• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.577-587
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• 2021

Drought is generally considered to be a natural disaster caused by accumulated water shortages over a long period of time, taking months or years and slowly occurring. However, climate change has led to rapid changes in weather and environmental factors that directly affect agriculture, and extreme weather conditions have led to an increase in the frequency of rapidly developing droughts within weeks to months. This phenomenon is defined as 'Flash Drought', which is caused by an increase in surface temperature over a relatively short period of time and abnormally low and rapidly decreasing soil moisture. The detection and analysis of flash drought is essential because it has a significant impact on agriculture and natural ecosystems, and its impacts are associated with agricultural drought impacts. In South Korea, there is no clear definition of flash drought, so the purpose of this study is to identify and analyze its characteristics. In this study, flash drought detection condition was presented based on the satellite-derived drought index Evaporative Stress Index (ESI) from 2014 to 2018. ESI is used as an early warning indicator for rapidly-occurring flash drought a short period of time due to its similar relationship with reduced soil moisture content, lack of precipitation, increased evaporative demand due to low humidity, high temperature, and strong winds. The flash droughts were analyzed using hydrometeorological characteristics by comparing Standardized Precipitation Index (SPI), soil moisture, maximum temperature, relative humidity, wind speed, and precipitation. The correlation was analyzed based on the 8 weeks prior to the occurrence of the flash drought, and in most cases, a high correlation of 0.8(-0.8) or higher(lower) was expressed for ESI and SPI, soil moisture, and maximum temperature.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.150-162
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• 2021

Companies have recently become increasingly interested in corporate entrepreneurship to establish a core employment policy in order to secure and maintain core employees and to foster an innovation-oriented organizational culture. Therefore, The purpose of this study is to empirically analyze the causal relationship between core employment policies and the team leader's the perceived internal process organizational competency and job satisfaction, and to investigate whether corporate entrepreneurship has a moderation effect. Non-financial service industries including ICT companies were subject of the study and human capital corporate panel(HCCP) was utilized. The samples were 367 team leaders from 79 domestic companies. This study also applied Multi-Level Analysis(HLM), which simultaneously analyzes variables at both the individual level and the organizational level in order to improve the accuracy of the study. According to the result of the study analysis, core employment policies have a positive(+) impact on the perceived internal process organizational competency and job satisfaction. In addition, the moderation effect of corporate entrepreneurship was verified during the process when core employment policies influence the two outcome variables. corporate entrepreneurship was found to have a moderation effect that strengthens the influence on job satisfaction, but the moderation effect on the perceived internal process organizational competency was not statistically significant. These results confirm that organizational cultural factors such as corporate entrepreneurship are important influence variables in the process of influencing individual-level variables such as core employment policies. Academically, It suggests that in order to enhance the effectiveness of company strategies in respect of human resource management such as core employment policies, it is important to foster a cultural environment that suits the industry and characteristics of the organization. In addition, it suggested that companies in need of new innovation need to utilize corporate entrepreneurship.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.179-188
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• 2022

Global warming causes climate change and increases extreme weather events worldwide, and the occurrence of heatwaves and droughts is also increasing in Korea. For the monitoring of extreme weather, various satellite data such as LST (Land Surface Temperature), TCI (Temperature Condition Index), NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and VHI (Vegetation Health Index) have been used. VHI, the combination of TCI and VCI, represents the vegetation stress affected by meteorological factors like precipitation and temperature and is frequently used to assess droughts under climate change. TCI and VCI require historical reference values for the LST and NDVI for each date and location. So, it is complicated to produce the VHI from the recent satellite GK2A (Geostationary Korea Multi-Purpose Satellite-2A). This study examined the retrieval of VHI using GK2A AMI (Advanced Meteorological Imager) by referencing the historical data from VIIRS (Visible Infrared Imaging Radiometer Suite) NDVI and LST as a proxy data. We found a close relationship between GK2A and VIIRS data needed for the retrieval of VHI. We produced the TCI, VCI, and VHI for GK2A during 2020-2021 at intervals of 8 days and carried out the interpretations of recent extreme weather events in Korea. GK2A VHI could express the changes in vegetation stress in 2020 due to various extreme weather events such as heatwaves (in March and June) and low temperatures (in April and July), and heavy rainfall (in August), while NOAA (National Oceanic and Atmospheric Administration) VHI could not well represent such characteristics. The GK2A VHI presented in this study can be utilized to monitor the vegetation stress due to heatwaves and droughts if the historical reference values of LST and NDVI can be adjusted in a more statistically significant way in the future work.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.177-196
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• 2021

In the era of the 4th industrial revolution, high technology is causing many changes in modern society and economy. Among them, changes in industries and jobs require new competencies of future human resources. As an educational alternative to these changes, maker education and design thinking methods are spreading around the world, and it is necessary to actively apply such education in university curriculum. Therefore, this study examines the effects of the maker education using the design thinking method on the learners' competencies required as future human resources and, relationship between the development of university students' entrepreneurial competencies and learners' competencies. And the purpose of this study is to contribute to the vitalization of entrepreneurship education for university students by suggesting an educational model. For this purpose, this study investigated the prior research on maker education/environment and design thinking methods to examine concepts and characteristics, and analyzed the influences between maker education/environment and design thinking methods and the development of learners' personal, social and technological capabilities. In addition, this study analyzed the relationship between learners' developed capabilities and university students' entrepreneurial capabilities, and based on the results, suggested directions and conceptual models for education that combine maker education/environment and design thinking methods. In conclusion, maker education/environment and design thinking methods in university education have a positive effect on the cognitive, social, and technological development of learners, and this has a significant relationship with the factors of personal, social, and technological dimensions of university students' entrepreneurial competency. It is analyzed that it has a positive effect on the promotion of entrepreneurship activities of university students. Therefore, it is judged that university's interest and support should be given to the vitalization of maker education using the design thinking method for university student entrepreneurship education and future human resources nurturing.

• 한국노년학
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• v.41 no.2
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• pp.197-215
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• 2021

This study aims to evaluate Person Centered Care practice and characteristics of care services in Korean long-term care facilities using Dementia Care Mapping as a tool. DCM, systematic observational evaluation tool for measuring dementia patients' QOL, was transformed into self-report rating scale. The process of transforming DCM into a scale of 34 items involves operationalization of DCM concepts and it's adaptation into Korean long-term care practices. Review by research team of Bradford university was added to maintain DCM concept and meaning in this scale. The scale with Cronbach alpha of .88 was surveyed on 343 care workers. Survey result shows PCC value practiced by them is 3.77(of 5 likert scale) and values on each categories of PCC reveal the characteristics of care in Korean facilities; attachment(4.02), comfort(3.95), inclusion(3.89), identity(3.67) and occupation(3.41). Dementia care in Korean facilities focuses on recipients'safety, comfort but lacks individualistic care and the meaningful and fulfilling occupation for patients. Looking at the organizational and individual factors influencing DCM values, the small facilities showed higher PCC values and there are no significant difference in PCC values between public and private facilities. Managers and care workers with career of 1~2 years showed higher PCC values compared to other career ranks and lengthes. This study suggests care practice should be centered on personhood of patients in long-term care facilities, for which introduction of unit care and education of PCC for service providers including support personnel are needed. DCM and Korean DCM scale developed in this study are suggested for the PCC-based assessment on care quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.69-77
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• 2022

The 3D spatial model is an analysis framework for solving urban problems and is used in various fields such as urban planning, environment, land and housing management, and disaster simulation. The utilization of drones that can capture 3D images in a short time at a low cost is increasing for the construction of 3D spatial model. In terms of building a virtual city and utilizing simulation modules, high location accuracy of aerial survey and precision of 3D spatial model function as important factors, so a method to increase the accuracy has been proposed. This study analyzed location accuracy of aerial survey and precision of 3D spatial model by each condition of aerial survey for urban areas where buildings are densely located. We selected Daerim 2-dong, Yeongdeungpo-gu, Seoul as a target area and applied shooting angle, shooting altitude, and overlap rate as conditions for the aerial survey. In this study, we calculated the location accuracy of aerial survey by analyzing the difference between an actual survey value of CPs and a predicted value of 3D spatial Model. Also, We calculated the precision of 3D spatial Model by analyzing the difference between the position of Point cloud and the 3D spatial Model (3D Mesh). As a result of this study, the location accuracy tended to be high at a relatively high rate of overlap, but the higher the rate of overlap, the lower the precision of 3D spatial model and the higher the shooting angle, the higher precision. Also, there was no significant relationship with precision. In terms of baseline-height ratio, the precision tended to be improved as the baseline-height ratio increased.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.105-117
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• 2022

This study analyzes the effects between stock returns and interest rate spread, difference between long-term and short-term interest rate through the polynomial linear regression analysis. The existing research concentrated on the business forecast through the interest rate spread focusing on the US market. The previous studies verified the interest rate spread based on the leading indicators of business forecast by moderating the period of long-term/short-term interest rates and analyzing the degree of leading. After the 7th reform of composite indices of business indicators in Korea of 2006, the interest rate spread was included in the items of composing the business leading indicators, which is utilized till today. Nevertheless, there are a few research on stock returns of each industry and interest rate spread in domestic stock market. Therefore, this study analyzed the stock returns of each industry and interest rate spread targeting Korean stock market. This study selected the long-term/short-term interest rates with high causality through the regression analysis, and then understood the correlations with each leading period and industry. To overcome the limitation of the simple linear regression analysis, polynomial linear regression analysis is used, which raised explanatory power. As a result, the high causality was verified when using differences between returns of corporate bond(AA-) without guarantee for three years by leading six months and call rate returns as interest rate spread. In addition, analyzing the stock returns of each industry, the relation between the relevant interest rate spread and returns of the automobile industry was the closest. This study is significant in the aspect of verifying the causality of interest rate spread, business forecast, and stock returns in Korea. Even though it could be limited to forecast the stock price by using only the interest rate spread, it would be working as a strong factor when it is properly utilized with other various factors.

• Journal of Life Science
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• v.32 no.11
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• pp.855-864
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• 2022

The pharmacological efficacy of Dipterocarpus tuberculatus Roxb. has been verified in only several fields including photoaging, inflammation, hepatotoxicity, acute gastritis and osseointegration. To identify the novel functions of Dipterocarpus tuberculatus Roxb. on anti-obesity, inhibitory effect on lipid accumulation and stimulatory effect on lipolysis were investigated in MDI (3-isobutyl-1-methyl-xanthine, dexamethasone, and insulin) stimulated 3T3-L1 adipocytes treated with methanol extracts of Dipterocarpus tuberculatus Roxb. (MED). Lipogenic targets, including lipid accumulation, level of lipogenic transcription factors, and expression of lipogenic regulators, were downregulated in MDI-stimulated 3T3-L1 adipocytes treated with MED without any significant cytotoxicity. Also, MED treatment inhibited the mRNA levels of adipogenic targets including peroxisome proliferator-activated receptor (PPAR)γ and CCAAT-enhancer binding protein (C/EBP) α, as well as lipogeic targets including adipocyte fatty acid binding protein 2 (aP2) and fatty acid synthetase (FAS) in MDI-stimulated 3T3-L1 adipocytes. A similar decrease patterns were detected in Oil red O stained lipid droplets of MED treated MDI-stimulated 3T3-L1 adipocytes. Furthermore, several lipolytic targets, such as cAMP concentration, concentration of free glycerol, expression level of lipases, including ATGL, perilipin and HSL, were upregulated in MDI-stimulated 3T3-L1 adipocytes treated with MED. These results show that MED has a novel role as a lipogenesis inhibitor and lipolysis stimulator in MDI-stimulated 3T3-L1 adipocytes.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.174-187
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• 2022

Purpose: This study was performed to evaluate an education program for housewives to reduce sodium intake based on the social cognitive theory. Methods: Housewives (n = 387) received 2 education sessions focused on food purchase and cooking, and completed a questionnaire on their perceptions of environmental, cognitive, and behavioral factors and the stages of behavioral change to reducing sodium intake both before and after the education program. Results: After the education program, the recognition of social efforts for sodium reduction and sodium labeling and experience with low-sodium products increased. Positive expectancies for the prevention of osteoporosis by the reduction of sodium were enhanced while the main barriers in practicing sodium reduction decreased, especially 'interrupting social relationships when dining with others', 'bad taste', 'preference for soup or stew', and 'limited knowledge and skills to practice'. In addition, cognition and nutrition knowledge related to reducing sodium intake were improved on all scores, but the effect on self-efficacy and dietary behavior was limited to only a few items. The percentage of participants in the pre-action stage (including pre-contemplation, contemplation, and preparation stages) for reducing sodium intake decreased from 43.2% before education to 21.5% after education, while that in the action stage increased from 19.6% before education to 43.5% after education (p < 0.001). The education program had the most significant impact on participants who were in the pre-action stage and showed improved scores in all sections. Conclusion: These results suggest that a customized education program for housewives could be an effective tool to reduce sodium intake by improving personal expectancies, cognition, and nutrition knowledge regarding sodium reduction and enabling a greater section of the population to move to the action stage of reducing sodium intake.