• 한국태양에너지학회 논문집
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• 제39권3호
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• Geomechanics and Engineering
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• 제33권1호
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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.

• 한국기후변화학회지
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• 제6권1호
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• pp.21-31
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• 2015

Various crop models have been extensively used for estimation of the crop yields. Compared to the other models, the EPIC model uses a unified approach to simulate more than 100 types of crops. It has been successfully applied in simulating crop yields for various combinations of weather conditions, soil properties, crops, and management schemes in many countries. The objective of this study was to estimate the rice and maize yield in South Korea using the EPIC model. The input datasets for the 30 types in the 11 categories were created for the EPIC model. The EPIC model simulated rice and maize yields. The performance of the EPIC model was evaluated with the goodness-of-fit measures including Root Mean Square Error (RMSE), Relative Error (RE), Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE), and Pearson Correelation Coefficient (r). The rice yield showed to more high accuracy than maize yield on four type of method without NSEC. Theses results showed that the EPIC model better simulated rice yields than maize yields. The results suggest that the EPIC crop model can be useful to estimate crop yield in South Korea.

• 한국항만경제학회지
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• 제26권1호
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• pp.222-233
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• 2010

해상운임의 변동은 해운업계에만 영향을 미치는데 그치지 않고 전후방 연쇄효과를 통해 조선업계를 비롯하여 경제 전반에 영향을 미친다. 따라서 해상운임의 움직임을 정확히 예측하는 것은 해운업계 뿐만 아니라 우리나라 경제에도 중요한 의미를 갖게 된다. 그러나 해상운임은 주가나 환율과 같이 다양한 요인에 의해 결정될 뿐만 아니라 최근 들어 운임의 변동성이 크게 커지는 추세이어서 예측에 상당한 어려움이 있다. 본고는 2010년의 BDI를 예측하기 위하여 가장 단순한 모형인 단변량모형인 ARIMA 모형, 개입ARIMA모형, HP 모형을 이용한다. 개입ARIMA 모형은 글로벌 금융위기와 중국효과가 미친 효과를 분석하기 위한 것이다. ARIMA모형은 2010년 말에 4,230-4.690에 도달할 것으로, 개입ARIMA모형은 낙관적인 경우 4,460-4,900선에, 비관적일 경우 2,820-2,940선이 될 것으로 예상하여 모형별로 상당한 차이를 드러내고 있다. 그런데 HP 모형에 의한 예측치는 기준 역할을 하므로 HP모형에 의한 2010년 말 예측치 3,500 포인트를 감안하면 2010년 12월에 2,820-4,230의 범주에 도달할 것으로 예측된다. 2010년 12월 2,800 포인트는 해운업계에 어두운 그림자를 드리우는 예측치이다. 그러나 낙관적인 2010년 12월 4,000포인트는 2008년 BDI가 10,000 포인트를 넘어선 때를 기억하면 그리 높게 생각되지 않을 수 있으나 4,000 포인트 이상의 BDI는 해운관련업계에게 어느 정도의 안도감을 주고 재도약을 할 수 있는 기반을 제공할 수 있는 수준으로 판단된다.

• 한국전문물리치료학회지
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• 제26권1호
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• pp.1-7
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• 2019

Background: The Microsoft Kinect which is a low-cost gaming device has been studied as a promise clinical gait analysis tool having satisfactory reliability and validity. However, its accuracy is only guaranteed when it is properly positioned in front of a subject. Objects: The purpose of this study was to identify the error when the Kinect was positioned at a $45^{\circ}$ angle to the longitudinal walking plane compare with those when the Kinect was positioned in front of a subject. Methods: Sixteen healthy adults performed two testing sessions consisting of walking toward and $45^{\circ}$ obliquely the Kinect. Spatiotemporal outcome measures related to stride length, stride time, step length, step time and walking speed were examined. To assess the error between Kinect and 3D motion analysis systems, mean absolute errors (MAE) were determined and compared. Results: MAE of stride length, stride time, step time and walking speed when the Kinect set in front of subjects were investigated as .36, .04, .20 and .32 respectively. MAE of those when the Kinect placed obliquely were investigated as .67, .09, .37, and .58 respectively. There were significant differences in spatiotemporal outcomes between the two conditions. Conclusion: Based on our study experience, positioning the Kinect directly in front of the person walking towards it provides the optimal spatiotemporal data. Therefore, we concluded that the Kinect should be placed carefully and adequately in clinical settings.

• Structural Engineering and Mechanics
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• 제91권6호
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• pp.643-656
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• 2024

The prediction of the moment rotation behavior of semi-rigid connections has been the subject of extensive research. However, to improve the accuracy of these predictions, there is a growing interest in employing machine learning algorithms. This paper investigates the effectiveness of using Multi-gene genetic programming (MGGP) to predict the moment-rotation behavior of flush-end plate connections compared to that of artificial neural networks (ANN) and previous studies. It aims to automate the process of determining the most suitable equations to accurately describe the behavior of these types of connections. Experimental data was used to train ANN and MGGP. The performance of the models was assessed by comparing the values of coefficient of determination (R²), maximum absolute error (MAE), and root-mean-square error (RMSE). The results showed that MGGP produced more accurate, reliable, and general predictions compared to ANN and previous studies with an R² exceeding 0.99, an RMSE of 6.97, and an MAE of 38.68, highlighting its advantages over other models. The use of MGGP can lead to better modeling and more precise predictions in structural design. Additionally, an experimentally-based regression analysis was conducted to obtain the rotational capacity of FECs. A new equation was proposed and compared to previous ones, showing significant improvement in accuracy with an R² score of 0.738, an RMSE of 0.014, and an MAE of 0.024.

• 한국자기학회지
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• 제26권3호
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• pp.92-98
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• 2016

신호증강효과기법을 이용한 자기공명혈관술에서 뇌동맥을 half scan factor에 따른 절반스캔과 완전스캔의 영상을 평가하는데 목적으로 한다. 뇌혈관성 질환이 없는 환자(n = 30)를 대상으로 절반스캔과 완전스캔 하였고, 뇌동맥의 관심영역을 세 영역(C1, C2, C3)에서 7~8 mm의 범위로 설정하였다. MIP로 재구성한 영상으로 신호강도를 SNR(signal to noise ration), PSNR(peak signal noise to ratio), RMSE(root mean square error), MAE(mean absolute error)을 산출하고 paired t-test를 이용하여 통계분석 하였다. 스캔시간은 절반스캔(4분 53초), 완전스캔(6분 04초)이었다. 뇌혈관의 모든 ROI의 평균 측정 범위(7.21 mm)이었고, 첫번째 C1의 SNR은 완전스캔(58.66 dB), 절반스캔(62.10 dB)이었고, 양의 상관관계($r^2=0.503$)이고, 두 번째 C2의 SNR은 완전스캔(70.30 dB), 절반스캔(74.67 dB)이고 양의 상관관계($r^2=0.575$)이었다. 세 번째 C3의 완전스캔 SNR(70.33 dB), 절반스캔 SNR (74.64 dB)로 양의 상관관계를 ($r^2=0.523$)로 분석되었다. 절반스캔과 완전스캔의 비교에서 SNR($4.75{\pm}0.26dB$), PSNR($21.87{\pm}0.28dB$), RMSE($48.88{\pm}1.61$)이었고 MAE($25.56{\pm}2.2$)로 산출되었다. SNR은 두 검사 스캔에서 통계학적으로 유의하지 않았고 (p-value > .05) 영상의 질에서는 많은 차이가 없어 완전스캔을 사용하였을 때보다 적은 시간이 소요되는 절반스캔을 적용하여 검사하여도 된다.

• 대한안과학회지
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• 제59권12호
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• pp.1173-1180
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• 2018

목적: 원발개방각녹내장 환자에서 섬유주절제술과 백내장수술을 동시에 시행하는 경우 술 후 예측 굴절력의 정확성을 분석하고, 굴절력 오차에 영향을 미치는 요소에 대해 알아보고자 한다. 대상과 방법: 섬유주절제술과 백내장수술을 동시에 시행한 원발개방각녹내장 환자 27명을 대상으로 후향적으로 분석하였다. 술 후 예측 굴절력과 실제 굴절력을 비교하였으며 평균예측오차와 평균절대예측오차를 계산하였다. 같은 시기에 백내장수술만 단독 시행한 나이와 성별이 짝지어지는 대조군과 굴절력 오차를 비교하였다. 또한 굴절력 오차에 영향을 미치는 환자의 수술 전 인자들에 대하여 통계학적으로 분석하였다. 결과: 술 후 평균예측오차는 섬유주절제술과 백내장수술의 병합수술군에서 $+0.02{\pm}0.92$디옵터, 대조군에서 $-0.01{\pm}0.45$디옵터로 유의한 차이는 없었다. 평균절대예측오차는 병합수술군에서 $0.65{\pm}0.64$디옵터, 대조군에서 $0.35{\pm}0.28$디옵터로, 술 후 난시는 병합수술군에서 $-1.07{\pm}0.65$디옵터, 대조군에서 $-0.66{\pm}0.48$디옵터로 병합수술군에서 유의하게 높았다(p=0.035, p=0.020). 술 전 앞방깊이, 술 후 안압 변화가 부정확한 평균절대예측오차와 유의한 연관성을 보였다. 결론: 원발개방각녹내장 환자에서 섬유주절제술과 백내장수술을 동시에 시행하는 경우 술 후 예측 굴절력의 정확성이 떨어지고, 더 큰 난시를 유발하는 것으로 나타났다. 술 전 얕은 앞방깊이, 술 후 큰 안압 변화가 굴절력 오차를 증가시키므로 이를 고려하여야 하겠다.

• Geomechanics and Engineering
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• 제12권3호
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• 한국지반신소재학회논문집
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• 제19권1호
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• pp.11-23
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• 2020

본 본문에서는 도심지 지하굴착 및 터널현장의 예비설계 및 지반침하를 예측이 가능한 인공신경망 개발에 대한 내용을 다루었다. 인공신경망의 개발을 위해 먼저 다양한 도심지 터널 및 지하굴착 현장 계측자료를 수집하여 데이터베이스를 구축하고 이를 인공신경망 학습에 필용한 학습데이터를 구축하는데 활용하였다. 개발된 인공신경망은 학습에 활용되지 않은 검증 데이터 세트를 및 현장계측자료를 활용하여 결정계수(R²), 평균제곱근오차(Root Mean Square Error; RMSE), 절대평균오차(Mean Absolute Error; MAE) 등 통계적 파라메타를 근거로 하여 신뢰도를 검증하였다. 개발된 인공신경망은 도심지 굴착현장의 예비 설계 및 이에 따른 주변침하를 예측하는데 효율적으로 활용될 수 있는 것으로 평가되었다.