• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.627-635
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• 2019

In Korea, snow damage has been happened in the region with no snowfalls in history. Also, casual damage was caused by heavy snow. Therefore, policy about the Natural Disaster Reduction Comprehensive Plan has been changed to include the mitigation measures of snow damage. However, since heavy snow damage was not frequent, studies on snowfall have not been conducted in different points. The characteristics of snow data commonly are not same to the rainfall data. For example, some parts of the southern coastal areas are snowless during the year, so there is often no values or zero values among the annual maximum daily snow accumulation. The characteristics of this type of data is similar to the censored data. Indeed, Busan observation sites have more than 36% of no data or zero data. Despite of the different characteristics, the frequency analysis for snow data has been implemented according to the procedures for rainfall data. The frequency analysis could be implemented in both way to include the zero data or exclude the zero data. The fitness of both results would not be high enough to represent the real data shape. Therefore, in this study, a methodology for selecting a probability density function was suggested considering the characteristics of snow data in Korea. A method to select probability density function using conditional joint probability distribution was proposed. As a result, fitness from the proposed method was higher than the conventional methods. This shows that the conventional methods (includes 0 or excludes 0) overestimated snow depth. The results of this study can affect the design standards of buildings and also contribute to the establishment of measures to reduce snow damage.

• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.221-233
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• 2019

In Korea, small and medium sized domestic enterprises(SMEs) play an pivotal role in the national economy, accounting for 99.9% of all enterprises, 87.9% of total employment, and 48.3% of production. and SMEs was driving a real force of the development of national economy in many respects such as innovation, job creation, industrial diversity, balanced regional development. Despite their crucial role in the national development, most of SMEs suffer from a lack of R&D capabilities and equipments as well as funding capacity. Public R&D institutes can provide SMEs with valuable supplementary technological knowledge and help them build technological capacity. so, In order to effectively support SMEs, government and public R&D institutes must be a priority to know about the factors influencing the performance related to technology transfer and technological collaborations. In particular, SMEs are not only taking up a large portion of the national economy, but also their influence in politics and economy so strong that raising the competitiveness of small and medium-sized companies is a national policy goal that must be achieved in order to achieve sustained economic growth. For this reason, it is necessary to look specifically at the relationship between concepts such as the environment, strategy, and organizational culture surrounding the enterprise to enhance the competitiveness of SMEs. The paper analyzes 665 companies to find out which organizational culture affects their performance by classification and type of business of SMEs. This study demonstrated that when SMEs seek consistency in their external environment, strategies, and organizational structure to maintain their continued competitiveness. According to three-way analysis of variance (3-way ANOVA) indicates that classification of industries in SMEs has statistically significant main effects, but the type of business and organizational culture do not have significant effects. However, the company's organizational performance (operating profit) of SMES were found to differ significantly in comparison between groups according to classification standards of industries, and therefore adopted some parts. In addition, an analysis of the effect of interaction between the three independent variables of small and medium-sized enterprises has shown that there are statistically significant interaction effects among classification, types of business, and organizational cultures. The results shows that there is an organizational culture suitable for each industry classification and type of business of an entity, and is expected to be used as a basis for establishing promotion policies related to the incubation and commerciality of small and medium-sized venture companies in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.51-78
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• 2021

Followed by 'start-up', the theme of 'scale-up' has been considered as an important agenda in both corporate and policy spheres. In particular, although it is a term commonly used in industry and policy fields, even a conceptual definition has not been achieved from the academic perspective. "Corporate Growth" in the academic aspect and "Business Growth" in the practical management field have different understandings (Achtenhagen et al., 2010). Previous research on corporate growth has not departed from Penrose(1959)'s "Firm as a bundle of resources" and "the role of managers". Based on the theory and background of economics, existing research has mainly examined factors that contribute to firms' growth and their growth patterns. Comparatively, we lack knowledge on the firms' growth with a focus on 'annual revenue growth rate'. In the early stage of the firms, they tend to exhibit a high growth rate as it started with a lower level of annual revenue. However, when the firms reach annual revenue of more than 100 billion KRW, a threshold to be classified as a 'middle-standing enterprise' by Korean standards, they are unlikely to reach a high level of revenue growth rate. In our study, we used our sample of 333 companies (6.7% out of 5,000 'fastest-growing' companies) which reached 15% of the compound annual growth rate in the last three years with more than USD 100 million. It shows that sustaining 'high-growth' above a certain firm size is difficult. The study focuses on firms with annual revenue of more than $100 billion (approximately 120 billion KRW) from the 'Inc. 2020 fast-growing companies 5,000' list. The companies have been categorized into 1) Fast-growing companies (revenue CAGR 15%~40% between 2016 and 2019), 2) Hyper-growing companies (40%~99.9%), and 3) Super-growing (100% or more) with in-depth analysis of each group's characteristics. Also, the relationship between the revenue growth rate, individual company's strategy choice (market orientation, generic strategy, growth strategy, pioneer strategy), industry/market environment, and firm age is investigated with a quantitative approach. Through conducting the study, it aims to provide a reference to the 'Hyper-Growing Model' that combines the paths and factors of growth strategies. For policymakers, our study intends to provide a reference to which factors or environmental variables should be considered for 'optimal effective combinations' to promote firms' growth.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.419-431
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• 2021

In Seoul, it has been confirmed that the duration of rainfall is shortened and the frequency and intensity of heavy rains are increasing with a changing climate. In addition, due to high population density and urbanization in most areas, floods frequently occur in flood-prone areas for the increase in impermeable areas. Furthermore, the Seoul City is pursuing various projects such as structural and non-structural measures to resolve flood-prone areas. A disaster prevention performance target was set in consideration of the climate change impact of future precipitation, and this study conducted to reduce the overall flood damage in Seoul for the long-term. In this study, 29 GCMs with RCP4.5 and RCP8.5 scenarios were used for spatial and temporal disaggregation, and we also considered for 3 research periods, which is short-term (2006-2040, P1), mid-term (2041-2070, P2), and long-term (2071-2100, P3), respectively. For spatial downscaling, daily data of GCM was processed through Quantile Mapping based on the rainfall of the Seoul station managed by the Korea Meteorological Administration and for temporal downscaling, daily data were downscaled to hourly data through k-nearest neighbor resampling and nonparametric temporal detailing techniques using genetic algorithms. Through temporal downscaling, 100 detailed scenarios were calculated for each GCM scenario, and the IDF curve was calculated based on a total of 2,900 detailed scenarios, and by averaging this, the change in the future extreme rainfall was calculated. As a result, it was confirmed that the probability of rainfall for a duration of 100 years and a duration of 1 hour increased by 8 to 16% in the RCP4.5 scenario, and increased by 7 to 26% in the RCP8.5 scenario. Based on the results of this study, the amount of rainfall designed to prepare for future climate change in Seoul was estimated and if can be used to establish purpose-wise water related disaster prevention policies.

• Knowledge Management Research
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• v.22 no.4
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• pp.103-118
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• 2021

Gross Domestic Product(GDP) is affected by the economic power of each industry. Therefore, using statistical data related to the food and drug industry, we tried to determine the proportion of GDP and analyzed the impact of the food, medical & drug industry on the domestic economy through comparison with other industries. The food, medical & drug industry has a wide range of industries among domestic industries and is closely related to the lives of the people. In addition, human lifespan is increasing, and recently, due to the spread of an infectious disease called COVID-19, the bio sector belonging to the food, medical & drug industry is in the spotlight. Attention is needed to the industry as the competitiveness of the food, medical & drug industry is expected to increase. The Ministry of Food and Drug Safety provides statistics on the food, medical & drug industry, but does not provide a systematic share of GDP. Since it is difficult to determine how influential the industry is compared to other industries, this study attempts to obtain the share of GDP in the food, medical & drug industry and compare it with other industries. In the process of obtaining GDP in the food, medical & drug industry sector, there was a difficulty in that the figures in statistical data were not unified by time point. In order to overcome the limitations, statistical data as a standard are determined. The GDP of the Food, Medical & Drug Industry was estimated using total added value, production, sales, and added value by industry. Compared to other industries, the Food, Medical & Drug Industry's GDP ranked second after the GDP of the manufacturing industry. As a result, it suggests that the food, medical & drug industry has a great influence on the national economic power among domestic industries.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.182-193
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• 2022

Purpose: By measuring and evaluating the risk of biodiesel through non-volatile residue (NVR) and flash point and explosion limit measurement at a specific temperature according to ASTM test standards, the risk of chemical fire causative substances is identified and a universal evaluation method By derivation and securing the risk-related data of the material, it can be used for the identification and analysis of the cause of the fire, and it can be applied to the risk assessment of other chemical substances Method: In order to measure the risk of biodiesel, it was measured using the non-volatile residue(NVR) measurement method, which measures how much flammable liquid is generated at a specific temperature. Heating was tested by applying KS M 5000: 2009 Test Method 4111. In addition, the flash point was measured using the method specified in ASTM E659-782005, and the energy supply method was measured using the constant temperature method. In addition, the explosion limit measurement was conducted in accordance with ASTM E 681-04 「Standard test method for concentration limits of flammability of chemicals(Vapors and gases)」 test standard. Result: As a result of checking the amount of combustible liquid by the non-volatile residue (NVR)measurement method, the non-volatile residue(NVR) of general diesel when left at 105±2℃ for 3 hours was about 30% (70% of volatile matter) and about 4% of biodiesel. In addition, similar results were obtained for the non-volatile residue(NVR)heating temperature of 150±2℃, 3 hours and 200±2℃ for 1 hour, and white smoke was generated at 200℃ or higher. In addition, similar values were obtained as a result of experimentally checking the explosion (combustion) limits of general diesel, general diesel containing 20% biodiesel, and 100% biodiesel. Therefore, it was confirmed that the flammability risk did not significantly affect the explosion risk. Conclusion: The results of this study suggested the risk judgment criteria for mixtures through experimental research on flammable mixtures for the purpose of securing the effectiveness, reliability, and reproducibility of the details of the criteria for determining dangerous substances in the existing Dangerous Materials Safety Management Act. It will be possible to provide reference data for the judgment criteria for flammable liquids that are regulated in the field. In addition, if the know-how for each test method is accumulated through this study, it is expected that it will be used as basic data in the research on risk assessment of dangerous substances and as a basis for research on the determination of dangerous substances.

• Analytical Science and Technology
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• v.34 no.6
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• pp.259-266
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• 2021

Cannabis (Marijuana) is one of the most widely used drugs in the world, and its distribution has been controlled in South Korea since 1976. Identification of 11-nor-Δ⁹-carboxy-tetrahydrocannabinol (THCCOOH) in urine can provide important proof of cannabis use, and it is considered scientific evidence in the forensic field. In this study, we describe a simultaneous quantitative method for identifying THCCOOH and THCCOOH-glucuronide in urine, using simple liquid-liquid extraction (LLE), and liquid chromatography-tandem mass spectrometry (LC-MS/MS). THCCOOH-D₃ and THCCOOH-glucuronide-D₃ were used as internal standards. Validation results of the matrix effect, as well as recovery, linearity, precision, accuracy, process efficiency, and stability were all satisfactory. No carryover, endogenous or exogenous interferences were observed. The limit of detection (LOD) of THCCOOH and THCCOOH-glucuronide were 0.3 and 0.2 ng/mL, respectively. The developed method was applied to 28 authentic human urine samples that tested positive in immunoassay screening and gas chromatography/mass spectrometry (GC/MS) tests. The ranges of concentrations of THCCOOH and THCCOOH-glucuronide in the samples were less than LOQ~266.90 ng/mL and 6.43~2133.03 ng/mL, respectively. The concentrations of THCCOOH-glucuronide were higher than those of THCCOOH in all samples. This method can be effectively and successfully applied for the confirmation of cannabinoid use in human urine samples in the forensic field.

• Analytical Science and Technology
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• v.22 no.6
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• pp.488-497
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• 2009

In this study, analytical methodology for several organic fatty acids (OFA: propionic acid (PA), butyric acid (BA), isovaleric acid (IA), and valeric acid (VA)) designated as new offensive odorants in Korea (as of year 2010) was investigated along with some odorous VOCs (styrene, toluene, xylene, methyl ethyl ketone, methyl isobutyl ketone, butyl acetate, and isobutyl alcohol). For this purpose, working standards (WS) containing all of these 13 compounds were loaded into adsorption tube filled with Tenax TA, and analyzed by gas chromatography (GC) system thermal desorber interfaced with. The analytical sensitivities of organic fatty acids expressed in terms of detection limit (both in absolute mass (ng) and concentration (ppb)) were lower by 1.5-2 times than other compounds (PA: 0.24 ng (0.16 ppb), BA: 0.19 ng (0.11 ppb), IA: 0.15 ng (0.07 ppb), and VA: 0.28 ng (0.13 ppb)). The precision of BA, IA, and VA, if assessed in terms of relative standard error (RSE), maintained above 5%, while the precison of other compounds were below 5%. The reproducibility of analysis improved with the aid of internal standard calibration (PA: $1.1{\pm}0.4%$, BA: $10{\pm}0.46$, IA; $12{\pm}0.3%$, VA: $4{\pm}0.1%$), respectively. The results of this study showed that organic fatty acid can be analyzed using adsorption tube and thermal desorber in a more reliable way to replace alkali absorption method introduced in the odor prevention law of the Korea Ministry of Environment (KMOE).

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.203-207
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• 2010

Purpose: Hepatitis B is infection caused by Hepatitis B virus (HBV). Currently, there are several methods, Kits and equipments for conducting Hepatitis B test. Due to ununiformed methods, it would cause some differences. To manage these differences, it needs process evaluating function of test system and reagent using particular standard substance. The aim of this study is to investigate tendency of RIA method's reagent used in Asan Medical Center through comparing several other test reagents using national standard substance. Materials and Methods: The standard substance in National Institute of Food and Drug Safety Evaluation's biology medicine consists of 5 things, 4 antigens and 1 antibody. We tested reagents using A, B company's Kits according to each test method. All tests are measured repeatedly to obtain accurate results. Results: Test result of "HBs Ag Mixed titer Performance panel" is obtained match rate compared S/CO unit standard with RIA method and EIA 3 reagents, CIA 2 reagents is that company A's reagent is 94.4% (17/18), 83.3% (15/18), B is 88.9% (16/18), 77.8% (14/18). Test result of "HBs Ag Low titer Performance panel" is obtain that EIA 2 reagents is shown 7 posive results, CIA 3 reagents is 11, and RIA method's company A's reagent is 3, B is 2 of 13 in low panel. "HBV surface antigen 86.76 IU/vial" tested dilution. A is obtain positive results to 600 times(0.14 IU/mL), B is 300 times (0.29 IU/mL). Case of "HBV human immunoglobulin 95.45 IU/vial", A is shown positive result to 10,000 times (9.5 mIU/mL) and B is 4,000 times (24 mIU/mL). Test result of "HBs Ag Working Standards 0.02~11.52 IU/mL" is shown that Company A's kit concentration level was 0.38IU/mL, company B was 2.23 IU/mL and higher level of concentration was positive results. Conclusion: When comparing various test reagents and RIA method according to National Standard substances for Hepatitis B test, we recognized that there were no significant trends between reagents. For hepatitis B virus antigen-antibody titers even in parts of the test up to 600 times the antigen, antibodies to 10,000 times the maximum positive results could be obtained. Therefore, we confirmed that results from Asan Medical Center are performed smoothly by reagents and system for hepatitis B virus test.