fact that through which we can examine the course of outflow of our art works to Japan. Furthermore, they can be studies of art-sociology that examine flow and phase of recognition and taste of art works of those days. And from now on, comparative studies of auctions and exhibitions being held in Japan such as Tokyo, Osaka and etc. as well as art markets in Seoul during modern times would also be necessary.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.1
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• pp.147-159
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• 2021

After the 2020 Corona 19 pandemic, consumers' online consumption is increasing rapidly, and non-store online retail channels are showing high growth. In particular, social media is gaining its status as a social media market where direct transactions take place in the means of promoting companies' brands and products. In this study, changes in consumer behavior after the Corona 19 pandemic are different in choosing online shopping media such as existing online shopping malls and SNS markets that can be classified into open social media and closed social media when purchasing agri-food online. We tried to find out what type of product is preferred in the selection of agri-food products. For this study, demographic characteristics of consumers, perceived risk of consumers, and dietary lifestyle were set as independent variables to investigate the effect on online shopping media type and product selection. The summary of the empirical analysis results is as follows. When consumers purchase agri-food online, there are significant differences in demographic characteristics, consumer perception risks, and detailed factors of dietary lifestyle in selecting shopping channels such as online shopping malls, open social media, and closed social media. Appeared to be. The consumers who choose the open SNS market are higher in men than in women, with lower household income, and higher in consumers seeking health and taste. Consumers who choose the closed SNS market were analyzed as consumers who live in rural areas and have a high degree of risk perception for delivery. Consumers who choose existing online shopping malls have high educational background, high personal income, and high consumers seeking taste and economy. Through this study, we tried to provide practical assistance by providing a basis for judgment to farmers who have difficulty in selecting an online shopping medium suitable for their product characteristics. As a shopping channel for agri-food, social media is not a simple promotional channel, but a direct transaction. It can be differentiated from existing studies in that it is approached as a market that arises.

• Journal of Technology Innovation
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• v.30 no.2
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• pp.29-58
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• 2022

Startups lack resources and manpower to build internal capabilities to strengthen market competitiveness; external cooperation such as joint research and networking plays is important. In this study, we analyzed the effect of startups' industry-university cooperation on organizational learning capability and innovation performance. Empirical results demonstrate the mechanism by which government R&D investment strengthens organizational learning capability and creates innovative results by promoting cooperation between startups and universities. First, industry-university cooperation strengthened organizational learning capability. An empirical analysis shows that startups increase internal capabilities through external cooperation. Second, startups' organizational learning capability had a significant effect on innovation performance. We analyze how organizations with high learning capabilities positively develop corporate innovation performance by having a culture of discovery and sharing new ideas. Finally, industry-university cooperation had different effects on organizational learning capability and innovation performance according to the previous experiences of startup founders. In particular, small- and medium-sized (startup) businesses and individual-based experience groups positively affected the creation of organizational learning capabilities and innovation performance through industry-university cooperation. Small- and medium-sized businesses and individual founders have a relatively small cooperative network with the outside world compared to founders of large companies, universities, and research institutes; therefore, they strengthen organizational learning capabilities through cooperation with universities. This study demonstrates that government should create policy inducements for cooperation with universities to maximize the R&D performance of startups. Criticism exists that lending support to startups and universities will hinder innovation performance; nevertheless, government investment plays a role in expanding intangible resources such as accumulating technologies, fostering high-quality human resources, and strengthening innovation networks. Therefore, the government should appropriately utilize the its authority to strengthen investment strategies for startup growth.