• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.383-396
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• 2022

The purpose of this study was to investigate the effect of worker's innovative organizational culture on organizational commitment in companies. The research data were used from the fourth year (2011) to the seventh year (2017) of the HCCP (Human Capital Corporate Panel), and 207 companies were subject to the final analysis except for missing values. To statistically validate the research model, we utilize the SPSS 26.0 and AMOS 21.0 programs to perform Latent Growth Modeling analysis. The results of the study are as follows. First, both the innovative organizational culture and organizational commitment of companies have been shown to continue to decline over time. Second, the initial value of innovative organizational culture has been shown to have a significant impact on the initial value of organizational commitment. On the other hand, the initial value of innovative organizational culture has no significant effect on the rate of change in organizational commitment. Third, the rate of change in innovative organizational culture has been shown to have a significant impact on the rate of change in organizational commitment. Based on these findings, we present practical measures to enhance the importance of innovative organizational culture along with its implications.

• Journal of Industrial Convergence
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• v.20 no.3
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• pp.89-97
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• 2022

The purpose of this study is to understand the effect of volunteer activities and self-development activities of elementary school students on subjective happiness and to verify the mediating effect of leadership life skills based on this. The subjects were surveyed in 2018 by the Korea Youth Policy Institute, and out of 2,739 elementary school students, unresponsive and missing values were removed and the final 1,978 were selected. As a result of the analysis, it was found that leadership life skills were completely mediated in the relationship between volunteer work and subjective happiness of elementary school students. For the analysis method, SPSS 25.0 program was used to achieve the purpose of this study. And to verify the mediating effect, a 3-step mediating effect analysis was performed by Baron & Keeny(1986). It was confirmed that leadership life skills were completely mediated in the relationship between self-development activities and subjective happiness. Based on these research results, in order to increase the subjective happiness of elementary school students, measures were discussed to develop practical skills that can improve communication and organizational management skills, which are leadership life skills.

• Korean Journal of Psychosomatic Medicine
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• v.30 no.1
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• pp.46-53
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• 2022

Objectives : This study aimed to assess the possible association of dental pain with suicidal ideation among adolescents by analysing data from the 2018 Korean Youth Risk Behavior Survey, a nationwide online survey. Methods : Of 62,823 adolescent middle and high school students in Korea, 60,040 participants were selected for analysis, after excluding cases with missing values. Participants were given a questionnaire about their self-evaluation of health including dental pain and suicidal ideation. Logistic regression analysis demonstrated the relationships between dental pain and suicidal ideation after controlling for potential confounding factors. Results : The proportion of Korean adolescents reporting suicidal ideation was 13.3%. The proportion of adolescents who experienced dental pain was 23.4%. Compared to adolescents who did not report dental pain, adolescents who reported experiencing dental pain were significantly more likely to experience suicidal ideation (OR=1.94, p<0.001). In two multivariate models, the relationships between dental pain and suicidal ideation (AOR=1.24, p<0.001) were statistically significant. Conclusions : Dental pain was associated with increased risk of suicidal ideation among Korean adolescents, even when controlling for sociodemographic factors and other health conditions. It is necessary to consider screening adolescent patients who present with dental pain for suicidal ideation.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.60-70
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• 2021

The purpose of this study was to promote an understanding of suicidal ideation and identify the factors that influence suicide ideation according to the life cycle of Korean adults. This study was a secondary analysis study using the Korea Health Panel 2016 data. Among adults over 19 years of age, 14,538 people with no missing values in suicidal ideation and influencing factors were classified into young adults (19-39 years old), middle-aged adults (40-64 years old), and the elderly (65 years or over). The data were analyzed by multiple logistic regression. The results showed that 2.9% (108 people) of young adults, 3.2% (181 people) of middle-aged adults, and 3.7% (80 people) of the elderly had suicide ideation over the past year. Factors influencing suicidal ideation by life cycle were anxiety, depression, drug use and quality of life for young adults, subjective health status, stress, anxiety, depression, drug use and quality of life for middle-aged adults. The factors affecting the elderly were body mass index, stress, anxiety, depression, and quality of life. Therefore, factors influencing suicidal ideation should be considered as a major factor for screening risk groups according to the life cycle, and differentiated intervention programs should be developed and provided to prevent and manage suicide in risk groups.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.47-56
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• 2021

This study a secondary data analysis study attempted to identify the factors influencing the successful aging of the elderly in Korea. Using the data of the 7th Aging Research Panel in 2018, 4,106 people over 65 years of age who had at least one non-living child and no missing values in the study variables were enrolled. Data were analyzed by frequency analysis, crossover analysis, independent sample t-test, and binary logistic regression analysis. The results of this study revealed that the factors affecting successful aging among elderly included age, the presence or absence of a spouse, education level, housing type, subjective health, exercise, alcohol drinking, and non-face-to-face contact frequency with non-cohabiting children, and the explanatory power of the variables was 24.1%. In order for the elderly to achieve successful aging, centering on child ties, the frequency of non-face-to-face contact, which can comfort the elderly's life and increase the satisfaction of life in a continuous relationship, is more important than having children live close and meet frequently. Based on this study, various strategies are needed for the successful aging of elderly people who are socially isolated due to concerns about COVID-19 infection.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• The Korean Journal of Franchise Management
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• v.13 no.2
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• pp.17-31
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• 2022

Purpose: With the development of the Internet, consumers can quickly access the electronic word-of-mouth. Consumers seek to reduce uncertainty by referring to the opinions of other consumers about products and services when making purchase decisions. In the food service industry, evaluating a restaurant before an actual visitation is difficult. Therefore, electronic word-of-mouth is important to interact with the customer in restaurants. as it can be used as an exchange of information in which consumers participate and interact with other customers. This study was conducted to verify how online word-of-mouth characteristics (Consensus, Vividness, Neutrality) on attitudes and visit intention from the perspective of social exchange theory. And it was performed to verify the structural relationship between short-term visit intention, mid-term visit and long-term visit intention. Research design, data, and methodology: A survey was conducted on customers who have visited restaurants. Of a total of 312 responses, 306 responses were used, excluding insincere responses and missing values for factors analysis. SPSS 25.0 and AMOS 25.0 were used for statistical analysis, and hypothesis testing was conducted after verifying the validity and reliability of the questionnaire items. Result: The result of the analysis showed that, consensus and neutrality have a positive effect on attitude but not much on vividness. In addition, consensus, vividness, and neutrality have no effect on the short-term visit intention. Finally, the short-term visit intention has a positive effect on mid-term visit intention, and mid-term visit intention has a positive effect on long-term visit intention. Conclusions: Based on the results, this study suggested that it is necessary to have practical implications for marketing and monitoring restaurant reviews in consideration of the characteristics of electronic word-of-mouth. When managing electronic-word-of-mouth, it is necessary to manage the consensus and neutrality is essential to provide sufficient information about the restaurant. The focus should not only be on vividness, such as photos and videos. In addition, restaurants should also provide a good experience for first-time visitors as the short-term visit intention positively affects mid-term and long-term visit intention.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.9-24
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• 2022

Currently, a vast amount of hydrologic data is accumulated in real-time through automatic water level measuring instruments in agricultural reservoirs. At the same time, false and missing data points are also increasing. The applicability and reliability of quality control of hydrological data must be secured for efficient agricultural water management through calculation of water supply and disaster management. Considering the characteristics of irregularities in hydrological data caused by irrigation water usage and rainfall pattern, the Korea Rural Community Corporation is currently applying the Hampel filter as a water level data quality management method. This method uses window size as a key parameter, and if window size is large, distortion of data may occur and if window size is small, many outliers are not removed which reduces the reliability of the corrected data. Thus, selection of the optimal window size for individual reservoir is required. To ensure reliability, we compared and analyzed the RMSE (Root Mean Square Error) and NSE (Nash-Sutcliffe model efficiency coefficient) of the corrected data and the daily water level of the RIMS (Rural Infrastructure Management System) data, and the automatic outlier detection standards used by the Ministry of Environment. To select the optimal window size, we used the classification performance evaluation index of the error matrix and the rainfall data of the irrigation period, showing the optimal values at 3 h. The efficient reservoir automatic calibration technique can reduce manpower and time required for manual calibration, and is expected to improve the reliability of water level data and the value of water resources.