• 한국수자원학회논문집
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• 제54권8호
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• pp.617-628
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• 2021

가뭄은 심각한 사회적 경제적 손실을 초래하는 주요 자연재해이다. 지역 가뭄 예측은 가뭄 대비에 중요한 정보를 제공할 수 있다. 본 연구에서는 한반도 동남부 부산-울산-경남 지역에서 1981년부터 2020년까지 10개 관측소의 과거 가뭄지수 및 기상 관측자료를 사용하여 가뭄을 예측하는 새로운 기계학습모델을 제안한다. 베이지안 최적화기법을 이용하여 하이퍼 파라미터가 튜닝된 Random Forest, XGBoost, Light GBM 모델을 구축하여 1개월 뒤의 6개월 시간 척도의 증발 수요 가뭄지수를 예측하였다. 단일 지점별 모델과 지역 모델을 각각 구성하여 모델 성능을 비교하였다. 또한 지역 모델을 기반으로 개별 지점의 자료에 대해 미세조정된 모델을 구성하여 모델 성능을 높일 가능성을 살펴보았다.

• Structural Engineering and Mechanics
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• 제86권5호
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• pp.673-686
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• 2023

Determining the properties of pile from cone penetration test (CPT) is costly, and need several in-situ tests. At the present study, two novel hybrid learning models, namely PSO-RF and HHO-RF, which are an amalgamation of random forest (RF) with particle swarm optimization (PSO) and Harris hawks optimization (HHO) were developed and applied to predict the pile set-up parameter "A" from CPT for the design aim of the projects. To forecast the "A," CPT data along were collected from different sites in Louisiana, where the selected variables as input were plasticity index (PI), undrained shear strength (S_u), and over consolidation ratio (OCR). Results show that both PSO-RF and HHO-RF models have acceptable performance in predicting the set-up parameter "A," with R² larger than 0.9094, representing the admissible correlation between observed and predicted values. HHO-RF has better proficiency than the PSO-RF model, with R² and RMSE equal to 0.9328 and 0.0292 for the training phase and 0.9729 and 0.024 for testing data, respectively. Moreover, PI and OBJ indices are considered, in which the HHO-RF model has lower results which leads to outperforming this hybrid algorithm with respect to PSO-RF for predicting the pile set-up parameter "A," consequently being specified as the proposed model. Therefore, the results demonstrate the ability of the HHO algorithm in determining the optimal value of RF hyperparameters than PSO.

• 산업경영시스템학회지
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• 제47권1호
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• pp.41-50
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• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

• Advances in materials Research
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• 제13권1호
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• pp.63-86
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• 2024

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.

• 대한원격탐사학회지
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• 제35권6_3호
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• pp.1285-1298
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• 2019

가뭄은 산불을 일으킬 수 있는 요소 중 하나로, 산불의 빈도 및 피해 면적과 연관성이 있다. 특히, 우리나라는 가뭄이 주로 발생하는 건조한 봄과 가을에 산불이 많이 발생하고, 그 중 일부는 강풍을 동반하여 대형산불로 번지는 경향을 보인다. 따라서 본 연구에서는 우리나라를 대상으로 산불발생 및 면적과 가뭄 변수의 관련성을 파악하고, 우리나라에 적합한 가뭄 변수를 이용하여 산불발생위험 추정을 위한 위성기반의 가뭄지수를 개발하였다. 사용한 가뭄 변수는 다운스케일링(downscaling)한 고해상도의 토양수분, Normalized Different Water Index(NDWI), Normalized Multi-band Drought Index(NMDI), Normalized Different Drought Index(NDDI), Temperature Condition Index(TCI), Precipitation Condition Index(PCI), Vegetation Condition Index(VCI)이며, 경험적 가중 선형조합(Weighted Linear Combination) 및 One-class SVM을 통해 지수 개발을 하였다. 2013년부터 2017년 기간 동안의 변수를 이용하여 상관성 분석을 통해 대부분의 가뭄 변수가 산불 발생에 유의미한 결과를 보임을 확인했으며, 특히 토양수분과 NDWI, PCI가 우리나라 산불과 상관성을 보였다(88 % 이상 일치함). 개발된 지수를 2018년 산불 발생 건에 대해 적용한 결과, 다섯 가지의 선형조합 중에서 토양수분과 NDWI의 조합이 시 공간적으로 적합한 것으로 나타났으며, One-class SVM은 대형산불에 적합한 것으로 나타났다.

• 한국잡초학회지
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• 제17권4호
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• pp.382-389
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• 1997

말레이지아 Selangor지역(地域)의 부식질 경지토양(耕地土壤)에 분포(分布)하고 있는 경지(耕地)의 잡초식생(雜草植生)을 조사하고 정량적 생태분석을 수행한 결과는 다음과 같이 요약(要約)된다. 1. 이 지역(地域) 경지잡초(耕地雜草) 종조성(種組成)은 19과(科)에 속하는 31종(種)의 광엽잡초(廣葉雜草), 10종(種)의 화본과(禾本科) 잡초(雜草), 7종(種)의 사초과(莎草科) 잡초(雜草)로 이루어졌고, 잡초(雜草)의 식생피도(植生被度)에서 각각 77. 8. 15%를 점유하였으며, 이들의 중요도(重要度)는 각각 71, 11, 18%이었다. 2. 이 지역(地域) 경지잡초(耕地雜草)중 10종(種)의 우점종(優點種)은 우점도(優點度) 순위(順位)에서 Fimbristylis acuminata, Murdannia nudiflora, Hedyotis corymbosa, Ageratum conyzoides, Asystasia gangetica, Cleome rutidosperma, Cyperus sphacelatus, Lindernia crustacea, Ludwigia hyssopiflora 및 Ludwigia perennis이었다. 3. 이상과 같은 초종간(草種間) 우점도(優點度) 및 공간분포유형(空間分布類型)의 차이(差異)는 각 초종(草種) 본래(本來)의 종자생산성(鐘子生産性) 및 번식습성(繁殖習性)과 군집성(群集性)의 차이(差異)와 이 지역(地域) 경지(耕地)의 작물종류(作物種類), 작부체계(作付體系) 및 경종방법(耕種方法)과 제초관행(除草慣行)의 차이(差異)에 기인하는 것으로 생각한다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.114-119
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• 2008

A refined three-dimensional finite element interaction model between the high-speed train and railway bride deck has been developed in the present study. Analytical predictions of vertical deflections for a railway bridge are compared with in-situ test results and a good agreement is achieved. Then, input variables employed in the analytical comparisons are selected as random variables for the limit state functions. followed by risk assessment. For this purpose, a linear adaptive weighted response surface method has been developed and applied. A typical railway bridge has been selected and the limit state functions are employed from UIC and Korean specifications in the comparative studies. The results reveal that Korean specifications give significantly risky reliability indices in comparison with UIC specifications. It is thus encouraged from the above that the present linear adaptive weighted response surface method can be an alternative for the fast estimation of nonlinear structural systems.

• 전기학회논문지
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• 제58권3호
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• pp.486-495
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• 2009

This paper proposes a new methodology on reliability evaluation of a power system including solar cell generators (SCG). The SCGs using renewable energy resource such as solar radiation(SR) should be modeled as multi-state operational model because the uncertainty of the resource supply may occur an effect as same as the forced outage of generator in viewpoint of adequacy reliability of system. While a two-state model is well suited for modeling conventional generators, a multi-state model is needed to model the SCGs due to the random variation of solar radiation. This makes the method of calculating reliability evaluation indices of the SCG different from the conventional generator. After identifying the typical pattern of the SR probability distribution function(pdf) from SR actual data, this paper describes modelling, methodology and details process for reliability evaluation of the solar cell generators integrated with power system. Two test results indicate the viability of the proposed method.

• Structural Engineering and Mechanics
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• 제65권6호
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• pp.751-760
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• 2018

Damages in concrete structures due to aging and other factors could be a serious and immense matter. Making the best selection of the most viable and practical repairing and strengthening techniques are relatively difficult tasks using traditional methods of structural analyses. This is due to the fact that the traditional methods used for assessing aging structure are not fully capable when considering the randomness in strength, loads and cost. This paper presents a reliability-based methodology for assessing reinforced concrete members. The methodology of this study is based on probabilistic analysis, using statistics of the random variables in the performance function equations. Principles of reliability updating are used in the assessment process, as new information is taken into account and combined with prior probabilistic models. The methodology can result in a reliability index ${\beta}$ that can be used to assess the structural component by comparing its value with a standard value. In addition, these methods result in partial safety factor values that can be used for the purpose of strengthening the R/C elements of the existing structure. Calculations and computations of the reliability indices and the partial safety factors values are conducted using the First-order Reliability Method and Monte Carlo simulation.

• Journal of the Korean Wood Science and Technology
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• 제22권3호
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• pp.26-31
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• 1994

To provide further information for reutilization of the waste wood obtained after cultivating oak mushroom in Kangwon-do, the crystal structures of the waste wood were investigated and compared to those of normal woods by a series of x-ray diffraction analysis. The results obtained are as follows: 1. An x-ray diffraction diagram of cultivated wood for 5 years was same as that of typical cellulose with some orientation of cellulose crystallites, but that of cultivated wood for 8 years a random. 2. Crystallinity indices in normal and cultivated woods for 5 years ranged from 57% to 60%. In the cultivated wood for 8 years, however, the value showed about 40%. 3. Crystallite widths of cultivated woods for 5 years and for 8 years were 3 nm and 2.5 nm, respectively. 4. Intensity ratios of equatorial and meridional layers did not show any significant differences. From the above results, it is clear that the waste wood obtained after cultivating oak mush room for 5 years showed basically same crystal structures with normal wood. Therefore, we think that the waste wood may be used available for cellulosic material instead of normal wood.