• Atmosphere
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• v.32 no.1
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• pp.51-60
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• 2022

Corona Virus Disease 19 pandemic (COVID-19) causes many deaths worldwide, and has enormous impacts on society and economy. The COVID-19 was caused by a new type of coronavirus (Severe Acute Respiratory Syndrome Cornonavirus 2; SARS-CoV-2), which has been found that these viruses can be effectively inactivated by ultraviolet (UV) radiation of 290~315 nm. In this study, 90% inactivation time of the SARS-CoV-2 virus was analyzed using ground observation data from Brewer spectrophotometer at Yonsei University, Seoul and simulation data from UVSPEC for the period of 2015~2017 and 2020. Based on 12:00-13:00 noon time, the shortest virus inactivation time were estimated as 13.5 minutes in June and 4.8 minutes in July/August, respectively, under all sky and clear sky conditions. In the diurnal and seasonal variations, SARS-CoV-2 could be inactivated by 90% when exposed to UV radiation within 60 minutes from 10:00 to 14:00, for the period of spring to autumn. However, in winter season, the natural prevention effect was meaningless because the intensity of UV radiation weakened, and the time required for virus inactivation increased. The spread of infectious diseases such as COVID-19 is related to various and complex interactions of several variables, but the natural inactivation of viruses by UV radiation presented in this study, especially seasonal differences, need to be considered as major variables.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.157-157
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• 2022

Recent decades have seen all over the world increasing drought in some regions and increasing flood in others. Climate change has been alarming in many regions resulting in degradation and diminution of available freshwater. The effect of global warming and overpopulation associated with increasing irrigated farming and valuable agricultural lands could be particularly disastrous for coastal areas like the one of Benin. The coastal region of Benin is under a heavy demographic pressure and was in the last decades the object of important urban developments. The present study aims to roughly study the general effect of climate change (Sea Level Rise: SLR) and groundwater pumping on Seawater intrusion (SWI) in Benin's coastal region. To reach the main goal of our study, the region aquifer system was built in numerical model using SEAWAT engine from Visual MODFLOW. The model is built and calibrated from 2016 to 2020 in SEAWAT, and using WinPEST the model parameters were optimized for a better performance. The optimized parameters are used for seawater intrusion intensity evaluation in the coastal region of Benin The simulation of the hydraulic head in the calibration period, showed groundwater head drawdown across the area with an average of 1.92m which is observed on the field by groundwater level depletion in hand dug wells mainly in the south of the study area. SWI area increased with a difference of 2.59km² between the start and end time of the modeling period. By considering SLR due to global warming, the model was stimulated to predict SWI area in 2050. IPCC scenario IS92a simulated SLR in the coastal region of Benin and the average rise is estimated at 20cm by 2050. Using the average rise, the model is run for SWI area estimation in 2050. SWI area in 2050 increased by an average of 10.34% (21.04 km²); this is expected to keep increasing as population grows and SLR.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.233-233
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• 2022

최근 기후변화로 인해 집중호우 및 돌발홍수의 증가로 침수피해가 빈번하게 발생하고 있다. 마찬가지로 해안지역의 피해 또한 증가하고 있으나, 해안지역의 특성을 고려한 연구가 미비한 실정이다. 따라서 본 연구에서 해안지역의 특성을 고려해 폭풍해일로 인한 월파뿐만 아니라 강우도 고려하여 해안지역의 범람 양상을 확인하고자 하였다. 본 연구에서는 국내 해안지역에 대한 빈도별 폭풍해일과 강우로인한 범람 모의를 진행하였다. 우선, 수치해석 모형의 경계조건을 산정하기 위해 EurOtop(2018)의 경험식을 이용하여 월파량을 산정하였다. EurOtop의 월파량 산정 시 암석 옹벽이 아닌 콘크리트 옹벽으로된 경사식 단면으로 고려하여 계산하였고 산책로와 벽까지 고려하여 계산하였다. 경험식 계산을 위해 매개변수(유의파고, 여유고, 구조물의 조도계수, 구조물의 기울기 및 경사 등)를 조정하여 계산하였다. 이 중, 계산에 사용된 유의파고는 시나리오별 강우에 대해 SWAN(Simulating WAves Nearshore)으로 계산된 값을 활용하였고, 해안선을 두 부분으로 나누어 해안지역 각 지점별 파고값의 평균을 사용해 월파량 계산을 진행했다. 이때, 파고의 종류로 5% 확률의 파고, 평균 파고, 중앙값 파고, 95% 확률의 파고로 분류해 월파량 계산을 진행했고, 그 중, 평균 파고를 이용해 계산한 월파량을 수치해석 모델의 입력자료로 활용하였다. 시나리오별로 계산된 월파량만을 이용해 2차원 침수모형인 FLO-2D의 경계조건 입력값으로 사용하여 침수 양상을 표출하기 위해 Mapper와 ArcGIS를 이용하여 침수와 범람 양상을 확인하였다. 또, 다른 조건으로 시나리오별 계산된 월파량, 연구유역 해안 반대편에 위치한 산으로부터 유입되는 물의 양 그리고 해안지역 전체에 내리는 강우를 입력자료로 사용해 모의를 진행한 후 Mapper와 ArcGIS로 표출하여 침수 및 범람 양상을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.110-113
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• 2022

Nowadays, breaking through the conventional traffic signal control method based on mathematical optimization, artificial intelligence began to be used in the area. In response to this trend, many studies are ongoing to figure out how to utilize AI technology properly for traffic signal optimization. They just simply focus on which method will work well besides lots of machine learning techniques and abandon the reward function engineering. In many cases, the reward function consists of the average delay of the vehicles in the intersection. However, this may lead to AI's misunderstanding about the traffic signal control: what AI regards as a good situation may not be realistic. Even the reward function itself may not meet the service level. Therefore, this study analyzes the problems of previous reward functions and will suggest how to reward function can be enhanced.

• Geomechanics and Engineering
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• v.31 no.1
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• pp.1-14
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• 2022

Urban deep excavation will affect greatly on the deformation of adjacent existing buildings, especially those with shallow foundations. Isolation piles has been widely used in engineering to control the deformation of buildings adjacent to the excavation, but its applicability is still controversial. Based on a typical engineering, numerical calculation models were established and verified through monitoring data to study the influence characteristics of isolation piles on the deformation of existing shallow foundation buildings. Results reveal that adjacent buildings will increase building settlement δ_v and the deformation of diaphragm walls δ_h, while the isolation piles can effectively decrease these. The surface settlement curve is changed from "groove" type to "double groove" type. Sufficiently long isolation pile can effectively decrease δ_v, while short isolation piles will lead to a negative effect. When the building is within the range of the maximum settlement location P, maximum building rotation θ_m will increase with the pile length L and the relative position between isolation pile and building d/D increase (d is the distance between piles and diaphragm walls, D is the distance between buildings and diaphragm walls), instead, θ_m will decrease for buildings outside the location P, and the optimum was obtained when d/D=0.7.

• Smart Structures and Systems
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• v.32 no.4
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• pp.253-266
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• 2023

The time-reversal method is employed to improve the ability of pipeline defect detection, and a new approach of identifying the pipeline defect depth is proposed in this research. When the L(0,2) mode ultrasonic guided wave excited through a lead zirconate titinate (PZT) transduce array propagates along the pipeline with a defect, it will interact with the defect and be partially converted to flexural F(n, m) modes and longitudinal L(0,1) mode. Using a receiving PZT array attached axisymmetrically around the pipeline, the L(0,2) reflection signal as well as the mode conversion signals at the defect are obtained. An appropriate rectangle window is used to intercept the L(0,2) reflection signal and the mode conversion signals from the obtained direct detection signals. The intercepted signals are time reversed and re-excited in the pipeline again, result in the guided wave energy focusing on the pipeline defect, the L(0,2) reflection and the L(0,1) mode conversion signals being enhanced to a higher level, especially for the small defect in the early crack stage. Besides the L(0,2) reflection signal, the L(0,1) mode conversion signal also contains useful pipeline defect information. It is possible to identify the pipeline defect depth by monitoring the variation trend of L(0,2) and L(0,1) reflection coefficients. The finite element method (FEM) simulation and experiment results are given in the paper, the enhancement of pipeline defect reflection signals by time-reversal method is obvious, and the way to identify pipeline defect depth is demonstrated to be effective.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.56-63
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• 2024

As the safety of unmanned vehicles continues to improve, their usage in urban environments, which are full of obstacles such as buildings, is expected to increase. When numerous unmanned vehicles are operated in such environments, an algorithm that takes into account mutual collision avoidance, as well as static and dynamic obstacle avoidance, is necessary. In this paper, we propose an algorithm that handles task assignment and path planning. To efficiently plan paths, we construct a grid-based map and derive the paths from it. To enable quick re-planning in dynamic environments, we focus on reducing computational time. Through simulation, we explain obstacle avoidance and mutual collision avoidance in small-scale problems and confirm their performance by observing the entire mission completion time (Makespan) in large-scale problems.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.11-20
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• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.131-143
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• 2024

Shield tunneling construction commonly crosses underground pipelines in urban areas, resulting in soil loss and followed deformation of grounds and pipelines nearby, which may threaten the safe operation of shield tunneling. This paper investigated the pipeline deformation caused by double curved shield tunnels in soil-rock composite stratum in Nanjing, China. The stratum settlement equation was modified to consider the double shield tunneling. Moreover, a three dimensional finite element model was established to explore the effects of hard-layer ratio, tunnel curvature radius, pipeline buried depth and other influencing factors. The results indicate the subsequent shield tunnel would cause secondary disturbance to the soil around the preceding tunnel, resulting in increased pipeline and ground surface settlement above the preceding tunnel. The settlement and stress of the pipeline increased gradually as buried depth of the pipeline increased or the hard-layer ratio (the ratio of hard-rock layer thickness to shield tunnel diameter within the range of the tunnel face) decreased. The modified settlement calculation equation was consistent with the measured data, which can be applied to the settlement calculation of ground surface and pipeline settlement. The modified coefficients a and b ranged from 0.45 to 0.95 and 0.90 to 1.25, respectively. Moreover, the hard-layer ratio had the most significant influence on the pipeline settlement, but the tunnel curvature radius and the included angle between pipeline and tunnel axis played a dominant role in the scope of the pipeline settlement deformation.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.109-123
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• 2024

Research into vision-based end-to-end autonomous driving systems utilizing deep learning and reinforcement learning has been steadily increasing. These systems typically encode continuous and high-dimensional vehicle states, such as location, velocity, orientation, and sensor data, into latent features, which are then decoded into a vehicular control policy. The complexity of urban driving environments necessitates the use of state representation learning through networks like Variational Autoencoders (VAEs) or Convolutional Neural Networks (CNNs). This paper analyzes the impact of different image state encoding methods on reinforcement learning performance in autonomous driving. Experiments were conducted in the CARLA simulator using RGB images and semantically segmented images captured by the vehicle's front camera. These images were encoded using VAE and Vision Transformer (ViT) networks. The study examines how these networks influence the agents' learning outcomes and experimentally demonstrates the role of each state representation technique in enhancing the learning efficiency and decision- making capabilities of autonomous driving systems.