• Journal of Hydrogen and New Energy
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• v.33 no.6
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• pp.845-855
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• 2022

In response to climate change, the world is continuing efforts to reduce fossil fuels, expand renewable energy, and improve energy efficiency with the goal of achieving carbon neutrality. In particular, R&D is being made on the value chain covering the entire cycle of hydrogen production, storage, transportation, and utilization in order to shift the energy supply system to focus on hydrogen energy. Hydrogen-based energy sources can produce heat and electricity at the same time, so it is possible to utilize heat energy, which can increase overall efficiency. In this study, calculation of the optimal scale for hydrogen-based cogeneration and the composition of heat sources were reviewed. It refers to a method of the optimal heat source size according to the external heat supply and heat storage to be considered. The results of this study can be used as basic data for establishing a hydrogen-based energy supply model in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.51-61
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• 2023

Travel demand estimation of E-Scooter is the start point of solving the regional demand-supply imbalance problem and plays pivotal role in a linked transportation system such as Mobility-as-a-Service (a.k.a. MaaS). Most focuses on developing trip generation model of shared E-Scooter but it is no study on selection of an appropriate zone scale when it comes to estimating travel demand of E-Scooter. This paper aimed for selecting an optimal TAZ scale for developing trip distribution model for shared E-Scooter. The TAZ scale candidates were selected in 250m, 500m, 750m, 1,000m square grid. The shared E-Scooter usage historical data were utilized for calculating trip distance and time, and then applying to developing gravity model. Mean Squared Error (MSE) is applied for the verification step to select the best suitable gravity model by TAZ scale. As a result, 250m of TAZ scale is the best for describing practical trip distribution of shared E-Scooter among the candidates.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.71-71
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• 2021

현재 국내에서 사용되고 있는 지자체 단위 위험도 평가 기법들은 자연재난과 사회재난으로부터 유발되는 여러 위험성들을 함께 고려하여 평가에 반영하고 있다. 또한, 지자체 내에서 홍수위험에 노출될 수 있는 대상만을 선별하여 분석한 것이 아닌 지자체별 단순 통계값으로 평가가 이루어지기 때문에 홍수위험에 대한 정확한 평가가 어렵다는 한계를 가지고 있다. 따라서 본 연구에서는 Indicator Based Approach(IBA)에서 제시하는 평가 항목인 Hazard, Exposure, Vulnerability, Capacity 중 Exposure에 해당하는 건축물수를 대상으로 홍수위험지도와 중첩되는 건축물들을 선별하여 홍수위험도 평가를 수행하였다. 지자체별 건축물수 산정은 2018년 11월 기준 경기도 31개 시군별 도로명주소 전자지도(건물)와 500m × 500m 건축물수 격자자료를 사용하였다. 건축물수 격자자료는 도로명주소 전자지도의 건물 폴리곤 자료 대비 분석이 간편하다는 장점을 가지고 있다. 비교 분석을 통해 공간분석자료의 유형에 따라 발생하는 통계값의 차이는 격자자료에 보정계수를 적용하여 보완하였다. 보정된 경기도 지자체별 건축물수 격자자료로 세부지표 지수를 산정한 결과 단순히 자지체별 건축물수를 사용했을 때에는 화성시, 용인시, 평택시 순으로 지수가 크게 산정되었다, 하지만 홍수위험지도와 중첩된 건축물수를 사용했을 때에는 고양시, 광명시, 김포시 순으로 지수가 크게 산정되었다. 본 연구를 통해서 건축물수 격자자료와 홍수위험지도를 사용하여 위험도 평가를 수행했을 때 기존 방법론 대비 합리적인 평가결과를 얻을 수 있었다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.1
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• pp.1-12
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• 2023

It is very time-intensive to obtain data with labels on anomaly detection tasks for multivariate time series. Therefore, several studies have been conducted on unsupervised learning that does not require any labels. However, a well-done integrative survey has not been conducted on in-depth discussion of learning architecture and property for multivariate time series anomaly detection. This study aims to explore the characteristic of well-known architectures in anomaly detection of multivariate time series. Additionally, architecture was categorized by using top-down and bottom-up approaches. In order toconsider real-world anomaly detection situation, we trained models with dataset such as power grids or Cyber Physical Systems that contains realistic anomalies. From experimental results, we compared and analyzed the comprehensive performance of each architecture. Quantitative performance were measured using precision, recall, and F1 scores.

• Wind and Structures
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• v.36 no.3
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• pp.161-174
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• 2023

The analytical model of tornado vortices plays an essential role in tornado wind description and tornado-resistant design of civil structures. However, there is still a lack of guidance for the selection and application of tornado analytical models since they are different from each other. For single-cell tornado vortices, this study conducts a comparative study on the velocity characteristics of the analytical models based on numerically simulated tornado-like vortices (TLV). The single-cell stage TLV is first generated by Large-eddy simulations (LES). The spatial distribution of the three-dimensional mean velocity of the typical analytical tornado models is then investigated by comparison to the TLV with different swirl ratios. Finally, key parameters are given as functions of swirl ratio for the direct application of analytical tornado models to generate full-scale tornado wind field. Results show that the height of the maximum radial mean velocity is more appropriate to be defined as the boundary layer thickness of the TLV than the height of the maximum tangential mean velocity. The TLV velocity within the boundary layer can be well estimated by the analytical model. Simple fitted results show that the full-scale maximum radial and tangential mean velocity increase linearly with the swirl ratio, while the radius and height corresponding to the position of these two velocities decrease non-linearly with the swirl ratio.

• 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.