• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.31-47
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• 2022

The purpose of this study is to develop a reliable tool that can classify and measure detailed indicators related to the performance of ESG implementation in the country and verify their applicability. Based on World Bank's data as input data, 67 types of ESG-related detailed indicators measured in a total of 239 countries were tested to derive an optimal model that could group detailed indicators into three categories: environment, society, and governance. As a result of the analysis, it was confirmed that a total of 10 detailed indicators had a statistically significant relationship with the country's ESG performance. In addition, the detailed indicators showed a positive correlation with the primary latent variables E, S, and G, and showed a high overall index in the suitability of the model to secure the validity and reliability of variable input. As a result, this study confirmed that several detailed performance indicators constituting ESG can be classified as latent variables, and it can be said that clear criteria for the selection method and input validity of variables were presented.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.477-477
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• 2023

빗물이용시설은 집수면적에 내린 빗물을 모아 이용가능하도록 처리하는 시설이며, 일정 면적 이상의 건축물에는 법적으로 빗물이용시설을 설치·운영하여야한다. 빗물이용시설의 저류조 용량은 간편식과 시뮬레이션, 수문모형으로 산정가능하며, 설계계획 수립시 대상지역의 강우 특성, 사용수량 등 지역 특성과 목적을 고려하여 저류조 용량이 결정된다. 저류조 용량 산정시 시뮬레이션을 이용하는 방법은 수문모형 사용에 비하여 용이하지만, 일단위 물수지분석을 구현하는데까지 시간이 소요된다. 간편식은 집수면적에 규모계수 0.05를 곱하여 간단히 구할 수 있지만, 지역 특성과 목적이 고려되어있지 않으며 초기 계획수립 및 개략 평가를 제외하고는 활용에 제약이 존재한다. 이에따라, 본 연구에서는 지역적 특성을 고려한 빗물저류조의 적정 규모 산정을 위해 개선된 간편식을 개발하였다. 빗물이용시설 물수지 분석 Excel 도구를 개발하였으며, 해당 물수지분석 결과에 상수대체율 효율을 기준으로 지역별 적정 저류조 규모 산정을 위한 규모계수를 도출하였다. 빗물사용 용도로써 폭염저감, 미세먼지저감, 조경, 화장실을 채택하였으며, 용도별 1일 사용수량을 산정 및 적용하였다. 7개의 연구대상지역 물수지분석을 위해 연구지역의 최근 10년 강우·미세먼지·기온데이터를 기상청으로부터 적용하였으며, 집수면적은 500-2500m²까지 500m²씩 증분, 저류조용량은 5-700m³까지 5m³씩 증분하여 지역별 적정 저류조 용량 규모계수를 선정하였다. 그 결과 연구대상지역의 적정 저류조 용량산정시 완도군의 규모계수는 평균 0.058이었으며, 보령시의 규모계수는 평균 0.040으로 도출되었다. 본 연구를 통하여 다양한 용도의 빗물사용처에 따른 지역별 저류조 용량 선정을 위한 지원도구로써 사용될 것으로 판단한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.353-353
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• 2022

본 연구에서는 CMIP5(The fifth phase of the Couple Model Intercomparison Project) 미래기후시나리오와 LSTM(Long Short-Term Memory) 모형 기반의 딥러닝 기법을 이용하여 하천유량 예측을 위한 최적 학습 기간을 제시하였다. 연구지역으로는 진안군(성산리) 지점을 선정하였다. 보정(2000~2002/2014~2015) 및 검증(2003~2005/2016~2017) 기간을 설정하여 연구지역의 실측 유량 자료와 LSTM 기반 모의유량을 비교한 결과, 전체적으로 모의값이 실측값을 잘 반영하는 것으로 나타났다. 또한, LSTM 모형의 장기간 예측 성능을 평가하기 위하여 LSTM 모형 기반 유량을 보정(2000~2015) 및 검증(2016~2019) 기간의 SWAT 기반 유량에 비교하였다. 비록 모의결과에일부 오차가 발생하였으나, LSTM 모형이 장기간의 하천유량을 잘 산정하는 것으로 나타났다. 검증 결과를 기반으로 2011년~2100년의 CMIP5 미래기후시나리오 기상자료를 이용하여 SWAT 기반 유량을 모의하였으며, 모의한 하천유량을 LSTM 모형의 학습자료로 사용하였다. 다양한 학습 시나리오을 적용하여 LSTM 및 SWAT 모형 기반의 하천유량을 모의하였으며, 최적 학습 기간을 제시하기 위하여 학습 시나리오별 LSTM/SWAT 기반 하천유량의 상관성 및 불확실성을 비교하였다. 비교 결과 학습 기간이 최소 30년 이상일때, 실측유량과 비교하여 LSTM 모형 기반 하천유량의 불확실성이 낮은 것으로 나타났다. 따라서 CMIP5 미래기후시나리오와 딥러닝 기반 LSTM 모형을 연계하여 미래 장기간의 일별 유량을 모의할 경우, 신뢰성 있는 LSTM 모형 기반 하천유량을 모의하기 위해서는 최소 30년 이상의 학습 기간이 필요할 것으로 판단된다.

• Smart Structures and Systems
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• v.30 no.4
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.70-78
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• 2023

Inclinometer has several problems of 1)difficulty installing inclinometer casing, 2) measuring 2D local lateral displacement of retaining wall, 3) measurement by manpower. To solve such problems, a 2D LiDAR sensor-based retaining wall displacement measurement system was developed in previous studies. The purpose of this study is to select a displacement analysis algorithm to be applied in the retaining wall displacement measurement system. As a result of the displacement analysis algorithm selection, the M3C2 (Multiple Model to Model Cloud Comparison) algorithm with a displacement estimation error of 2mm was selected as the displacement analysis algorithm. If the M3C2 algorithm is applied in the system and the reliability of the displacement analysis result is secured through several field experiments. Convenient management of the displacement for the retaining wall is possible in comparison with the current measurement management.

• Geomechanics and Engineering
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• v.32 no.6
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• pp.583-600
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• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.

• 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.

• Convergence Security Journal
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• v.22 no.2
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• pp.105-114
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• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.

• Journal of Environmental Science International
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• v.31 no.11
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• pp.941-950
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• 2022

This study was conducted to provide data and stem information to establish a local volume table of Cryptomeria japonica in Jeju Island. Stem analysis was performed on 26 trees by selecting two average trees from each site of the 13 plots of C. japonica stands in 2021 and 2022. During the analysis stage, one outlier tree was rejected, and a total of 260 observations of the specific stem height of 25 trees were used. Of the seven major taper equation models applied for parameter estimation and statistical verification, the Muhairwe 1999 model was found to be the best fit and selected as the optimal model. Stem shape-related estimates were acquired through the selected model, and sectional measurements according to the Smalian formula applied at an interval of 10 cm from the height of the stem were used to develop a volume table. A paired t-test comparison between the C. japonica volume obtained from the present study and those selected from the current yield table by NIFoS(2020), revealed significant differences (p<0.05), highlighting the necessity of a local volume table for C. japonica in Jeju Island.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.677-683
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• 2023

The purpose of the study was to estimate the trajectories of elementary school adjustment in grades 1 through 4. For the purpose, the Korean Children's Panel data were analyzed using potential growth model and the growth mixture model. As the result, the linear model was selected as the optimal model. The four potential groups were derived as trajectories: high-level maintenance, low-level maintenance, low-level increase, and high-level decrease. In terms of group distribution, the most children were in high-level maintenance group and then low-level maintenance, low-level increase, and high-level decrease in order. Based on the findings that trajectories of elementary school adjustment changes as children growth, we suggest that schools and families need to carefully investigate and support their school adjustment in individual levels.