• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3B
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• pp.213-219
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• 2009

In densely urban areas, features such as the sewer system, buildings and river banks have an effect on flow dynamics and flood propagation, and will therefore be accounted for in the model set-up. While two-dimensional (2D) flood models of urban areas are at the forefront of current research into flood inundation mechanisms, they are however constrained by inadequate parameters of topography, and insufficient and inaccurate data. In this study, an urban flood model (overland flow, 2D urban flood flow and sewer flow) was combined and applied at Samcheok city which was damaged by inundation in 2002, in order to simulate inundation depth. The influence of buildings and pumping capacity was also analyzed to estimate the inundated depth in the study area. As a result, it was found that urban inundated depth are affected by pumping capacity directly and it increased about 20-30 cm on most of the modeled area with a building share rate of 0.2-0.6 per unit grid.

• Korean Journal of Ecology and Environment
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• v.56 no.4
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• pp.303-319
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• 2023

Pitfall traps that use ethylene glycol as a preservative solution are commonly used in arthropod research. However, a recent surge in cases involving damage to these traps by roe deer or wild boars owing to the sweet taste of ethylene glycol has prompted the addition of quinone sulfate, a substance with a pungent taste, to deter such wildlife interference. This study aimed to assess the effects of quinone sulfate on arthropods collected from pitfall traps containing ethylene glycol. We strategically positioned 50 traps using ethylene glycol alone and 50 traps containing a small amount of quinone sulfate mixed with ethylene glycol in a grid pattern for systematic sampling at the Gwangneung Forest long-term ecological research (LTER) site. Traps were collected 10 days later. The results revealed a notable effect on ants when quinone sulfate was introduced. Specifically, it decreased the number of ants. In a species-specific analysis of ants, only Nylanderia flavipes showed a significant decline in response to quinone sulfate, whereas other ant species remained unaffected. Additionally, among the arthropod samples obtained in this survey, we identified species or morpho-species of spiders, beetles, and ants and assessed species diversity. Consequently, the utilization of quinone sulfate should be undertaken judiciously, taking into account the specific species composition and environmental characteristics of the monitoring site. Our study also highlighted the significant response of various arthropod groups to variations in leaf litter depth, underscoring the crucial role of the leaf litter layer in providing sustenance and shelter for ground-foraging arthropods. Furthermore, we have compiled comprehensive species lists of both spiders and ants in Gwangneung forest by amalgamating data from this investigation with findings from previous studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.515-522
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• 2024

Energy consumption is steadily increasing, and buildings in particular account for more than 20% of the total energy consumption around the world. As an effort to cost-effectively manage the energy consumption of buildings, many research groups have recently focused on Smart Building Energy Management Systems (BEMS), which are deepening the research depth by applying artificial intelligence(AI). In this paper, we propose a reinforcement learning-based energy control method for smart energy buildings integrated with V2G station, which aims to reduce the total energy cost of the building. The results of performance evaluation based on the energy consumption data measured in the real-world building shows that the proposed method can gradually reduce the total energy costs of the building as the learning process progresses.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.715-727
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• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.

• Steel and Composite Structures
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• v.51 no.4
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• pp.441-456
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• 2024

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.116-130
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• 2019

In South Korea, forest fire occurrences are increasing in size and duration due to various factors such as the increase in fuel materials and frequent drying conditions in forests. Therefore, it is necessary to minimize the damage caused by forest fires by appropriately providing the probability of forest fire risk. The purpose of this study is to improve the Daily Weather Index(DWI) provided by the current forest fire forecasting system in South Korea. A new Fire Risk Index(FRI) is proposed in this study, which is provided in a 5km grid through the synergistic use of numerical weather forecast data, satellite-based drought indices, and forest fire-prone areas. The FRI is calculated based on the product of the Fine Fuel Moisture Code(FFMC) optimized for Korea, an integrated drought index, and spatio-temporal weighting approaches. In order to improve the temporal accuracy of forest fire risk, monthly weights were applied based on the forest fire occurrences by month. Similarly, spatial weights were applied using the forest fire density information to improve the spatial accuracy of forest fire risk. In the time series analysis of the number of monthly forest fires and the FRI, the relationship between the two were well simulated. In addition, it was possible to provide more spatially detailed information on forest fire risk when using FRI in the 5km grid than DWI based on administrative units. The research findings from this study can help make appropriate decisions before and after forest fire occurrences.

• Economic and Environmental Geology
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• v.53 no.1
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• pp.33-43
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• 2020

CO₂ geological storage is currently considered as the most stable and effective technology for greenhouse gas reduction. The saline formations for CO₂ geological storage are generally located at a depth of more than 800 m where CO₂ can be stored in a supercritical state, and an extensive impermeable cap rock that prevents CO₂ leakage to the surface should be distributed above the saline formations. Trough analysis of seismic and well data, we identified the basalt flow structure for potential CO₂ storage where saline formation is overlain by basalt cap rock around PZ-1 exploration well in the Southern Continental Shelf of Korea. To evaluate CO₂ storage capacity of the saline formation, total porosity and CO₂ density are calculated based on well logging data of PZ-1 well. We constructed a 3D geological grid model with a certain size in the x, y and z axis directions for volume estimates of the saline formation, and performed a property modeling to assign total porosity to the geological grid. The estimated average CO₂ geological storage capacity evaluated by the U.S. DOE method for the saline formation covered by the basalt cap rock is 84.17 Mt of CO₂(ranges from 42.07 to 143.79 Mt of CO₂).

• Journal of Korean Society of Forest Science
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• v.87 no.3
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• pp.358-365
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• 1998

To offer the basic information for sustainable production of forest resources and conservation of the global environment, change in potential natural vegetation (PNV) associated with climate change due to doubling atmospheric carbon dioxide ($2{\times}CO_2$) was estimated with the global natural vegetation mapping system based an K${\ddot{o}}$ppen scheme. The system interpolates climate data spherically to each grid cell, determines the vegetation types onto the grid cell, and produces potential vegetation map and area on the globe and continents. The climate data consist of the current, ($1{\times}CO_2$) climate prior to AD 1958 observed at some 2,000 stations and the doubling ($2{\times}CO_2$) climate estimated from Meteorological Research Institute of Japan. The vegetation zone under the $2{\times}CO_2$ climate scenario expanded mainly toward the poles due to the rise in temperature. The changed PNV area on the globe amounts to 1/3 (4.91 billion (G) ha) of the total land area (15.04 Gha). Kappa statistic for judging agreement between the patterns of vegetation distribution under $1{\times}CO_2$ climate and $2{\times}CO_2$ climates shows good agreement (0.63) for the globe as a whole. The most stable areas are desert and ice. The potential forest area (PFA) was estimated at 6.82 Gha of the land area in $2{\times}CO_2$ climate scenario. In terms of continental changes in PFA, North America and Asis are increased under the $2{\times}CO_2$ climate. However, the potential forest arms of the other continents are decreased by the climate. Europe has no change in the PFA. Especially, the expansion of desert area in Oceania would be accelerated by the $2{\times}CO_2$ climate.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1361-1371
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• 2021

Among smart city services, the crime and disaster prevention sector accounted for the highest 24% in 2018. The most important platform for providing real-time situation information is CCTV (Closed-Circuit Television). Therefore, it is essential to create the actual CCTV surveillance coverage to maximize the usability of CCTV. However, the amount of CCTV installed in Korea exceeds one million units, including those operated by the local government, and manual identification of CCTV coverage is a time-consuming and inefficient process. This study proposed a method to efficiently construct CCTV's actual surveillance coverage and reduce the time required for the decision-maker to manage the situation. For this purpose, first, the exterior orientation parameters and focal lengths of the pre-installed CCTV cameras, which are difficult to access, were calculated using the point cloud data of the MMS (Mobile Mapping System), and the FOV (Field of View) was calculated accordingly. Second, using the FOV result calculated in the first step, CCTV's actual surveillance coverage area was constructed with 1 m, 2 m, 3 m, 5 m, and 10 m grid interval considering the occluded regions caused by the buildings. As a result of applying our approach to 5 CCTV images located in Uljin-gun, Gyeongsnagbuk-do the average re-projection error was about 9.31 pixels. The coordinate difference between calculated CCTV and location obtained from MMS was about 1.688 m on average. When the grid length was 3 m, the surveillance coverage calculated through our research matched the actual surveillance obtained from visual inspection with a minimum of 70.21% to a maximum of 93.82%.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.