• 한국군사과학기술학회지
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• 제22권3호
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• pp.425-432
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• 2019

Modeling and Simulation(M&S) has widely been used in various areas such as industry, academy and military. Especially, military have used the technology for acquisition, training, and combat assessment. In this paper, we introduce our experience using M&S technique to analyze the network traffic amount and packet delay time between two military bases. For this, we modeled the current network configuration of the military bases and simulated the model with NS-3 tool. The result provided us for an insight regarding the required network performance between two bases.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.162-162
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• 2023

Climate change is a complex phenomenon having its impact on diverse sectors. Temperature and precipitation are two of the most fundamental variables used to characterize climate, and changes in these variables can have significant impacts on ecosystems, agriculture, and human societies. This study evaluated the historical (1981-2010) and future (2011-2100) climatic trends in the Seti-Gandaki basin of Nepal based on 5 km resolution Multi Model Ensemble (MME) of 18 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for SSP1-2.6, SSP2-4.5 and SSP5-85 scenarios. For this study, ERA5 reanalysis dataset is used for historical reference dataset instead of observation dataset due to a lack of good observation data in the study area. Results show that the basin has experienced continuous warming and an increased precipitation pattern in the historical period, and this rising trend is projected to be more prominent in the future. The Seti basin hosts 13 operational hydropower projects of different sizes, with 10 more planned by the government. Consequently, the findings of this study could be leveraged to design adaptation measures for existing hydropower schemes and provide a framework for policymakers to formulate climate change policies in the region. Furthermore, the methodology employed in this research could be replicated in other parts of the country to generate precise climate projections and offer guidance to policymakers in devising sustainable development plans for sectors like irrigation and hydropower.

• Clinical and Experimental Reproductive Medicine
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• 제48권2호
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• pp.163-173
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• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.191-191
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• 2017

Floods have become more widespread and frequent among natural disasters and consisted significant losses of lives and properties worldwide. Flood's impacts are threatening socio-economic and people's lives in the Mekong River Basin every year. The objective of this study is to identify the flood hazard areas and inundation depth in the Mekong River Basin. A rainfall-runoff and flood inundation model is necessary to enhance understanding of characteristic of flooding. Rainfall-Runoff-Inundation (RRI) model, a two-dimensional model capable of simulating rainfall-runoff and flood inundation simultaneously, was applied in this study. HydoSHEDS Topographical data, APPRODITE precipitation, MODIS land use, and river cross section were used as input data for the simulation. The Shuffled Complex Evolution (SCE-UA) global optimization method was integrated with RRI model to calibrate the sensitive parameters. In the present study, we selected flood event in 2000 which was considered as 50-year return period flood in term of discharge volume of 500 km3. The simulated results were compared with observed discharge at the stations along the mainstream and inundation map produced by Dartmouth Flood Observatory and Landsat 7. The results indicated good agreement between observed and simulated discharge with NSE = 0.86 at Stung Treng Station. The model predicted inundation extent with success rate SR = 67.50% and modified success rate MSR = 74.53%. In conclusion, the RRI model was successfully used to simulate rainfall runoff and inundation processes in the large scale Mekong River Basin with a good performance. It is recommended to improve the quality of the input data in order to increase the accuracy of the simulation result.

• Geomechanics and Engineering
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• 제36권2호
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• pp.111-119
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• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.

• 한국과학교육학회지
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• 제44권3호
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• pp.231-248
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• 2024

본 연구에서는 오픈소스 소프트웨어와 인공지능 문서 분류 모델인 한국어 Sentence-BERT로 고등학교 1학년 통합과학 질문-답변 챗봇을 제작하고 2023학년도 1년 동안 독립형 서버에서 운영했다. 챗봇은 Sentence-BERT 모델로 학생의 질문과 가장 유사한 질문-답변 쌍 6개를 찾아 캐러셀 형태로 출력한다. 질문-답변 데이터셋은 인터넷에 공개된 자료를 수집하여 초기 버전을 구축하였고, 챗봇을 1년 동안 운영하면서 학생의 의견과 사용성을 고려하여 자료를 정제하고 새로운 질문-답변 쌍을 추가했다. 2023학년도 말에는 총 30,819개의 데이터셋을 챗봇에 통합하였다. 학생은 챗봇을 1년 동안 총 3,457건 이용했다. 챗봇 사용 기록을 빈도분석 및 시계열 분석한 결과 학생은 수업 중 교사가 챗봇 사용을 유도할 때 챗봇을 이용했고 평소에는 방과 후에 자습하면서 챗봇을 활용했다. 학생은 챗봇에 한 번 접속하여 평균적으로 2.1~2.2회 정도 질문했고, 주로 사용한 기기는 휴대폰이었다. 학생이 챗봇에 입력한 용어를 추출하고자 한국어 형태소 분석기로 명사와 용언을 추출하여 텍스트 마이닝을 진행한 결과 학생은 과학 질문 외에도 시험 범위 등의 학교생활과 관련된 용어를 자주 입력했다. 학생이 챗봇에 자주 물어본 주제를 추출하고자 Sentence-BERT 기반의 BERTopic으로 학생의 질문을 두 차례 범주화하여 토픽 모델링을 진행했다. 전체 질문 중 88%가 35가지 주제로 수렴되었고, 학생이 챗봇에 주로 물어보는 주제를 추출할 수 있었다. 학년말에 학생을 대상으로 한 설문에서 챗봇이 캐러셀 형태로 결과를 출력하는 형태가 학습에 효과적이었고, 통합과학 학습과 학습 목적 이외의 궁금증이나 학교생활과 관련된 물음에 답해주는 역할을 수행했음을 확인할 수 있었다. 본 연구는 공교육 현장에서 학생이 실제로 활용하기에 적합한 챗봇을 개발하여 학생이 장기간에 걸쳐 챗봇을 사용하는 과정에서 얻은 데이터를 분석함으로써 학생의 요구를 충족할 수 있는 챗봇의 교육적 활용 가능성을 확인했다는 점에 의의가 있다.

• 한국지반공학회논문집
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• 제32권6호
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• pp.27-40
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• 2016

기존의 강우에 의한 사면붕괴 기준들은 지형학적 특성, 임상특성, 불포화 지반 조건 등을 반영하지 않아 사면 파괴 예측 지표로 활용하기에는 많은 문제점을 가지고 있다. 본 연구에서는 강우패턴, 지형학적 특성(경사, 토심), 공학적 특성(불포화 지반 특성), 임상특성(식생 뿌리에 의한 영향, 수목하중) 등을 반영할 수 있는 불포화 사면 안정성 해석 방법을 제안하였다. 뿌리보강과 수목하중을 고려하지 않는 사면의 안전율 평가는 안전율을 과다 평가하는 것으로 나타났다. 제안된 안정성 해석 방법에 의하여 강우에 의한 불포화사면의 안전율에 대한 데이터베이스를 구축하고, 이를 GIS와 연계하여 울주군 산사태위험 지정지역에 적용하여 강우에 대한 사면 위험성을 정량적으로 분석하였다. 산사태위험지역은 강우에 의한 사면의 안전율이 낮거나, 대부분 안전율 감소가 급격하게 발생한 지점의 하류에 위치하고 있음을 알 수 있다.

• 환경영향평가
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• 제8권4호
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• pp.59-72
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• 1999

The purpose of the study is to propose the concrete and realistic alternative measures for $CO_2$ emission reduction on commercial sector. To achieve the purpose, this study adopted AIM/KOREA simulation model modified from AIM(Asia-Pacific Integrated Model) originally developed by Japan National Environmental Research Institute. The results of simulation demonstrate that the $CO_2$ emission from the commercial sector in 1995 was estimated 864 million TC(tons of carbon); however, according to the base scenario, $CO_2$ emission in 2020 is expected to be increased to 1,872 million TC, which is 2.17 times greater than that in 1995. In order to mitigate the ever-increasing $CO_2$ emission, the results of AIM/KOREA simulations under various scenarios showed that the 30-thousand-won carbon tax scenario does not successfully motivate the selection of advanced technology; however, with the 300-thousand-won carbon tax, a substantial amount of $CO_2$ emission reduction by 1.69 million TC from the BaU((Business-as-Usual)scenario is expected to be achieved by year 2020. Such substantial reduction of $CO_2$ emission under the 300-thoudsand-won carbon tax scenario is due to the introduction of advanced technology, such as use of condensing boilers, forced by heavier carbon tax. Under the scenario that presumes the maximum introduction of gas-burning industrial appliances, an 2.66 million TC of $CO_2$ reduction was expected. The results of this study suggest that the $CO_2$ emission reduction measures can be interpreted in many different views. However, if people and industries are fully aware of the economic benefit of energy saving, a certain level of $CO_2$ reduction by a successful introduction of advanced energy saving technology appears to be achieved without carbon tax or subsidies.

• 환경영향평가
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• 제12권1호
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• pp.23-34
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• 2003

The emergency response modeling system CARIS has been developed at CCSM (Center for Chemical Safety Management), NIER (National Institute of Environmental Research) to track and predict dispersion of hazardous chemicals for the environmental decision support in case of accidents at chemical or petroleum companies in Korea. The main objective of CARIS is to support making decision by rapidly providing the key information on the efficient emergency response of hazardous chemical accidents for effective approaches to risk management. In particular, the integrated modeling system in CARIS consisting of a real-time numerical weather forecasting model and air pollution dispersion model is supplemented for the diffusion forecasts of hazardous chemicals, covering a wide range of scales and applications for atmospheric information. In this paper, we introduced the overview of components of CARIS and described the operational modeling system and its configurations of coupling/integration in CARIS. Some examples of the operational modeling system is presented and discussed for the real-time risk assessments of hazardous chemicals.

• 지질공학
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• 제34권1호
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.