• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.87-87
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• 2021

이상기후 현상으로 기후변화가 사회와 경제에 미치는 영향이 뚜렷한 추세로 변화되고 있다. 현재 기후변화에 관련된 연구는 사회 시스템에서 위험관리를 위해 기온과 강수량에 따라 다양한 분야에 미치는 영향에 대한 연구를 중점으로 이뤄지고 있다. 본 연구는 여름철 폭염에 의한 기후변화가 하천수질에 미치는 영향을 평가하기 위한 것으로, 우리나라 기상청 91개의 기상관측소에서 일일온도 33℃ 이상의 이벤트를 대상으로 환경부 수질관측망 918개에 대한 14개의 하천수질인자인 DO, BOD, COD, TOC, DOC, TN, DTN, NH4-N, NO2-N, NO3-N, TP, DTP, PO4-P, Chl-a를 분석하였다. 이를 우리나라 117개 중권역별 하천수질과 폭염강도와 지속시간을 나타내는 폭염 지수를 산정하여 분석하였다. 폭염 관련 뉴스 데이터는 2013년부터 2019년까지 Python 기반 뉴스 크롤러를 이용해 폭염 취약지수(Heat Wave Vulnerability Index, HWVI)를 기준으로 분류하여 키워드를 수집하였으며 HWVI 중 '기후노출' 키워드와 관련된 기사는 총 22,514건으로 69.9%로 수집되었다. 공간적 영향 평가를 위해 Getis-Ord Gi*를 이용하여 폭염지수와 하천수질인자간 핫스팟 분석을 실시하고 폭염관련 빅데이터가 하천수질에 미치는 영향을 평가하였다. 폭염지수는 낙동강유역 하류에 대해 Chl-a, TN, TP 항목에서 높은 밀도를 보였다. 분석대상지역 내 폭염이 발생한 확률과 반경 밖에서 발생할 확률의 우도비를 분석하기 위해 SaTScan을 이용한 공간검색통계분석을 실시하였다. 분석결과 폭염지수와 DO의 공간상관성이 높은 것으로 나타났다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1A
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• pp.155-162
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• 2006

This study presents the seismic risk assesment for an extradosed bridge with seismic isolators of lead rubber bearings(LRB). First, the seismic vulnerability of a structure and then the seismic hazard of the site are evaluated using earthquake data set and seismic hazard map in Korea, and then the seismic risk of the structure is assessed. The nonlinear seismic analyses are carried out to consider plastic hinges of bridge columns and nonlinear characteristics of soil foundation. The ductility demand is adopted to describe the nonlinear behavior of a column, and the moment-curvature curve of a column is assumed to be bilinear hysterestic. The fragility curves are represented as a log-normal distribution function for column damage, movement of superstructure, and cable yielding. And seismic hazard is estimated using the available seismic hazard maps. The results show that the effectiveness of the seismic isolators for the columns is more noticeable than those for cables and girders, in seismic isolated extradosed bridges under earthquakes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.561-569
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• 2010

In this study, the impact of climate change on extreme drought events is investigated by comparing drought severity-area-duration curves under present and future climate. The depth-area-duration analysis for characterizing an extreme precipitation event provides a basis for analysing drought events when storm depth is replaced by an appropriate measure of drought severity. In our climate-change impact experiments, the future monthly precipitation time series is based on a KMA regional climate model which has a $27km{\times}27km$ spatial resolution, and the drought severity is computed using the standardized precipitation index. As a result, agricultural drought risk is likely to increase especially in short duration, while hydrologic drought risk will greatly increase in all durations. Such results indicate that a climate change vulnerability assessment for present water resources supply system is urgent.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.152-152
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• 2022

Only employing historical data limits the estimation of the full distribution of probable Tropical Cyclone (TC) risk due to the insufficiency of samples. Addressing this limitation, this study introduces a semi-physical TC rainfall model that produces spatially and temporally resolved TC rainfall data to improve TC risk assessments. The model combines a statistical-based track model based on the Markov renewal process to produce synthetic TC tracks, with a physics-based model that considers the interaction between TC and the atmospheric environment to estimate TC rainfall. The simulated data from the combined model are then fitted to a probability distribution function to compute the spatially heterogeneous risk brought by landfalling TCs. The methodology is employed in South Korea as a case study to be able to implement a country-scale-based vulnerability inspection from damaging TC impacts. Results show that the proposed model can produce TC tracks that do not only follow the spatial distribution of past TCs but also reveal new paths that could be utilized to consider events outside of what has been historically observed. The model is also found to be suitable for properly estimating the total rainfall induced by landfalling TCs across various points of interest within the study area. The simulated TC rainfall data enable us to reliably estimate extreme rainfall from higher return periods that are often overlooked when only the historical data is employed. In addition, the model can properly describe the distribution of rainfall extremes that show a heterogeneous pattern throughout the study area and that vary per return period. Overall, results show that the proposed approach can be a valuable tool in providing sufficient TC rainfall samples that could be an aid in improving TC risk assessment.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.415-415
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• 2022

IPCC 제5차 보고서('14) 및 세계위험보고서('15) 등에서 기후변화에 대한 과학적 근거를 제시하였으며, 상위 위험요소로 '수자원 위기'를 꼽았다. 전 세계적으로 기후변화로 인한 이상 기상현상이 발생하고 있으며, 국내에서도 최근 기후변화에 따른 수문사상의 변화로 극한홍수 및 극한가뭄 등으로 인한 피해가 지속적으로 발생하고 있다. 물 관리에서 기후변화는 가장 큰 리스크 요인이므로 물관리 계획 수립 과정에서 기후변화 영향을 고려한 대책을 수립할 필요가 있다. 기후변화에 대한 댐 취약성 평가 관련 연구가 이루어지고 있으나, 미래 기후변화의 불확실성을 충분히 고려했다고 보기 어렵기 때문에 현업에서 의사결정 도구로 활용하기에는 한계가 있다고 볼 수 있다. 이에 따라 과거 수문자료 및 특정 기후모델에 의존하지 않고 댐 인프라의 취약성을 평가할 수 있는 새로운 방법론이 필요하다고 판단된다. 따라서 본 연구를 통해 기후변화의 불확실성에 대비한 댐 취약성 평가 방법론을 정립하고자 한다. 본 연구에서는 기존에 진행된 IPCC 기후변화 시나리오에 따른 댐 취약성 평가 연구사례 및 한반도의 기후변화 영향 및 수문변화를 조사하였다. 그리고 기후 스트레스 시나리오 기반 취약성 평가 체계 및 방법론을 정립한 뒤, 월 강우량을 4분위로 나누어 각 분위별 강우량과 기온을 변경하여 기후 스트레스 시나리오를 생성하였다. 생성된 기후 스트레스 시나리오와 IPCC 기후변화 시나리오 기반 취약성 평가를 유출 및 저수지 모형을 결합하여 충주댐, 용담댐, 합천댐, 섬진강댐에 실시하였다. 그 결과 기후 스트레스에 따른 유출 취약성 평가는 20분위 수 갈수량을 이용해 연중 보장확률을 나타내는 것이 효율적이며, 온도의 영향보다는 강우의 변동이 댐 이수안전도 취약성 평가에 더 큰 영향을 주는 것을 확인할 수 있었다.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.81-92
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• 2023

The purpose of this study is to identify rural research topics, differences in research topics over time, and key mediators through the analysis of academic research trends using topic modeling. This study analyzed a total of 1,183 articles published in the Journal of Rural Planning and Rural Society over a 23-year period (2000-2022). We categorized rural research topics into 30, examined the proportion of research in each topic, and identified major changes in research topics over time. We also identified key words that mediate between research topics. The study found that, first, rural research trends can be categorized into five types (resources and utilization, area/space, people, ecosystem/environment, and tourism), with area/space being the most studied. Subtopics include rural amenities, rural disappearance/village miniaturization, and rural landscape management. Second, the research topics for each period were different. In the first period(2003-2007), the main research topics were rural amenities and Agricultural production- based climate vulnerability assessment. In the second period(2008-2012), the main research topics were Rural extinction and village depopulation, and rural landscape management, and in the third period(2013-2017), the main research topics were rural sixth industrialization and rural ecotourism. In the fourth period(2018-2022), rural development planning and rural life services(life SOC) were the main research topics. The significance of this study is that it extends the existing method of analyzing research trends and provides basic data to enhance comprehensive insights and understanding of rural research.

• Child Health Nursing Research
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• v.29 no.4
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• pp.271-279
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• 2023

Purpose: Adolescent self-harm is a public health problem. Research suggests a link between adverse childhood experiences (ACEs) and self-destructive behaviors. Few studies, however, have examined the effects of ACEs on self-harm among Asian adolescents. This study explored the association between lifetime ACEs and a history of self-harm among Korean children and adolescents in elementary, middle, and high schools. Methods: A cross-sectional, retrospective medical record review was conducted on a dataset of a national psychiatrist advisory service for school counselors who participated in the Wee Doctor Service from January 1 to December 31, 2020. The data were analyzed using multiple logistic regression to predict self-harm. Results: Student cases (n=171) were referred to psychiatrists by school counselors for remote consultation. Multiple logistic regression analyses revealed that the odds of self-harm were higher among high school students (adjusted odds ratio [aOR]=4.97; 95% confidence interval [CI]=1.94-12.76), those with two or more ACEs (aOR=3.27; 95% CI=1.43-7.47), and those with depression (aOR=3.06; 95% CI=1.32-7.10). Conclusion: The study's findings provide compelling evidence that exposure to ACEs can increase vulnerability to self-harm among Korean students. Students with a history of ACEs and depression, as well as high school students, require increased attention during counseling. School counselors can benefit from incorporating screening assessment tools that include questions related to ACEs and depression. Establishing a systematic referral system to connect students with experts can enhance the likelihood of identifying self-harm tendencies and offering the essential support to prevent self-harm.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.231-245
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• 2024

In recent decades, the protection and vulnerability of civil structures under explosion loads became a critical issue in terms of security, which may cause loss of lives and structural damage. Concrete retaining walls also restrict soils and slopes from displacements; meanwhile, intensive temporary loading may cause massive damage. In the current study, the modified Johnson-Holmquist (also known as J-H2) material model is implemented for concrete materials to model damages into the ABAQUS through user-subroutines to predict the blasting-induced concrete damages and volume strains. For this purpose, a 3D finite-element model of the concrete retaining wall was conducted in coupled Eulerian-Lagrangian simulation. Subsequently, a blast load equal to 500 kg of TNT was considered in three different positions due to UFC 3-340-02. Influences of the critical parameters in smooth blastings, such as distance from a free face, position, and effective blasting time, on concrete damage rate and destroy patterns, are explored. According to the simulation results, the concrete penetration pattern at the same distance is significantly influenced by the density of the progress environment. The result reveals that the progress of waves and the intensity of damages in free-air blasting is entirely different from those that progress in a dense surrounding atmosphere such as soil. Half-damaged elements in air blasts are more than those of embedded explosions, but dense environments such as soil impose much more pressure in a limited zone and cause more destruction in retaining walls.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1273-1300
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• 2024

In the complex virtual environment of cyberspace, comprised of digital and communication networks, ensuring the security of information is being recognized as an ongoing challenge. The importance of 'Cyber Situation Awareness (CSA)' is being emphasized in response to this. CSA is understood as a vital capability to identify, understand, and respond to various cyber threats and is positioned at the heart of cyber security strategies from a defensive perspective. Critical industries such as finance, healthcare, manufacturing, telecommunications, transportation, and energy can be subjected to not just economic and societal losses from cyber threats but, in severe cases, national losses. Consequently, the importance of CSA is being accentuated and research activities are being vigorously undertaken. A systematic five-step approach to CSA is introduced against this backdrop, and a deep analysis of recent research trends, techniques, challenges, and future directions since 2019 is provided. The approach encompasses current situation and identification awareness, the impact of attacks and vulnerability assessment, the evolution of situations and tracking of actor behaviors, root cause and forensic analysis, and future scenarios and threat predictions. Through this survey, readers will be deepened in their understanding of the fundamental importance and practical applications of CSA, and their insights into research and applications in this field will be enhanced. This survey is expected to serve as a useful guide and reference for researchers and experts particularly interested in CSA research and applications.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.183-191
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• 2024

Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f² trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R² (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.