• Journal of Korean Society of Disaster and Security
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• v.15 no.1
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• pp.47-56
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• 2022

Recently, water resources and energy policies such as integrated water management and carbon neutrality are changing rapidly. There is an opinion that the value of hydropower reservoirs related to these policies should be re-evaluated. In the past, they have contributed to flood control in addition to electricity generation, such as operating at a limited water level during the flood season, but loss of power generation is inevitable with this operation. Therefore, this study introduced the concept of resilience to the hydropower generation system to minimize the power loss. A framework for evaluating the power generation performance of them was presented by defining the maximization of electricity sales as performance. Based on the current procedure of multiple operation plan, a scenario was established and simulation was performed using HEC-5. As a result of applying to the framework, it was confirmed that the power generation performance according to each scenario was evaluated as an important factor. And it was confirmed that the performance of flood control and water use could also be evaluated.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.207-220
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• 2021

The purpose of the study is to investigate the application and utility of emergency rescue education and derive limitations, improvements and development directions of university education based on the field experience of 119 emergency medical technician(EMT)s. The research subjects were six new 119 emergency medical technician(EMT)s within three years of starting their first-aid service in the field. After conducting in-depth narrative interviews, the analysis was performed using Colaizzi method. The 82 formulated meanings were derived from significant statements. From formulated meanings, 23 themes, 4 theme clusters, 2 categories were identified. The four theme clusters were 'The effectiveness of university education', 'The limitations of university education', 'The direction of improvement in educational methodology' and 'The direction of improvement in educational contents. University education has been helpful overall, but limitations are observed at the same time, suggesting that it should be developed through the improvement of educational methodologies (i.e. problem-based learning, field case review, education through role-playing, simulation education, strengthening skill ect.) and educational content (i.e. training tailored to the field, education focused on trauma or cardiac arrest, expansion of triage education in disaster management, reinforcement of education on-site safety, education on special patients, diverse guidance and faculty for different perspectives).

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.367-372
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• 2022

With the recent advent of the New Normal era, realistic technologies and non-contact technologies are receiving social attention. However, the hair styling field focuses on the direction of the hair itself, individual movements, and modeling, focusing on hair simulation. In order to create an improved practice environment and demand of the times, this study proposed a non-contact hair styling VR system. In the theoretical review, we studied the existing cases of hair cut research. Existing haircut-related research tend to be mainly focused on force-based feedback. Research on the interactive haircut work in the virtual environment as addressed in this paper has not been done yet. VR controllers capable of finger tracking the movements necessary for beauty enable selection, cutting, and rotation of beauty tools, and built a non-contact collaboration environment. As a result, we conducted two experiments for interactive hair cutting in VR. First, it is a haircut operation for synchronization using finger tracking and holding hook animation. We made position correction for accurate motion. Second, it is a real-time interactive cutting operation in a multi-user virtual collaboration environment. This made it possible for instructors and learners to communicate with each other through VR HMD built-in microphones and Photon Voice in non-contact situations.

• Journal of Korean Elementary Science Education
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• v.41 no.3
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• pp.538-552
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• 2022

This study attempted to understand the characteristics of pedagogic activities performed by pre-service elementary school teachers. To this end, it applied Algodoo simulations to analyze the actions of students and obtain educational implications for optical learning. The study's participants comprised 79 first-year students enrolled in a teacher training college. Their activities could be classified as representation reproductions, verification experiments, and inquiry experiments. Students who performed representation reproduction exercises replicated renowned and authoritative exemplars, apprehending and demonstrating their principal features through simulations. Students performing verification experiments attempted to validate previously learned optical concepts by reviewing the relevant theoretical contexts. Such students primarily conducted simple experiments. Students accomplishing inquiry experiments used simulations to explore phenomena they did not know. Some of them even investigated optical phenomena beyond the domain of general physics. The above results confirmed that free optical experiments performed using Algodoo can effectively denote starting points for learners to engage in activities at varying levels. Additionally, students require assistance from instructors in addressing queries about the application of the principles and models related to optics. This study suggests ways in which instructors should help students at each level of activity. Additionally, the paper presents examples of varying levels of inquiry-related activities available on Algodoo. It also discusses the advantages and disadvantages of performing inquiry-based activities on Algodoo and suggests ways of enhancing the learning achieved through this platform.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.161-172
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• 2023

Safety education delivered to construction workers in a lecture manner has limitations in concentration and immersion, so delivery power and interest are low. In order to improve unstable behavior through education and prevent safety accidents, it is necessary to change the paradigm to hands-on education. Purpose: Experiential safety education aims to contribute to preventing accidents for construction workers by quickly recognizing risks, improving emergency response skills, and verifying the effectiveness of pre- and post-learning. Method: Based on a survey of workers who experienced the same work environment as the actual construction site, an opinion survey on the pre- and post-safety experience education and a variable measurement tool were planned, and a research hypothesis was established. Results: The Bayesian theory and MC simulation analysis were used to analyze the structural equation model, and the change in construction worker behavior was confirmed through the intended safety (A), non-experiential education in the sub-area of anxiety (B), average, standard deviation, and minimum and maximum values. Conclusion: The effect of education and industrial accidents are reduced only when construction workers are motivated to participate.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.505-511
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• 2023

In the present study, a deep learning model was established to predict the motion response of small fishing vessels. Hydrodynamic performances were evaluated for two small fishing vessels for the dataset of deep learning model. The deep learning model of the Long Short-Term Memory (LSTM) which is one of the recurrent neural network was utilized. The input data of LSTM model consisted of time series of six(6) degrees of freedom motions and wave height and the output label was selected as the time series data of six(6) degrees of freedom motions. The hyperparameter and input window length studies were performed to optimize LSTM model. The time series motion response according to different wave direction was predicted by establised LSTM. The predicted time series motion response showed good overall agreement with the analysis results. As the length of the time series increased, differences between the predicted values and analysis results were increased, which is due to the reduced influence of long-term data in the training process. The overall error of the predicted data indicated that more than 85% of the data showed an error within 10%. The established LSTM model is expected to be utilized in monitoring and alarm systems for small fishing vessels.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.310-323
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• 2020

To treat congenital heart disease, it is important to understand the anatomical structure correctly. Three-dimensional (3D) printed models of the heart effectively demonstrate the structural features of congenital heart disease. Occasionally, the exact characteristics of complex cardiac malformations are difficult to identify on conventional computed tomography, magnetic resonance imaging, and echocardiography, and the use of 3D printed models can help overcome their limitations. Recently, 3D printed models have been used for congenital heart disease education, preoperative simulation, and decision-making processes. In addition, we will pave the way for the development of this technology in the future and discuss various aspects of its use, such as the development of surgical techniques and training of cardiac surgeons.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.