• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.158-158
• /
• 2023

Characterizing the performance of precipitation (hereafter PRE) products in estimating the uncertainties in daily PRE in the era of global warming is of great value to the ecosystem's sustainability and human survival. This study intercompares the performance of different PRE products (gauge-based, satellite and reanalysis) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over diverse climate zones in Africa and identifies regions where they depict minimal uncertainties in order to build optimal maps as a guide for different climate users. This is achieved by utilizing various techniques, including the triple collection (TC) approach, to assess the capabilities and limitations of different PRE products over nine climatic zones over the continent. For daily scale analysis, the uncertainties in light PRE (0.1 5mm/day) are prevalent over most regions in Africa during the study duration (2001-2016). Estimating the occurrence of extreme PRE events based on daily PRE 90th percentile suggests that extreme PRE is mainly detected over central Africa (CAF) region and some coastal regions of west Africa (WAF) where the majority of uncorrected satellite products show good agreement. The detection of PRE days and non-PRE days based on categorical statistics suggests that a perfect POD/FAR score is unattainable irrespective of the product type. Daily PRE uncertainties determined based on quantitative metrics show that consistent, satisfactory performance is demonstrated by the IMERG products (uncorrected), ARCv2, CHIRPSv2, 3B42v7.0 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0, and REGEN_ALL (gauge) during the study period. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage for different stakeholders.

• 한국정책학회보
• /
• v.23 no.4
• /
• pp.179-209
• /
• 2014

Transition studies have been gaining attention among innovation researchers from the early 1990's mainly influenced by emerging global agendas on sustainability. In this paper, we review research issues and trends focusing on four prominent transition theories - Technological innovation systems, Multi-level perspective, Transition management and Strategic niche management. After reviewing main characteristics, research trends and debates of each theory from established literatures, we conduct comparative analysis of these theories and suggest integrated framework that can be considered when modeling transition policy. This integrated framework is applied to the case study on smart-grid policy of Korea.

• Journal of Sensor Science and Technology
• /
• v.32 no.2
• /
• pp.110-117
• /
• 2023

Reconstructing underwater geometry in real time with forward-looking sonar is critical for applications such as localization, mapping, and path planning. Geometrical data must be repeatedly calculated and overwritten in real time because the reliability of the acoustic data is affected by various factors. Moreover, scattering of signal data during the coordinate conversion process may lead to geometrical errors, which lowers the accuracy of the information obtained by the sensor system. In this study, we propose a three-step data processing method with low computational cost for real-time operation. First, the number of data points to be interpolated is determined with respect to the distance between each point and the size of the data grid in a Cartesian coordinate system. Then, the data are processed with a nonlinear interpolation so that they exhibit linear properties in the coordinate system. Finally, the data are transformed based on variations in the position and orientation of the sonar over time. The results of an evaluation of our proposed approach in a simulation show that the nonlinear interpolation operation constructed a continuous underwater geometry dataset with low geometrical error.

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

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.3D
• /
• pp.417-422
• /
• 2008

With standard orthorectification algorithms, one can produce unacceptable structure duplication in the orthophoto due to the double projection. Because of the abrupt height differences, such structure duplication is a frequently occurred phenomenon in the dense urban area which includes multi-history buildings. Therefore, occlusion area detection especially for the urban area is a critical issue in generation of true orthophoto. This paper deals with occlusion area detection with visible height based approach from aerial imagery and LiDAR. In order to accomplish this, a grid format DSM is produced from the point clouds of LiDAR. Next, visible height based algorithm is proposed to detect the occlusion area for each camera exposure station with DSM. Finally, generation of true orthophoto is presented with DSM and previously produced occlusion maps. The proposed algorithms are applied in the Purdue campus, Indiana, USA.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.329-329
• /
• 2022

Accurate characterization of terrestrial precipitation variation from high spatial resolution satellite sensors is beneficial for urban hydrology and microscale agriculture modeling, as well as natural disasters (e.g., urban flooding) early warning. However, the widely-used top-down approach for precipitation retrieval from microwave satellites is limited in several hydrological and agricultural applications due to their coarse spatial resolution. In this research, we aim to apply a novel bottom-up method, the parameterized SM2RAIN, where precipitation can be estimated from soil moisture signals based on an inversion of water balance model, to generate high spatial resolution terrestrial precipitation estimates at 0.01º grid (roughly 1-km) from the C-band SAR Sentinel-1. This product was then tested against a common reanalysis-based precipitation data and a domestic rain gauge network from the Korean Meteorological Administration (KMA) over central South Korea, since a clear difference between climatic types (coasts and mainlands) and land covers (croplands and mixed forests) was reported in this area. The results showed that seasonal precipitation variability strongly affected the SM2RAIN performances, and the product derived from separated parameters (rainy and non-rainy seasons) outperformed that estimated considering the entire year. In addition, the product retrieved over the mainland mixed forest region showed slightly superior performance compared to that over the coastal cropland region, suggesting that the 6-day time resolution of S1 data is suitable for capturing the stable precipitation pattern in mainland mixed forests rather than the highly variable precipitation pattern in coastal croplands. Future studies suggest comparing this product to the traditional top-down products, as well as evaluating their integration for enhancing high spatial resolution precipitation over entire South Korea.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.569-582
• /
• 2023

Lead-acid Battery is the oldest rechargeable battery system and has maintained its position in the rechargeable battery field. The battery causes thermal runaway for various reasons, which can lead to major accidents. Therefore, preventing thermal runaway is a key part of the battery management system. Recently, research is underway to categorize thermal runaway battery cells into machine learning. In this paper, we present a thermal runaway hazard cell detection and verification algorithm using DBSCAN and statistical method. An experiment was conducted to classify thermal runaway hazard cells using only the resistance values as measured by the Battery Management System (BMS). The results demonstrated the efficacy of the proposed algorithms in accurately classifying thermal runaway cells. Furthermore, the proposed algorithm was able to classify thermal runaway cells between thermal runaway hazard cells and cells containing noise. Additionally, the thermal runaway hazard cells were early detected through the optimization of DBSCAN parameters using a grid search approach.

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.3
• /
• pp.41-49
• /
• 2024

In this paper, we validate simulation results for the design optimization of a Superconducting Fault Current Limiter (SFCL) intended for use in Medium Voltage Direct Current systems (MVDC). With the increasing integration of renewable energy and grid connections, researchers are focusing on medium-voltage systems for balancing energy in new and renewable energy networks, rather than traditional transmission or distribution networks. Specifically, for DC distribution networks dealing with fault currents that must be rapidly blocked, current-limiting systems like superconducting current limiters offer distinct advantages over the operation of DC circuit breakers. The development of such superconducting current limiters requires finite element analysis (FEM) and an extensive design process before prototype production and evaluation. To expedite this design process, the design outcomes are assimilated using a Reduced Order Model (ROM). This approach enables the verification of results akin to finite element analysis, facilitating the optimization of design simulations for production and mass production within existing engineering frameworks.

• Nuclear Engineering and Technology
• /
• v.56 no.2
• /
• pp.715-727
• /
• 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
• /
• v.51 no.4
• /
• pp.441-456
• /
• 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.