• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.963-969
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• 2021

The wind data measured from local meteorological masts is used to evaluate wind speed distribution and energy production in the specified site for wind farm However, wind data measured from meteorological masts often contain missing information or insufficient desired height or data length, making it difficult to perform wind turbine control and performance simulation. Therefore, long-term continuous wind data is very important to assess the annual energy production and the capacity factor for wind turbines or wind farms. In addition, if seasonal influences are distinct, such as on the Korean Peninsula, wind data with seasonal characteristics should be considered. This study presents methodologies for generating synthetic wind that take into account fluctuations in both wind speed and direction using the hidden Markov model, which is a statistical method. The wind data for statistical processing are measured at Maldo island in the Kokunnsan-gundo, Jeonbuk Province using the Automatic Weather System (AWS) of the Korea Meteorological Administration. The synthetic wind generated using the hidden Markov model will be validated by comparing statistical variables, wind energy density, seasonal mean speed, and prevailing wind direction with measurement data.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.953-966
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• 2022

Since aerosols adversely affect human health, such as deteriorating air quality, quantitative observation of the distribution and characteristics of aerosols is essential. Recently, satellite-based Aerosol Optical Depth (AOD) data is used in various studies as periodic and quantitative information acquisition means on the global scale, but optical sensor-based satellite AOD images are missing in some areas with cloud conditions. In this study, we produced gap-free GeoKompsat 2A (GK-2A) Advanced Meteorological Imager (AMI) AOD hourly images after generating a Random Forest based gap-filling model using grid meteorological and geographic elements as input variables. The accuracy of the model is Mean Bias Error (MBE) of -0.002 and Root Mean Square Error (RMSE) of 0.145, which is higher than the target accuracy of the original data and considering that the target object is an atmospheric variable with Correlation Coefficient (CC) of 0.714, it is a model with sufficient explanatory power. The high temporal resolution of geostationary satellites is suitable for diurnal variation observation and is an important model for other research such as input for atmospheric correction, estimation of ground PM, analysis of small fires or pollutants.

• Journal of the Korean Applied Science and Technology
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• v.38 no.2
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• pp.543-550
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• 2021

The purpose of this study was to verify the mediating effect of the change of grit in the relationship that the change of exercise time of adolescents leads to the change of self-esteem. The data of 2,438 students (male = 1,327, female = 1,111) were used for the analysis, excluding cases including missing values in the data from the wave 1 and wave 2 of the Korean Children and Youth Panel 2018. The standardized residual score generated through regression analysis from the values in wave 1 to the values in wave 2 was used as a variable for the amount of change. As a results, the correlation between the amount of change in all three variables was found to be significant. Result of the hierarchical regression analysis revealed that it was confirmed that the partial mediating effect that the change of exercise time directly or indirectly affects the change of self-esteem through the change of grit is significant. These results indicate that adolescents, exercise time is gradually decreasing as the grade goes up, and this has an effect on the reduction of grit and self-esteem.

• Journal of People, Plants, and Environment
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• v.24 no.1
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• pp.17-27
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• 2021

Background and objective: This study was conducted as part of research to promote garden diversity and seek sustainable garden management plans, as well as to determine the trends in understanding and use of companion plants as an eco-friendly farming method and provide the results as the basic data for sustainable urban agriculture. Methods: To determine the trends in garden activities, eco-friendly pest control, and use of companion plants, a survey was conducted on 230 urban residents participating in the Urban Agriculture Expert course. 223 copies of the questionnaire were collected excluding missing values, and IBM SPSS statistics Ver. 25 Program was used for frequency analysis, descriptive statistics, and regression analysis. Results: Most of the respondents were female (71.3%), homemakers (26.5%), were in their 50s (29.1%), and had 2 members in the family (27.8%). 164 respondents (73.5%) had experience in gardening, most of them once a week (31.7%) and for self-consumption (55.5%). Both men and women raised crops for safe food production (32.3%), and they most preferred the city garden type (39.9%). For the preparation of nourishment for eco-friendly garden management, most respondents (60.1%) purchased fertilizers from the market. For the reason why eco-friendly pest control is necessary, all respondents except 4 of them (98.2%) responded that it is necessary 'because it affects my health as I eat it (73.5%)', indicating that they still had a high level of interest in health. Only 43.9% of the respondents said that they had heard of companion plants, 89.2% responded that companion plants were effective in eco-friendly management, and 87.4% showed the will to participate in gardening using companion plants in the future. Finally, the regression analysis confirmed that the awareness of companion plants and satisfaction with gardening activities are key variables that increase the intention to participate in gardening activities in the future. Conclusion: Since plants require special care depending on the period and various diseases and insect pests occur, there must be continuous research on companion plants as an eco-friendly farming method. Moreover, by actively using companion plants in urban gardens with the utility value in not only eco-friendly pest control but also in helping plant growth, urban agriculture is expected to be continuously activated and promoted by increasing satisfaction in gardening activities with aesthetic landscaping and pest control.

• Korea Journal of Hospital Management
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• v.29 no.1
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• pp.46-55
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• 2024

Objective: This study was conducted to provide basic data for the establishment of effective health policies for the unmet medical experience that may occur among the elderly depending on whether they live in a singleperson household or not. Methodology: This study used data from the 8th National Health and Nutrition Examination Survey (2019-2020) and excluded cases with missing values in variables for the total number of respondent participants of 15,469. Finally, 2,850 subjects aged 65 or older were selected for final analysis. This study examined the relationship between experiences of unmet medical needs, attempting to confirm the relationship between single-person households and unmet medical needs through subgroup analysis considering gender, age, and household income. Results: According to the results, in the case of single-person households, the odds ratio (OR) for unmet medical needs was significantly higher at 1.60 times (95% CI: 1.16-2.21). Upon conducting subgroup analyses for gender, age, and household income quintiles, the OR was significantly higher at 2.24 times (95% CI: 1.14-4.41) for males and 1.48 times (95% CI: 1.02-2.14) for females, statistically significant in both cases. For individuals aged 65-69, the OR was significantly higher at 1.90 times (95% CI: 1.04-3.47), but for those aged 70-74 and over 75, it was not statistically significant. In the case of households with 'low' income, the OR was higher at 1.62 times (95% CI: 1.16-2.26), and for 'middle' income, it was significantly higher at 3.21 times (95% CI: 1.08-9.51). Conclusion: This study confirmed that the experience of unmet medical care is high among men who make up single-person households and low-income seniors. Therefore, this study suggests that policies to expand medical services and support welfare for single-person households should be established to resolve these problems, showing that health policies that take into account individual and regional characteristics are needed to improve medical accessibility for single-person households.

• Journal of the Health Care and Life Science
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• v.10 no.2
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• pp.305-314
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• 2022

The purpose of this study is to investigate the effect of health behavioral factors such as general characteristics, lifestyle and disease characteristics on depression in patients with chronic diseases. To this end, among 7,359 people who participated in the 8th National Health and Nutrition Survey conducted from 2019 to 2020, chi-square test analysis between health behavior factors and depression for 1980 people aged 19 years or older with chronic diseases and no missing values in the basic survey items was performed. After that, binary logistic regression analysis was performed with the factors that were significant as independent variables. As a result of the analysis, depression was 1.49 times higher in women than men (CI: 1.086~2.044), and was 1.828 higher in smokers than in non-smokers (CI: 1.285~2.561). And the higher the income level, the lower the depression. In particular, the odds ratio was 28.034 (CI: 13.132~59.849) in 'not stressful' versus 'very stressed', which had the greatest effect when the intensity of stress was very high. And the influence of subjective health cognition and sleeping hours was also relatively high. This study is meaningful in that it identified the priority of health behavior factors that should be practiced to improve depression in patients with chronic diseases. And since the number of comorbidity was not significant in the occurrence of depression, it would be necessary to identify the extent to which each type of chronic disease affects depression and to suggest policy alternatives tailored to each patient group.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Distribution Research
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• v.16 no.2
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• pp.57-94
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• 2011

Nowadays, it is common that most consumers are purchasing goods in e-stores. The e-stores eager to attract, revisit, retain, and finally convert them into loyal customers. The e-store marketers have planned and executed numerous marketing efforts. As one of the marketing activities, e-store managers attempt to build web sites that meet customers' functional and psychological needs. A wide array of studies has been done to identify factors that could affect customers' response of web sites. Majority of studies were conducted to verify technology-related and functional variables of the website which facilitate transactions and enhance customer responses such as purchase intention and website loyalty. However, there has been little research on the external cues of website and psychological variables of consumer that could have positive influences on customer response. The purpose of this study is to investigate the influence of e-store personality on e-store loyalty through mediating variables such as e-store identification, e-store trust, and e-store engagement. The authors of this study develop the model and set up the six main hypotheses and a set of sub-hypotheses based on a literature review, shown in