• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.453-462
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• 2021

As the frequency of localized heavy rainfall has increased during recent years, the importance of high-resolution radar data has also increased. This study aims to correct the bias of Dual Polarization radar that still has a spatial and temporal bias. In many studies, various statistical techniques have been attempted to correct the bias of radar rainfall. In this study, the bias correction of the S-band Dual Polarization radar used in flood forecasting of ME was implemented by a Convolutional Autoencoder (CAE) algorithm, which is a type of Convolutional Neural Network (CNN). The CAE model was trained based on radar data sets that have a 10-min temporal resolution for the July 2017 flood event in Cheongju. The results showed that the newly developed CAE model provided improved simulation results in time and space by reducing the bias of raw radar rainfall. Therefore, the CAE model, which learns the spatial relationship between each adjacent grid, can be used for real-time updates of grid-based climate data generated by radar and satellites.

• Korean Association of Arts Management
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• no.54
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• pp.33-57
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• 2020

This study aimed to empirically analyze the effect of the appreciation of artwork in the workplace on creativity. To this end, two virtual workspace images with and without artwork were created, and an online survey was conducted with office workers. A regression analysis was performed on the results to investigate whether and how much the appreciation and recognition of artwork was effective for the creativity. As a result, among the factors of recognition according to the appreciation of artwork, "intellectual development" and "thinking" showed positive effects on the five sub-factors of creativity at work, such as original flexibility, alternative problem-solving skills, pursuit of adventure and freedom, individual independence, and exploratory immersion. Unlike most previous studies, however, "understanding" had a negative effect on original flexibility. In conclusion, it was found that some of the factors of the appreciation and recognition of artwork had a positive effect on creativity at work. This study provides implications that the appreciation of artwork in the workplace is effective for improving creativity at work and that it is important for each company to develop a streamlined approach based on its goal of pursuing a creative environment. In addition, it is expected that this study will contribute to the widespread use of artwork sharing services at workplaces as well as encouraging more empirical studies to be done on the effect of the services.

• Journal of Family Resource Management and Policy Review
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• v.26 no.2
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• pp.71-84
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• 2022

This study analyzed 2019 time survey data from the National Statistical Office to examine the childcare behaviors of fathers with preschool children and their determinants, considering types of care and working/nonworking days. The main results of this study are as follows. First, paternal childcare time of nonworking days was three times more than that of working days, and the participation rate was also higher on nonworking days. Second, there was no significant difference in the amount of time spent on primary care and developmental care by fathers, whether from dual- or single-income families. Third, it seems that fathers adjust their participation in childcare between working days and nonworking days in consideration of the mother's time availability. Fourth, the variables related to childcare needs had a significant influence on paternal childcare time on both working and nonworking days. Fathers' developmental care time was not explained by the independent variables entered into the regression analysis. As a result of the study, it is necessary to reduce fathers' working hours and increase family-friendly systems to increase fathers' participation in childrearing. Fathers' perception of parental responsibility must also be changed.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.

• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.64-69
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• 2022

The study measured a subway construction worker's Macpa stress by Heart Rate Variability measuring instrument and conducted a survey of Korean job stress from subway construction workers. Also, the study analyzed the relationship between Macpa stress index and Korean job stress result and suggested managing stress method for each item. According to National Statistical Office data, the first line subway in Seoul was started to open in 1974. The extended total length is 996 kilometers until 2019. Many aged workers are currently working at subway construction sites due to the avoidance of young workers since the past until now. It means that the elderly has a substantial portion among subway construction workers. The productivity has been adversely affected by health problems due to the aging of workers, job stress due to heavy work, and personal health problems. So, the regulation and policies on job stress health management are being strengthened. The data were measured Macpa stress by machine measuring heart rate variability and conducted Korean job stress survey(shortened) from Sa-sang to Ha-dan line Busan subway construction workers for analyzing the relationship. Independent variable were age, job duration, job position, employment type, working type in this study. Macpa's dependent variable was stress index and Korean job stress survey(shortened)'s dependent variables were job requirements, job autonomy, relationship conflict, job instability, organizational structure, inappropriate compensation, working place culture, and total score. SPSS 12.0 K Statistics Program was used for statistical analysis. Kruskal-wallis test, a nonparametric statistical analysis, was used because the data are difficult to be assumed as normal distribution. As a result, the paper indicated the significant correlation between Macpa stress index and Korean job stress(short version). The elderly workers presented higher Macpa index and higher job stress due to aging and heavy-duty work. The majority workers were daily workers who had unstable working condition and uncertainty about the future. The study suggested a manual that could reduce job stress for subway construction workers and future study deriving management tool through analyzing job stress factor is necessary.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1044-1050
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• 2021

After the occurrence of several passenger ship accidents in Korea, various systems are being developed for passenger ship safety management. A total of 162 passenger ships operate along the coast of Korea, of which 105 (65 %) are car-ferries with open vehicle decks. The car-ferry has a navigation pattern that passes through 2 to 4 islands. Safety inspections at the departure point(home port) are carried out by the crew, the operation supervisor of the operation management office, and the maritime safety supervisor. In some cases, self-inspections are carried out for safety inspections at layovers. As with any system, there are institutional and practical limitations. To this end, this study was conducted to suggest a method of detecting a vehicle using image processing and linking it to the calculations for ship stability. For vehicle detection, a method using a difference image and one using machine learning were used. However, a limitation was observed in these methods that the vehicle could not be identified due to strong background lighting from the pier and the ship in the cases where the camera was backlit such as during sunset or at night. It appears necessary to secure sufficient image data and upgrade the program for stable image processing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.1-14
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• 2022

In this study, in order to analyze the preference for UAM of public transportation users following the introduction of urban air transportation(UAM), A preference consciousness(SP) survey was conducted on 840 users of Incheon public transportation using the Incheon International Airport-Incheon Gil Hospital route, which is the urban air traffic(UAM) demonstration route section. In addition, the means selection model was estimated based on the results of conducting a preference consciousness (SP) survey. As a result of analyzing the time value for travel using the established means selection model, it was found to be 56,428 won/hour, As a result of verifying the elasticity of travel cost (won), in-vehicle time (minutes), and out-of-vehicle time (minutes) in the model, the effect on the urban air traffic (UAM) share in this model was found that travel cost (won), in-vehicle time (minutes), and out-of-vehicle time (minutes) were in order, and the effect was greater when the travel cost (won) decreased than when it increased.

• Journal of Family Resource Management and Policy Review
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• v.27 no.1
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• pp.53-65
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• 2023

This study focused on Japanese families engaging in childrearing to discover changes in their daily lives, such as in the role division between husband and wife and hours spent on housework and childcare, caused by the unexpected crisis of COVID-19. An empirical analysis attempted to determine whether changes in the working environment, such as working and commuting hours, affected the role division between husband and wife, as well as housework and childcare hours spent. The data analyzed were extracted from the 2021 "3rd Survey on Changes in Lifestyle Awareness and Behavior Due to the Impact of COVID-19" conducted by the Japanese Cabinet Office. A total of 983 couples aged 20 or older, living with their spouse, having at least one child under the age of 18, and both employed were selected. The analysis results were as follows: First, the division of roles between husband and wife changed in the direction of increasing the husband's role in housework and childrearing. Second, the decrease in working and commuting hours increased the husband's role. Third, housework and childcare hours were more clearly related to changes in the working environments of husbands and wives than to changes in role division between husband and wife. In conclusion, changes in men's working and commuting hours had a greater impact on role division, as well as housework and childrearing hours in the family, than changes in women's working and commuting hours. In the future, an analysis that considers labor market factors is necessary.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.30-30
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• 2023

This study aims to establish the multi-reservoir operation system model in the Upper Mun River Basin which includes 5 main dams namely, Mun Bon (MB), Lamchae (LC), Lam Takhong (LTK), Lam Phraphoeng (LPP), and Lower Lam Chiengkrai (LLCK) Dams. The knowledge and AI technology were applied aiming to develop innovative prototype for SMART dam-reservoir operation in future. Two different sorts of reservoir operation system model namely, Fuzzy Logic (FL) and Constraint Programming (CP) as well as the development of rainfall and reservoir inflow prediction models using Machine Learning (ML) technique were made to help specify the right amount of daily reservoir releases for the Royal Irrigation Department (RID). The model could also provide the essential information particularly for the Office of National Water Resource of Thailand (ONWR) to determine the short-term and long-term water resource management plan and strengthen water security against flood and drought in this region. The simulated results of base case scenario for reservoir operation in the Upper Mun from 2008 to 2021 indicated that in the same circumstances, FL and CP models could specify the new release schemes to increase the reservoir water storages at the beginning of dry season of approximately 125.25 and 142.20 MCM per year. This means that supplying the agricultural water to farmers in dry season could be well managed. In other words, water scarcity problem could substantially be moderated at some extent in case of incapability to control the expansion of cultivated area size properly. Moreover, using AI technology to determine the new reservoir release schemes plays important role in reducing the actual volume of water shortfall in the basin although the drought situation at LTK and LLCK Dams were still existed in some periods of time. Meanwhile, considering the predicted inflow and hydrologic factors downstream of 5 main dams by FL model and minimizing the flood volume by CP model could ensure that flood risk was considerably minimized as a result of new release schemes.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.22-34
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• 2023

Predicting accurately the construction cost budget in the early stages of construction projects is crucial to support the client's decision-making and achieve the objectives of the construction project. This holds true for public construction projects as well. However, the current methods for predicting construction cost budgets in the early stages of public construction projects are not sophisticated enough in terms of accuracy and reliability, indicating a need for improvement. The objective of this study is to develop a construction cost budget prediction model that can be utilized in the early stages of public building projects using an artificial neural network (ANN). In this study, an artificial neural network model was developed using the SPSS Statistics program and the data provided by the Public Procurement Service. The level of construction cost budget prediction was analyzed, and the accuracy of the model was validated through additional testing. The validation results demonstrated that the developed artificial neural network model exhibited an error range for estimates that can be utilized in the early stages of projects, indicating the potential to predict construction cost budgets more accurately by incorporating various project conditions.