• Tunnel and Underground Space
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• v.28 no.6
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• pp.651-669
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• 2018

This study suggests the prediction model to estimate the specific energy of a pick cutter using a gene expression programming (GEP) and particle swarm optimization (PSO). Estimating the performance of mechanical excavators is of crucial importance in early design stage of tunnelling projects, and the specific energy (SE) based approach serves as a standard performance prediction procedure that is applicable to all excavation machines. The purpose of this research, is to investigate the relationship between UCS and BTS, penetration depth, cut spacing, and SE. A total of 46 full-scale linear cutting test results using pick cutters and different values of depth of cut and cut spacing on various rock types was collected from the previous study for the analysis. The Mean Squared Error (MSE) associated with the conventional Multiple Linear Regression (MLR) method is more than two times larger than the MSE generated by GEP-PSO algorithm. The $R^2$ value associated with the GEP-PSO algorithm, is about 0.13 higher than the $R^2$ associated with MLR.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.65-72
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• 2021

We consider the joint optimization problem of transmit power level, MMSE receiver filter and access point(AP) selection for multi access points environment. In the previous work, transmit power and MMSE receiver filter were jointly optimized[1] and transmit power and best access point were optimized jointly[2]. For each case, the algorithm was proposed and its convergence which guarantees the minimum total transmit power was proved. In this paper, we further improve the algorithm by jointly optimizing three parameters. More specifically, 1) we propose the algorithm by considering transmit power, MMSE receiver filter and access point selection jointly. 2) we prove that the proposed algorithm guarantees convergence with minimum transmit power consumption. In the simulation results, it is shown that proposed algorithm outperforms two other algorithms, i.e., 1) algorithm with transmit power and MMSE receiver filter, and 2) algorithm with transmit power and best access point selection.

• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.145-159
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• 2021

Purpose: A statistical similarity evaluation to compare pharmacokinetics(PK) profile data between nonclinical and clinical experiments has become a significant issue on many drug development processes. This study proposes a new similarity index by considering important parameters, such as the area under the curve(AUC) and the time-series profile of various PK data. Methods: In this study, a new profile similarity index(PSI) by using the concept of a process capability index(Cp) is proposed in order to investigate the most similar animal PK profile compared to the target(i.e., Human PK profile). The proposed PSI can be calculated geometric and arithmetic means of all short term similarity indices at all time points on time-series both animal and human PK data. Designed simulation approaches are demonstrated for a verification purpose. Results: Two different simulation studies are conducted by considering three variances(i.e., small, medium, and large variances) as well as three different characteristic types(smaller the better, larger the better, nominal the best). By using the proposed PSI, the most similar animal PK profile compare to the target human PK profile can be obtained in the simulation studies. In addition, a case study represents differentiated results compare to existing simple statistical analysis methods(i.e., root mean squared error and quality loss). Conclusion: The proposed PSI can effectively estimate the level of similarity between animal, human PK profiles. By using these PSI results, we can reduce the number of animal experiments because we only focus on the significant animal representing a high PSI value.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.224-224
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• 2017

지구온난화로 인한 기후변화 등으로 안전한 하천구조물을 설계하기 위해서는 신뢰할 수 있는 홍수량 산정이 필요하다. 신뢰할 수 있는 홍수량 산정을 위해서는 정도 높은 과거 수문자료가 필요하나 국내의 많은 중소 규모유역이 미계측 유역 또는 과거 수문자료 부족으로 신뢰 할 수 있는 홍수량 산정이 어려운 실정이다. 본 연구에서는 미계측 유역의 홍수량 산정을 위하여 확률분포모형(PDM)의 매개변수 지역화를 수행하였다. 매개변수 지역화 연구를 수행하기 위하여, 금강 25개 유역을 대상으로 유역별 9~18개의 단기홍수수문사상을 선정하였다. 선정된 단기홍수수문사상을 확률분포모형에 적용하기위하여, MCAT (Monte Carlo Analysis Toolbox)을 활용하여 검정 및 검증을 수행하였으며, 목적함수는 수문곡선 모든 구간을 반영하는 NSE (Nash Sutcliffe Efficiency)와 고유량 부분을 반영하는 RMSE (Root Mean Squared Error) - FH를 적용하였다. 각각의 목적함수에 대하여 검정 모형 매개변수와 유역 특성인자의 다중 선형회귀식을 강우유출모형 매개변수 지역화 모형으로 제시하였다. 매개변수 지역화 결과의 평가를 위하여 청주 유역을 미계측 유역으로 가정하였다. 청주 유역에 대하여 지역화 매개변수를 적용한 결과, 17개의 사상 중 11개의 사상에서 NSE 목적함수 값이 0.5이상으로 전체적인 수문곡선의 경향성을 보였으며, 첨두 홍수량은 17개 사상 중 11개 사상에서 관측 첨두 홍수량 값의 20%이내를 제시하여 적합한 결과를 제시하였다. 또한 금강 25개 유역에 Jackknife 방법으로 검정 결과 관측 첨두 홍수량 값 20%이내의 성능을 보이는 사상이 56%를 포함하고 있어 의미있는 지역화 모형을 제시하였다고 판단된다. 본 연구에서 제시한 매개변수 지역화 방법은 미계측 유역의 유출모의에 활용될 수 있음을 확인하였다.

• Journal of Food Hygiene and Safety
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• v.36 no.6
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• pp.504-509
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• 2021

The objective of this study was to develop dynamic model to describe the kinetic behavior of E. coli in sliced smoked duck. E. coli was detected in 2 sliced smoked duck samples (16.7%) at 1.23 log CFU/g. The maximum specific growth rate (𝜇_max) of E. coli ranged from 0.05 to 0.36 log CFU/g/h, and lag phase duration (LPD) ranged from 4.39 to 1.07 h, depending on the storage at 10-30℃, and h₀ value ranged from 0.24 to 0.51. The developed model was validated with observed values obtained at 13℃ and 25℃. The model performance was appropriate with 0.130 of root mean squared error (RMSE), and the dynamic model also described properly kinetic behavior of E. coli in sliced smoked duck samples. These results indicate that E. coli can contaminate sliced smoked ducks and the models developed with the E. coli isolates are useful in describing the kinetic behavior of E. coli in sliced smoked duck.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.469-476
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• 2022

This study developed a Recurrent Neural Network model implemented through Long Short-Term Memory (LSTM) that generates long-term tidal level data at Busan Port using tide observation data. The tide levels in Busan Port were predicted by the Korea Hydrographic and Oceanographic Administration (KHOA) using the tide data observed at Busan New Port and Tongyeong as model input data. The model was trained for one month in January 2019, and subsequently, the accuracy was calculated for one year from February 2019 to January 2020. The constructed model showed the highest performance with a correlation coefficient of 0.997 and a root mean squared error of 2.69 cm when the tide time series of Busan New Port and Tongyeong were inputted together. The study's finding reveal that long-term tidal level data prediction of an arbitrary port is possible using the deep learning recurrent neural network model.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2334-2340
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• 2021

Radioactive particle tracking (RPT) is a minimally invasive nuclear technique that tracks a radioactive particle inside a volume of interest by means of a mathematical location algorithm. During the past decades, many algorithms have been developed including ones based on artificial intelligence techniques. In this study, RPT technique is applied in a simulated test section that employs a simplified mixer filled with concrete, six scintillator detectors and a¹³⁷Cs radioactive particle emitting gamma rays of 662 keV. The test section was developed using MCNPX code, which is a mathematical code based on Monte Carlo simulation, and 3516 different radioactive particle positions (x,y,z) were simulated. Novelty of this paper is the use of a location algorithm based on a deep learning model, more specifically a 6-layers deep rectifier neural network (DRNN), in which hyperparameters were defined using a Bayesian optimization method. DRNN is a type of deep feedforward neural network that substitutes the usual sigmoid based activation functions, traditionally used in vanilla Multilayer Perceptron Networks, for rectified activation functions. Results show the great accuracy of the DRNN in a RPT tracking system. Root mean squared error for x, y and coordinates of the radioactive particle is, respectively, 0.03064, 0.02523 and 0.07653.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.629-638
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• 2021

Background and objective: This study was conducted to develop diameter growth models for thinned Quercus glauca Thunb. (QGT) stands to inform production goals for treatment and provide the information necessary for the systematic management of this stands. Methods: This study was conducted on QGT stands, of which initial thinning was completed in 2013 to develop a treatment system. To analyze the tree growth and trait response for each thinning treatment, forestry surveys were conducted in 2014 and 2021, and a one-way analysis of variance (ANOVA) was executed. In addition, non-linear least squares regression of the PROC NLIN procedure was used to develop an optimal diameter growth model. Results: Based on growth and trait analyses, the height and height-to-diameter (H/D) ratio were not different according to treatment plot (p > .05). For the diameter of basal height (DBH), the heavy thinning (HT) treatment plot was significantly larger than the control plot (p < .05). As a result of the development of diameter growth models by treatment plot, the mean squared error (MSE) of the Gompertz polymorphic equation (control: 2.2381, light thinning: 0.8478, and heavy thinning: 0.8679) was the lowest in all treatment plots, and the Shapiro-Wilk statistic was found to follow a normal distribution (p > .95), so it was selected as an equation fit for the diameter growth model. Conclusion: The findings of this study provide basic data for the systematic management of Quercus glauca Thunb. stands. It is necessary to construct permanent sample plots (PSP) that consider stand status, location conditions, and climatic environments.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.6
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• pp.978-985
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• 2021

This study aims to propose a prediction model for the drape coefficient using artificial neural networks and to analyze the nonlinear relationship between the drape properties and physical properties of fabrics. The study validates the significance of each factor affecting the fabric drape through multiple linear regression analysis with a sample size of 573. The analysis constructs a model with an adjusted R² of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (m_warp, m_weft), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (c_warp, c_weft, c_bias), and force required for 1% tension in the warp direction (f_warp). Finally, an artificial neural network was created using seven selected factors. The performance was examined by increasing the number of hidden neurons, and the most suitable number of hidden neurons was found to be 8. The mean squared error was .052, and the correlation coefficient was .863, confirming a satisfactory model. The developed artificial neural network model can be used for engineering and high-quality clothing design. It is expected to provide essential data for clothing appearance, such as the fabric drape.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.4
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.