• Journal of Radiation Protection and Research
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• v.40 no.2
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• pp.79-86
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• 2015

$^3He$ gas has been used for neutron monitors as the neutron converter owing to its advantages such as high sensitivity, good ${\gamma}$-discrimination capability, and long-term stability. However, $^3He$ is becoming more difficult to obtain in last few years due to a global shortage of $^3He$ gas. Accordingly, the cost of a neutron monitor using $^3He$ gas as a neutron converter is becoming more expensive. Demand on a neutron monitor using an alternative neutron conversion material is widely increased. $^{10}B$ has many advantages among various $^3He$ alternative materials, as a neutron converter. In order to develop a neutron converter using $^{10}B$ (actually $B_4C$), we calculated the optimal thickness of a neutron converter with a Monte Carlo simulation using MCNP6. In addition, a neutron converter was fabricated by the Ar sputtering method and the neutron signal detection efficiencies were measured with respect to various thicknesses of fabricated a neutron converter. Also, we developed a 2-dimensional multi-wire proportional chamber (MWPC) for neutron beam profile monitoring using the fabricated a neutron converter, and performed experiments for neutron response of the neutron monitor at the 30 MW research reactor HANARO at the Korea Atomic Energy Research Institute. The 2-dimensional MWPC with boron ($B_4C$) neutron converter was proved to be useful for neutron beam monitoring, and can be applied to other types of neutron imaging.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• The Korean Journal of Ecology
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• v.26 no.1
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• pp.39-47
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• 2003

The leaf aqueous extracts from five gymnosperms plant were investigated for allelopathy with five Hibiscus syriacus varieties. The leaf aqueous extract of Pinus rigida had the highest total phenolic compound of 2.21mg/L, whereas the soil under Pinus koraiensis canopy had the highest total phenolic compound of 1.38mg/L. Fourteen phenolic compounds were isolated from five gymnosperm plants by HPLC. Among them, phenolic compounds were the highest in P. rigida (320.56 g/mg) with the primary compound 5-sulfosalicylic acid (312.55 g/mg). The correlation between leaf total phenolic compound and pH was not significant, while the total phenolic compound of the leaf extract changed soil pH. The relative seed germination of H. syriacus varieties showed 25% was threshold concentration. The germination rates of varieties were similar to the control group or showed slight stimulation to treatment of P. koraiensis extract. H. syriacus Cambanha was similar to the control group or showed stimulation in all treated groups. H. syriacus Seohohyang showed stimulation in both root and shoot growth compared to the control group. In other varieties except Seohohyang, shoot growth was similar to the control group, while root growth was stimulated in all treated groups. The extracts of tested gymnosperms showed significantly more stimulation to transplanted Seohohyang seedlings, whereas others were similar to control or inhibited in the greenhouse. The dry weight of Seohohyang was greater in all treated groups than the control group, while other varieties were inhibited. All gymnosperm extracts stimulated the chlorophyll contents of Seohohyang and H. syriacus Koyoro but other varieties were not significantly affected. Accordingly, it is suggested that Seohohyang seems the most desirable when planted within these five gymnosperms.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• Journal of Practical Agriculture & Fisheries Research
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• v.16 no.1
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• pp.37-53
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• 2014

The purpose of this research is to find what difficulties the agricultural successors, the Korea National College of Agriculture and Fisheries (KNCAF) graduates, face with in implementing eco-friendly agriculture in Jeju, and what solutions they can be provided with. This research, a case study on the basis of open-ended survey questions, has 6 cases out of 8 graduates who have or had implemented eco-friendly citrus farming. In Jeju, 24 graduates have involved in citrus farming. According to the case study, only one case was environment-friendly farming method at the pesticide-free level, and the others at organic farming level. All the cases have tried to alter main crops or to diversify management for coping with global climate change and market-opening. On analyzing operating cost to gain product of merchantable quality, it revealed that the environment-friendly farming method needs much more managing efforts than the conventional farming does. But to the contrary, the materials cost in the environment-friendly farming method was lower than in the conventional farming method. In the total production and the price, the environment-friendly farming was 20~50% lower and 10~50% higher than the conventional farming, respectively. Difficulties which the graduates confronted with in implementing the environment-friendly agriculture are as below. Firstly, many of the difficulties have resulted from lack of the environment-friendly farming techniques, and the high cost of farm scale improvement due to high price of land and topographical features of Jeju. Secondly, the agricultural successors, the KNCAF graduates, have trouble in obtaining approval of their parents to changeover from the conventional farming to the environment-friendly farming. Lastly, there is no advisory organizations and experts for environment-friendly farming in the given area. For shift to the environment-friendly farming, followings are needed. Agricultural Technology & Extension center, with cooperation of leading farms in environment-friendly farming, should have a key role in offering education and consults on the environment-friendly farming techniques. Also, this organization should inform rapidly the research results to the farmers, and their feed-back should be involved in the next research. Therefore, it is suggested that the forum called 'Environment-friendly Organic Farming Forum in Jeju' tentatively is organized.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.109-124
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• 2021

This study intends to find out the influence of a consumer's shopping style on impulse buying mediated by sales promotion, based on low-cost cosmetic goods. For the study, pleasure, economical, and convenience shopping styles were set as the independent variables, and impulse buying was set as the dependent variable, and as the mediating variable between the two, sales promotions such as price discount event, free giveaway event, and visit-inducing activity were reviewed. Accordingly, the influence relation of shopping style, sales promotion, and impulse buying were reviewed with hierarchical regression analysis to examine the mutual influence relation. The data for this study employed a structured questionnaire, and 230 copies were collected against men and women in their 20s-30s, who are the main consumers of low-cost cosmetic goods, and 197 faithful responses were analyzed, and the major findings from the analysis results are as follows. First, pleasure-style consumers were found to have influenced impulse buying, while economical-style consumers were found to have a negative influence, and convenience-style was found to have no significant relation. Second, as for the examination of the mediating effect of sales promotion, price discount event, free giveaway event, and visit-inducing activity were found to have a partial mediating effect on the influence of pleasure shopping style on impulse buying, and did not fulfill the economical shopping style mediating effect condition. Also, as convenience shopping style was found to be insignificant towards impulse buying, it was excluded from the mediating effect. Such result is thought to be a useful elementary material for establishing a sales promotion strategy according to shopping styles through the analysis of styles of major consumers in order to increase the sales of businesses. The theoretical and pragmatic implications of such study results were discussed and the future study directions were suggested.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• Archives of design research
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• v.19 no.5 s.67
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• pp.203-212
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• 2006

With interests and participation in the costumeplay that mimics characters appeared on carton or animation in recent days, the costumeplay becomes one of cultural phenomena. Using a qualitative research method, this study identified costumeplayers' costume consumption pattern and explored its meanings from the perspective of consumption culture. Indeed, this study intended to help for understanding costumeplayer group as a consumer, and to provide basic knowledge about new market analysis related to fashion design and marketing. The results from the analyzing participant observation and in-depth interviews data are as follows: first, costumeplayers usually begin costumeplay by friends' invitations or by themselves and then continue on participating. Through the costumeplay, participants have benefits such as fun, departure from the daily life, and social interaction. Second, participants acquire costumes through purchase, rent, producing or combination of daily wear, but both purchase and rent account high. Third, the meanings of consumption culture in costumeplay include consumption behavior repeating possession and disposal. Also, costumeplayers concerns efficiency when purchasing or renting the costumes, and internet is a place where information search, comparison, and actual purchasing are occurred. Based on the results, fashion design and marketing implication, limitation of this study and further research ideas were suggested.

• Korean Journal of Food Science and Technology
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• v.47 no.3
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• pp.336-344
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• 2015

This study was conducted to investigate changes in the physicochemical, antioxidant, and sensory properties of burdock during 9 repeated rounds of steaming ($90^{\circ}C$, 3 h) and drying ($60^{\circ}C$, 20 h) procedures. The moisture content decreased from 81.95% to 7.64% as the process was repeated. Fresh burdock showed the highest total sugar content, with 518.35 mg/g of soluble sugar, 86% being inulin. The reducing sugar content was the greatest (377.00 mg/g) in burdock that had been processed 3 times. The brown color continuously intensified, reaching its peak at 7 rounds of processing, and then weakened. Crude saponin content was the highest (6.17%) after the 5th processing. Polyphenol content and antioxidant activity (DPPH, ABTS, FRAP) were the highest at the 3rd and 5th procedures, respectively. Repeated processing weakened the grass and root odors and the bitter, astringent, and metallic tastes, whereas it strengthened the sweet and savory odors, caramel flavor, and richness.

• Journal of the Korean Geographical Society
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• v.28 no.4
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• pp.379-391
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• 1993

Present-day world economy is characterized by : technology nationalism, economic regionalism, market protectionism, multinational corporations, efc. All nations are striving for intensifying national economic rivalry and seeking after their own interests above everything else. Many regions of the world are also forming trading blocs, which could negatively affect nonmember states. The ultimate way to meet these difficulties is to establish production facilities in the countries imposing trade regulations. However, as the existing models of direct forrign investment (DFI) do not account for the particular nature of Korean firm's DFI activities, a new point of departure is imperative. It is because of this that Korean firms have only limited firm-specific advantages, the basic precondition of extant DFI theories, compared with their developed counterparts.