• Journal of the Korean GEO-environmental Society
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• v.23 no.6
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• pp.19-23
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• 2022

For the substantiation and verification of studies related to the construction of a levee using riverbed soil, real-scale levee construction and experimental studies are essential. One of the most important factors in the experimental study is the reproducibility of the multiple levees with the same initial conditions. Quantitative analysis of the reproducibility should be presented. In this study, a number of physical properties (specific gravity test, sieving test, liquid-plastic limit test, compaction test, on-site Density test) for multiple embankments built with fine-grained bed soil was obtained. The collected data then used to obtain the possibility of reproducing levee through statistical analysis to suggest a process of indicating a numeric initial condition of the real-scale test. As a result of statistical analysis to verify the aforementioned process, it was confirmed that it was possible to quantitatively evaluate the reproducibility of the construction under the same conditions of embankments. This is expected to be a basic data for a full-scale embankment test using riverbed soil including other soil based real-scale tests.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.609-625
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• 2021

It has now been more than a year since the start of the COVID-19 pandemic in Korea, which has claimed thousands of lives and changed every aspect of life. The corona pandemic not only caused physical damages but also psychological one which is a collective social stress phenomenon often termed as 'corona blue'. The purpose of this study is to examine how empathy affects anxiety and depression through risk perception and indirect trauma, which are psychological variables related to the corona pandemic as a disaster. The survey data from 214 people were analyzed with a structural equation modelling. The results shows that 53.3 % of the participants experienced anxiety and 35.7% suffered from depression, which were about 6 times higher than ones from the 2019 government data. Affective empathy had a significant effect on risk perception, and cognitive empathy had a significant effect on indirect trauma. Risk perception and indirect trauma both had a significant effect on anxiety, and anxiety had a significant impact on depression. Only cognitive empathy had a significant indirect effect on anxiety and depression. This study provides an important insight into understanding a social phenomenon of 'corona blue' from a empathic perspective.

• Explosives and Blasting
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• v.40 no.3
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• pp.19-32
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• 2022

Hydrogen is a fuel having the highest energy compared with other common fuels. This means hydrogen is a clean energy source for the future. However, using hydrogen as a fuel has implication regarding carrier and storage issues, as hydrogen is highly inflammable and unstable gas susceptible to explosion. Explosions resulting from hydrogen-air mixtures have already been encountered and well documented in research experiments. However, there are still large gaps in this research field as the use of numerical tools and field experiments are required to fully understand the safety measures necessary to prevent hydrogen explosions. The purpose of this present study is to develop and simulate 3D numerical modelling of an existing hydrogen gas station in Jeonju by using handheld LiDAR and Ansys AUTODYN, as well as the processing of point cloud scans and use of cloud dataset to develop FEM 3D meshed model for the numerical simulation to predict peak-over pressures. The results show that the Lidar scanning technique combined with the ANSYS AUTODYN can help to determine the safety distance and as well as construct, simulate and predict the peak over-pressures for hydrogen refueling station explosions.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.175-183
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• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.2
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• pp.113-120
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• 2023

This paper introduces a simulation method for the collision between roadblocks and vehicles using LS-DYNA. The need to evaluate the performance of anti-ram barriers to prepare for vehicle impact has increased since vehicle impact threats have been included as a design criterion for nuclear power plants. Anti-ram barriers are typically certified for their performance through collision experiments. However, because Koreas has no performance testing facilities for anti-ram barriers, their performance can only be verified through simulations. LS-DYNA is a specialized program for collision simulation. Various organizations, including NCAC, distributes numerical models that have been validated for their accuracy with collision tests. In this study, we constructed a finite element model of the most critical vehicle barrier module and simulated collision between roadblocks and vehicles. The calculated results were verified by applying the validation criteria for vehicle safety facility collision simulations of NCHRP 179.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.84-93
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• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• Design Convergence Study
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• v.16 no.5
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• pp.1-20
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• 2017

Cardio-cerebrovascular disease is one of the chronic diseases that often attack people in Korea, and in fact, it ranks second in terms of death rate. This disease can be prevented by improving lifestyle, usual health care is important. But, in Korea most of the prevention or management programs adopt passive methods like using guide books or giving lectures, so it is not very effective in preventing the disease. Presently, the smart health care market is being developed in Korea and overseas. As an example, quantified self is being spread through wearable devices which are intended to measure each individual's health conditions and quantify body data into numbers for bettering habits. Accordingly, this author will explore and discuss wearable health care devices so as to prevent and manage cardio-cerebrovascular disease in a more active way. First, this study has classified wearable health care devices presently commercialized or related with cardio-cerebrovascular disease into wrist, clothes, or attaching types by the way of their attachment and analyzed them. After that, summing that up, this author performed cross-tabulations with other ways of preventing cardio-cerebrovascular disease. This will contribute to improving one's health care behavior about disease more actively and also work as an active interdisciplinary mechanism in research dealing with how to prevent disease afterwards.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.3
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• pp.155-163
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• 2023

Real-time monitoring technology is critical for ensuring the safety and reliability of nuclear power plant structures. However, the current seismic monitoring system has limited system identification capabilities such as modal parameter estimation. To obtain global behavior data and dynamic characteristics, multiple sensors must be optimally placed. Although several studies on optimal sensor placement have been conducted, they have primarily focused on civil and mechanical structures. Nuclear power plant structures require robust signals, even at low signal-to-noise ratios, and the robustness of each mode must be assessed separately. This is because the mode contributions of nuclear power plant containment buildings are concentrated in low-order modes. Therefore, this study proposes an optimal sensor placement methodology that can evaluate robustness against noise and the effects of each mode. Indicators, such as auto modal assurance criterion (MAC), cross MAC, and mode shape distribution by node were analyzed, and the suitability of the methodology was verified through numerical analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.236-238
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• 2022

According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.