• Journal of Service Research and Studies
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• v.10 no.1
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• pp.55-67
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• 2020

Fine dust has recently become one of the greatest concerns of Korean people and has been a target of considerable efforts by governments and local governments. In the academic world, many researches have been carried out in relation to fine dust, but the research on the economic field has been relatively few. So we wanted to know how fine dust affects the economy. Big data of PM10 concentration for fine dust and fine dust theme stock price were collected for five years from 2013 to 2017. Regression analysis was performed using the linear regression model, the generalized least squares method. As a result, the change in the fine dust concentration was found to have a effect on the related theme stocks' price. When the fine dust concentration increased compared to the previous day, the fine dust theme stocks' price also showed a tendency to increase. Also, according to the analysis of stock price change from 2013 to 2017 based on fine dust theme stocks, companies with large regression coefficients were changed every year. Among them, the regression coefficients of Monalisa were repeatedly high in 2014, 2015, 2017, Samil Pharmaceutical in 2015, 2016 and 2017, and Welcron in 2016 and 2017, and the companies were judged to be sensitive to the concentration of fine dust. The companies that responded the most in the past 5 years were Wokong, Welcron, Dongsung Pharmaceutical, Samil Pharmaceutical, and Monalisa. If PM2.5 measurement data are accumulated enough, it would be meaningful to compare and analyze PM2.5 concentration with independent variables. In this study, only the fine dust concentration is used as an independent variable. However, it is expected that a more clear and well-explained result can be found by adding appropriate additional variables to increase the explanatory power.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.350-356
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• 2018

The contact force, which is the dynamic interaction between the pantograph and the catenary, is an important indicator for evaluating the current collecting quality, which is a stable power supply characteristic to the vehicle. In this study, dynamic contact force characteristics of pantograph of HEMU-430X vehicle, which is a power-distributed high-speed train test vehicle, were analyzed according to the catenary tension and compared with the analytical results using the pantograph-catenary interaction model. As a result of comparing the test results with the analytical results, it was confirmed that the average contact force and the standard deviation of the contact force, which are the main dynamic contact force characteristics, coincide relatively well. Using the analytical model, the relationship between the catenary tension and the contact force is presented according to the vehicle speed, and the optimal catenary tension for each operation speed is presented and compared with the international standard. As a result, it was found that the results obtained from the analysis are comparable to those recommended by international standards.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.5
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• pp.17-26
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• 2020

This study examined the structural effectiveness of the unbonded technique originally developed for seismic strengthening of unreinforced masonry walls on the basis of the prestressed steel bars and glass fiber (GF) grids. The masonry walls were strengthened by using individual steel bars or GF grids and their combination. Test results showed that the proposed technique was favorable in enhancing the strength, stiffness, and ductility of the masonry walls. When compared with the lateral load capacity, stiffness at the ascending branch of the lateral load-displacement curve, and energy dissipation capacity of the unstrengthened control wall, the increasing ratios were 110%, 120%, and 360%, respectively, for the walls strengthened with the individual GF grids, 140%, 130%, and 510%, respectively, for the walls strengthened with the individual steel bars, and 160%, 130%, and 840%, respectively, for the walls strengthened with the combination of steel bars and GF grids. The measured lateral load capacities of masonry walls strengthened with the developed technique were in relatively good agreement with the predictions by the equations proposed by Yang et al. Overall, the developed technique is quite promising in enhancing the seismic performance of unreinforced masonry walls.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.513-523
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• 2020

This study has derived the patents of the technology that have been filed and registered so far to investigate the trends of virtual reality(VR) experience contents technology, and analyzed them focusing on core patent technologies. The patents of Korea, USA, Japan, Europe and PCT, which were released until June 2020, were targeted, and patent search was conducted using WISDOMAIN search DB. The keywords for patent search were related to experience technology using VR, and a total of 1,013 data were obtained after creating a search formula by combining the derived keywords. Among them, a total of 65 data were extracted from the result of selecting valid patents, and a political analysis was conducted on them. Looking at the overall application trend, most of Korean patent applications accounted for, and noise patents are system-related devices to implement VR technology. The United States and Europe are focused on developing augmented reality(AR) technology, the study found. The technology of VR experience has increased rapidly since 2017, and the technology growth stage is the period from the beginning to the growth stage. As a result of examining the valid patents related to VR experience, technology was searched in various fields such as rural tour, exhibition, education, and performance, and patents for contents writing and general virtual experience related technology were also searched. If we predict the possibility of development of VR industry in the future, it is necessary to respond to preemption of intellectual property rights by proceeding technology development and patent application for more diverse fields.

• Journal of Industrial Convergence
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• v.21 no.2
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• pp.133-138
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• 2023

Due to social phenomena such as rapid aging in Korea, nuclear familyization, single marriage, and low birth rate, the number of Companion animals and the number of households with Companion animals are increasing due to the increase in single-person households. In fact, one out of every four households has a pet, and the scale of the industry is expected to reach 6 trillion won in 2027. In particular, in a situation where the Companion animal cosmetics market is in the spotlight amid the diversification of the pet industry, there is a great lack of research on related research and industry development methods. Accordingly, this study attempted to search and analyze academic data, patented technologies, and the latest data related to pet cosmetics and provide them as basic data for the Companion animal cosmetics industry, and the results are as follows. Academic data included verification of the effectiveness of natural materials to improve the skin condition of dogs, analysis of the pet cosmetics industry, and research on ICT-converged pet cosmetics, and the industry was mainly cleaning cosmetics, with pet shampoo launches in Amorepacific, LG Household & Healthcare, and Aekyung. In the patented technology for pets, a patent has been registered for natural product material composition and formulation ratio for skin moisturizing, skin improvement, thinning, and inflammation symptom relief. As a result of this award, it was confirmed that research and development are still insufficient compared to the consumption demand of the pet cosmetics market, and it is believed that industry analysis and development research in related fields should be actively carried out.

• Information Systems Review
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• v.19 no.3
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• pp.201-228
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• 2017

Vulnerability information, which can cause serious damage to information assets, has become a valuable commodity, thereby leading to the creation of a vulnerability market. Vulnerability information is traded on the vulnerability market from several hundred dollars to hundreds of thousands of dollars depending on its severity and importance, and the types and scope of the vulnerability markets are varying. Based on previous studies on vulnerability markets and hackers, this study empirically analyzed the effects of the security researcher's vulnerability research motivation on his/her vulnerability market use intention. The results are discussed as follows. First, vulnerability research self-efficacy had a significant effect on flow and on white and black market use intention but not on perceived benefit. Second, flow had a significant effect on perceived benefit and on black market use intention but had no effect on white market use intention. Third, perceived profit had a significant effect on white and black market use intention. Fourth, vulnerability research self-efficacy had a significant effect on perceived benefit through flow. Fifth, flow had a significant effect on white and black market use intention through perceived profit. These findings can be used to predict the behavior of security researchers who have experience in exploiting vulnerabilities.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• The Korean Journal of Pesticide Science
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• v.10 no.2
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• pp.124-130
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• 2006

Rice plant extracts of brown planthopper (BPH) resistant rice varieties, Jangseongbyeo (JSB) and Hwacheongbyeo (HCB) at different growth stages (seedling, tillering, heading and ripening) were sequentially fractioned using hexane, ethyl ether, ethyl acetate, butanol, and distilled water. The extracts were applied to BPH susceptible rice variety, Dongjjnbyeo (DJB), to investigate the insecticidal and repellent effects against BPH. BPH insecticidal effects were not clearly observed with almost all of the extract fractions obtained from both JSB and HCB varieties for 12 h, whereas the ethyl ether and hexane extract fractions showed about 10 to 30% of BPH mortality in 24 to 48 h of application periods. An effective BPH repellent activity was found with the applications of ethyl ether extract fractions obtained from JSB variety. The extract fractions obtained from HCB variety did not show any different repellence among the various fractions. The BPH repellent effects of the extract fractions obtained at different growth stages of either JSB or HCB varieties did not show any correlations. The effect of ethyl ether fraction on BPH repellent was continually increased by 30 h after treatment and thereafter decreased. In addition, the first sub-fraction separated by a flash column chromatography eluted with chloroform:methanol (9:1, v/v) from the BPH effective ethyl ether faction in JSB variety might be meaningful to repel BPH from BPH susceptible target rice plants. The results indicated that the ethyl ether fraction obtained from JSB was higher in repellent activity than in insecticidal activity, and suggesting that there might be specific substance(s) in the first sub-fraction (sF1) of the ethyl ether fraction in JSB that could provide repellent activity against BPH.