• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.603-609
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• 2022

The dose distribution in the human body was evaluated and analyzed through dosimetry data using water phantom, ionization chamber and simulated by Monte Carlo simulation for ^99mTc and ¹⁸F sources, which are frequently used in the nuclear medicine in this study. As a result of this study, it was found that the dose decreased exponentially as the distance from the radioisotope increased, and it particularly showed a tendency to decrease sharply when the radioisotope was separated by 5 cm. It means that a large amount of dose is delivered to an organ located within 4 cm of source's movement path when a source uptake in the human body. Numerically, it was formed in the rage of 0.16 to 2.16 pC/min for ^99mTc and 0.49 to 9.29 pC/min for ¹⁸F. In addition, the energy transfer coefficient calculated using the result was found to be similar to the measured value and the simulation value in the range of 0.240 to 0.260. Especially, when the measured data and the simulation value were compared, there was a difference is within 2%, so the reliability of the data was secured. In this study, the distribution of radiation generated from a source was calculated to quantitatively evaluate the internal dose by radioisotopes. It presented reliable results through comparative analysis of the measurement value and simulation value. Above all, it has a great significance to the point that it was presented by directly measuring the distribution of radiation in the human body.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Journal of Environmental Impact Assessment
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• v.31 no.5
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• pp.310-320
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• 2022

This study aimed to identify the change in the public perception of "bats" after the outbreak of the coronavirus (COVID-19) infection. Text mining and network analysis were conducted for blog posts, the largest social network in Korea. We collected 9,241 Naver blog posts from 2019 to 2020 just before the outbreak of COVID-19 in Korea. The data were analyzed with Python and NetMiner 4.3.2, and the public's perception of bats was examined through the relationship of keywords by period. Findings indicated that the frequency of bat keywords in 2020 increased more than 25 times compared to 2019, and the centrality value increased more than three times. The perception of bats changed before and after the outbreak of the pandemic. Prior to COVID-19, bats were highly recognized as a species of wildlife while in the first half of 2020, they were strongly considered as a threat to human society in relation to infectious diseases and health. In the second half of 2020, it was confirmed that the area of interest in bats expanded as the proportion of ecological and cultural types ofresearch increased. This study seeks to contribute to the expansion and direction of future research in bats by understanding the public's interest in the potential impact of the species as disease hosts post the COVID-19 pandemic.

• Journal of Internet of Things and Convergence
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• v.7 no.4
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• pp.35-41
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• 2021

Natural dyeing has traditionally been used in many countries around the world, and as natural dyes are diversified, the diversity of dyeing patterns is also expanding. This study tried to establish standardization by providing numerical values that could provide quantified information to the Internet of Things by more accurately analyzing the color changes of dyes and mordants for the four natural dyes. The addition of copper acetate, iron II sulfate and potassium dichromate to the dye extracted from Juglans regia Linn changed the original color of brown to other colors of purple, khaki and dark brown, respectively. Except for potassium dichromate added to Sophora japonica L. or Phellodendron amurense Ruprecht, the concentration of other mordants was reduced, but the color difference of the dyed silk was very large. However, although there is a difference in degree, copper acetate and iron sulfate induced color changes of 35% and 15%, respectively. In summary, it was confirmed that the highest color change was induced when 15 grams of copper acetate was added to J. regia Linn, S. japonica L. and P. amurense Ruprecht and 150 grams of iron to Phytolacca americana. The results of this study suggested that the accurate color change by various mordants can be utilized as important information that enables more accurate color induction by dyes and mordants.

• 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.