• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.515-535
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• 2022

The strength models for fiber-reinforced polymer (FRP)-confined normal strength concrete (NC) cylinders available in the literature have been suggested based on small databases using limited variables of such structural members portraying less accuracy. The artificial neural network (ANN) is an advanced technique for precisely predicting the response of composite structures by considering a large number of parameters. The main objective of the present investigation is to develop an ANN model for the axial strength of FRP-confined NC cylinders using various parameters to give the highest accuracy of the predictions. To secure this aim, a large experimental database of 313 FRP-confined NC cylinders has been constructed from previous research investigations. An evaluation of 33 different empirical strength models has been performed using various statistical parameters (root mean squared error RMSE, mean absolute error MAE, and coefficient of determination R²) over the developed database. Then, a new ANN model using the Group Method of Data Handling (GMDH) has been proposed based on the experimental database that portrayed the highest performance as compared with the previous models with R²=0.92, RMSE=0.27, and MAE=0.33. Therefore, the suggested ANN model can accurately capture the axial strength of FRP-confined NC cylinders that can be used for the further analysis and design of such members in the construction industry.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.515-515
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• 2023

팔당호는 북한강과 남한강이 합류하여 생성된 호소로 수도인 서울과 수도권인 경기도 동부지역의 물 공급을 담당하는 중요한 상수원이다. 이러한 팔당호에서 유해남조 발생은 상수원수 활용과 직접적으로 연관되어 있어 신속하고 정확한 관리 및 예측이 필요하다. 본 연구에서는 안전한 상수원 활용을 위해, 딥러닝 기법을 이용하여 유해남조 사전 예측 모델을 구축하고자 하였다. 모델 입력 변수는 2012년부터 2021년까지 10년 동안의 주간 팔당호 수질(수온, DO, BOD, COD, Chl-a, TN, TP, pH, 전기전도도, TDN, NH4N, NO3N, TDP, PO4P, 부유물질)과 수문(유입량, 총방류량), 기상 정보(평균기온, 최저기온, 최고기온, 일 강수량, 평균풍속, 평균 상대습도, 합계일조량), 그리고 북한강과 남한강 유입지점의 남조 세포 수를 사용하였다. 모델 출력 변수는 수질, 수문, 기상 요인으로 인한 남조의 성장 발현 시기를 고려하여 1주 후의 댐앞 남조 세포수를 사용하였다. 사용한 딥러닝 기법은 최근 주목받고 있는 Temporal Fusion Transformer (TFT)를 사용하였다. 모델 훈련용 데이터와 테스트용 데이터는 각각 8:2의 비율로 나누었으며, 검증용 데이터는 훈련용 데이터 내에서 훈련 데이터와 검증 데이터를 6:4 비율로 분배하였다. Lookback은 5로 설정하였고, 이는 주단위 데이터로 구성된 데이터세트의 특성을 반영한 것이다. 모델의 성능은 실측값과 예측값을 토대로 R-square와 Root Mean Squared Error (RMSE)를 계산하여 평가하였다. 모델학습은 총 154번 반복 진행되었으며, 이 중 성능이 가장 준수한 시점은 54번째 반복 시점으로 훈련손실 대비 검증손실이 가장 양호한 값을 나타냈다(훈련손실:0.443, 검증손실 0.380). R-square는 훈련단계에서 0.681, 검증단계에서 0.654였고, 테스트 단계에서 0.606으로 산출되었다. RMSE는 훈련단계에서 0.614(㎍/L), 검증단계에서 0.617(㎍/L), 테스트 단계에서 0.773(㎍/L)였다. 모델에 사용한 데이터세트가 주간 데이터라는 특성을 고려하면, 소규모 데이터를 사용하였음에도 본 연구에서 구축한 모델의 성능은 양호하다고 평가할 수 있다. 향후 연구에서 데이터세트를 보강하고 모델을 업데이트한다면, 모델의 성능을 더욱더 개선할 수 있을 것으로 기대된다.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.193-203
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• 2024

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R²), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.221-228
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• 2024

Predicting insurance claims is a key task for insurance companies to manage risks and maintain financial stability. Accurate insurance claim predictions enable insurers to set appropriate premiums, reduce unexpected losses, and improve the quality of customer service. This study aims to enhance the performance of insurance claim prediction models by applying ensemble learning techniques. The predictive performance of models such as Random Forest, Gradient Boosting Machine (GBM), XGBoost, Stacking, and the proposed Dynamic Weighted Ensemble (DWE) model were compared and analyzed. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). Experimental results showed that the DWE model outperformed others in terms of evaluation metrics, achieving optimal predictive performance by combining the prediction results of Random Forest, XGBoost, LR, and LightGBM. This study demonstrates that ensemble learning techniques are effective in improving the accuracy of insurance claim predictions and suggests the potential utilization of AI-based predictive models in the insurance industry.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.3
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• pp.277-288
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• 2017

Previous studies of health effects of $PM_{2.5}$ performed spatial monitoring campaigns to assess spatial variability of $PM_{2.5}$ across people's residences. Highly reliable portable and cost-effective samplers will be useful for such campaigns. This study aimed to investigate applicability of the Deployable Particulate Impact Sampler(DPIS), one of the compact impact samplers, to spatial monitoring campaigns of $PM_{2.5}$ in Seoul, Korea. The investigation focused on the consistency of $PM_{2.5}$ concentrations measured by DPISs compared to those by the Low-volume Cyclone sampler (LCS). LCS has operated at a fixed site in the Seoul National University Yeongeon campus, Seoul, Korea since 2003 and provided qualified $PM_{2.5}$ data. $PM_{2.5}$ sampling of DPISs was carried out at the same site from November 17, 2015 through February 3, 2016. $PM_{2.5}$ concentrations were quantified by the gravimetric method. Using a duplicated DPIS, we confirmed the reliability of DPIS by computing relative precision and mean square error-based R squared value ($R^2$). Relative precision was one minus the difference of measurements between two samplers relative to the sum. For accuracy, we compared $PM_{2.5}$ concentrations from four DPISs (DPIS_Tg, DPIS_To, DPIS_Qg, and DPIS_Qo) to those of LCS. Four samplers included two types of collection filters(Teflon, T; quartz, Q) and impaction discs(glass fiber filter, g; pre-oiled porous plastic disc, o). We assessed accuracy using accuracy value which is one minus the difference between DPIS and LCS $PM_{2.5}$ relative to LCS $PM_{2.5}$ in addition to $R^2$. DPIS showed high reliability (average precision=97.28%, $R^2=0.98$). Accuracy was generally high for all DPISs (average accuracy=83.78~88.88%, $R^2=0.89{\sim}0.93$) except for DPIS_Qg (77.35~78.35%, 0.82~0.84). Our results of high accuracy of DPIS compared to LCS suggested that DPIS will help the assessment of people's individual exposure to $PM_{2.5}$ in extensive spatial monitoring campaigns.

• Environmental Analysis Health and Toxicology
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• v.29
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• pp.12.1-12.8
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• 2014

Objectives Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to $10{\mu}m$ in diameter ($PM_{10}$) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. Methods We obtained hourly $PM_{10}$ data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average $PM_{10}$ concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared ($R^2$) statistics were computed. Results Mean annual average $PM_{10}$ concentrations in the seven major cities ranged between 45.5 and $66.0{\mu}g/m^3$ (standard deviation=2.40 and $9.51{\mu}g/m^3$, respectively). Cross-validated $R^2$ values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had $R^2$ values of zero. The national model produced a higher cross-validated $R^2$ (0.36) than those for the city-specific models. Conclusions In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate $PM_{10}$ source characteristics.

• Journal of dental hygiene science
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• v.9 no.1
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• pp.53-59
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• 2009

Infection control is now recognized as an important quality indicator in dental health service setting. The purpose of this study was to develop and validate Dental Hygienist's Infection Control Practice Scale for quality management of dental health service in Korea. The data of 254 dental hygienists was subjected to exploratory factor analysis using SPSS 16.0 and confirmatory factor analysis using AMOS 16.0. The total items of preliminary scale were 21 items and 5 subscale. Principal component analysis was completed with Varimax rotation. The results show a change in factor structure from 5 factor solution to 4 factor solution. The confirmatory factor analysis confirmed the four subscales(Immunization and periodic tests, Clinical procedure, Handwashing, Personal protection) which have a total of 12 items. After the item deleted because factor loading was low, measured model was tested. The results of the measurement model indicated fit indices: $x^2$= 79.593(df = 38, 0 = 0.000), RMR = 0.045, GFI = 0.940, CFI = 0.904, AGFI = 0.896, NFI = 0.837, TLI = 0.861, RMSEA = 0.67. The squared correlation between four constructs were less than the average variance extracted(AVE) of four constructs. Multiple regression analysis was completed. Dependent variable was the perceived infection control practice by dental hygienist. Independent variables were four summated subscales(R = 0.552, $R^2$= 0.304, Adjusted $R^2$= 0.431, F = 25.813, p = 0.000). Unstandardized coefficients of three independent variables were statistically significant.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1317-1328
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• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.