• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2011년도 춘계학술대회 논문집
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• pp.889-890
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• 2011

• 유통과학연구
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• 제12권2호
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Environmental Engineering Research
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• 제25권3호
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• pp.418-425
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• 2020

In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R² values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

• 한국물환경학회지
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• 제37권5호
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• 한국초지조사료학회지
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• 제34권3호
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• pp.214-218
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• 2014

This study was conducted to assess the feasibility of near-infrared reflectance spectroscopy (NIRS) as a rapid and reliable method for the estimation of crude protein (CP) fractions in forage legume mixtures (sudangrass and pea mixture, and kidney bean and potato mixture). A total of 178 samples were collected and their spectral reflectance obtained in the range of 400~2,500 nm. Of these, 50 samples were selected for calibration and validation, and 35 samples were used for calibration of the data set, and the modified partial least square regression (MPLSR) analysis was performed. The correlation coefficient ($r^2$) and the standard error of cross-validation (SECV) of the calibration models in the CP fractions, A, B1, B2, B3, and C, were 0.94 (1.05), 0.92 (0.74), 0.96 (0.95), 0.91 (0.42), and 0.83 (0.38), respectively. Fifteen samples were used for equation validation, and the $r^2$ and the standard error of prediction (SEP) were 0.87 (1.45), 0.91 (0.49), 0.94 (1.13), 0.36 (0.96), and 0.74 (0.67), respectively. This study showed that NIRS could be an effective tool for the rapid and precise estimation of CP fractions in forage legume mixtures.

• Journal of Biosystems Engineering
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• 제28권4호
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• pp.369-376
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• 2003

This study was conducted to develop a precision measurement method of water content in soil (find sand and silty sand) using dual RF impedance changes. The electrically stable perpendicular plate capacitive sensor was fabricated and utilized to sense the water content in soil. Crystal oscillators of 5 and 20 MHz and related circuits were designed to detect the capacitance changes of a perpendicular plate capacitive sensor with soil samples at various volumetric water contents. A multiple regression model for volumetric water content having dual oscillation frequency changes at 5 and 20 MHz as independent variables resulted in coefficient of determination of 0.963 and standard error calibration of 0.030 cm$^3$/cm$^3$ for calibration and coefficient of determination of 0.966, standard error of prediction of 0.027 cm$^3$/cm$^3$ and bias of 0.001 cm$^3$/cm$^3$ for prediction.

• 한국해양공학회지
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• 제34권3호
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• pp.167-179
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• 2020

Recently, the increasing importance of artificial intelligence (AI) technology has led to its increased use in various fields in the shipbuilding and marine industries. For example, typical scenarios for AI include production management, analyses of ships on a voyage, and motion prediction. Therefore, this study was conducted to predict a response amplitude operator (RAO) through AI technology. It used a neural network based on one of the types of AI methods. The data used in the neural network consisted of the properties of the vessel and RAO values, based on simulating the in-house code. The learning model consisted of an input layer, hidden layer, and output layer. The input layer comprised eight neurons, the hidden layer comprised the variables, and the output layer comprised 20 neurons. The RAO predicted with the neural network and an RAO created with the in-house code were compared. The accuracy was assessed and reviewed based on the root mean square error (RMSE), standard deviation (SD), random number change, correlation coefficient, and scatter plot. Finally, the optimal model was selected, and the conclusion was drawn. The ultimate goals of this study were to reduce the difficulty in the modeling work required to obtain the RAO, to reduce the difficulty in using commercial tools, and to enable an assessment of the stability of medium/small vessels in waves.

• Journal of Biosystems Engineering
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• 제26권5호
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• pp.475-480
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• 2001

Sensing soil organic matter is crucial for precision farming and environment friendly agriculture. Near infra-red(NIR) was utilized to measure the soil organic matter. Multivariate calibration methods, including stepwise multiple linear regression(MLR), principal components recession(PCR) and partial least squares regression(PLS), were applied to soil spectral reflectance data to predict the organic matter content. The effect of soil particle size and water content was studied. The range of soil organic matter contents was from 0.5 to 11%. Near infrared (NIR) region from 700 to 2,500nm was applied. For uniform soil particle size, result had good correlation (R$\^$2/ = 0.984, standard error of prediction= 0.596). The effect of soil particle size could be eliminated with 1st order derivative of the NIR signal. However. moist soil had a little lower correlation. R$\^$2/ was 0.95 and standard error of prediction was 0.94% using the PLS method. The results showed the possibility of soil organic matter measurement using NIR reflectance on the field.

• 한국식품저장유통학회지
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• 제12권6호
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• pp.608-612
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• 2005

친환경적이면서 신속한 비파괴 분석방법인 FT-NIR를 이용하여 백미의 총식이섬유(TDF)함량 예측모델을 개발하였다. 백미는 국내산으로 전남지방에서 재배된 47개 품종과, 시중 유통 중인 13개 브랜드 미에 대해서 AOAC 방법에 준한 효소법에 의해 TDF 함량을 분석하였다. 습식 분석된 TDF함량의 범위는 $1.17-1.92\%$ 이었다. FT-NIR로 측정된 스펙트럼의 검량식은 빛의 산란 효과를 최소화하기 위해 수학적 처리를 하였고, 몇 개의 특정 파장이 아닌 전 파장 영역(1,000-2,500 nm)에 대해서 PLS법으로 작성하였다. 얻어진 검량식의 정확도는 상관계수(r), SEE 및 SEP로 확인하였다. 백미 중 총 식이섬유 함량에 대한 회귀분석을 행한 결과, 검량식의 r은 0.9705, SEE는 0.0464, 검증식의 bias는 -0.0006, SEP가0.0604로 측정 정확도가 우수하여 실제 적용이 가능함을 보여주었다.

• 자원환경지질
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• 제56권3호
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• pp.331-341
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• 2023

지구통계 기법을 기반으로 토양오염지도를 작성하는 경우 예측 오차가 발생하며 이에 영향을 미치는 다양한 원인이 존재한다. 본 연구에서는 정규 크리깅을 활용하여 폐광산지역의 토양 내 중금속 농도 샘플링 데이터로부터 격자형 기반의 토양오염지도를 작성하였다. 해당 지도의 예측 오차에 영향을 미친다고 판단된 5개 인자를 선정하고, Leave-one-out 기법을 기반으로 인자의 옵션과 설정값의 변화에 따른 예측값과 실측값 간의 평균제곱근오차(root mean square error, RMSE) 변화를 분석하였다. 이후 머신러닝 알고리즘을 이용하여 RMSE에 영향을 미치는 상위 3개 인자를 도출하였다. 그 결과, Standard interpolation에서는 Variogram Model, Minimum Neighbors, Anisotropy 인자가 RMSE에 가장 큰 영향을 미치는 것으로 분석되었다. 베리오그램 모델에서는 Spherical 모델이 가장 낮은 RMSE를 보였으며, Minimum Neighbors는 3에서 최젓값을 보인 후 값이 증가함에 따라 증가하였다. Anisotropy의 경우 이방성을 고려하지 않는 것이 더 적합한 것으로 나타났다. 본 연구에서는 지구통계와 머신러닝의 복합 활용을 통해 지역 규모에서 높은 신뢰성을 갖는 토양오염지도를 작성할 수 있었고, 적은 수의 토양 샘플링 데이터의 보간 작업 시 어떠한 요인들이 큰 영향을 미치는지 파악할 수 있었다.