• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.1-7
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• 2007

In this paper, the necessary and sufficient conditions for strict positive realness of the rational transfer functions directly from basic definitions in the frequency domain are studied. A new frequency domain approach is used to check if a rational transfer function is a strictly positive real or not. This approach is based on the Taylor expansion and the Maximum Modulus Principle which are the fundamental tools in the complex functions analysis. Four related common statements in the strict positive realness literature which is appeared in the control theory are discussed. The drawback of these common statements is analyzed through some counter examples. Moreover a new necessary condition for strict positive realness is obtained from high frequency behavior of the Nyquist diagram of the transfer function. Finally a more simplified and completed conditions for strict positive realness of single-input single-output linear time-invariant systems are presented based on the complex functions analysis approach.

• East Asian mathematical journal
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• v.37 no.3
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• pp.333-354
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• 2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.

• Steel and Composite Structures
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• v.47 no.5
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• pp.601-613
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• 2023

Numerous methods have been proposed in predicting formability of sheet metals based on microstructural and macro-scale properties of sheets. However, there are limited number of papers on the optimization problem to increase formability of sheet metals. In the present study, we aim to use novel optimization algorithms in neural networks to maximize the formability of sheet metals based on tensile curve and texture of aluminum sheet metals. In this regard, experimental and numerical evaluations of effects of texture and tensile properties are conducted. The texture effects evaluation is performed using Taylor homogenization method. The data obtained from these evaluations are gathered and utilized to train and validate an artificial neural network (ANN) with different optimization methods. Several optimization method including grey wolf algorithm (GWA), chimp optimization algorithm (ChOA) and whale optimization algorithm (WOA) are engaged in the optimization problems. The results demonstrated that in aluminum alloys the most preferable texture is cube texture for the most formable sheets. On the other hand, slight differences in the tensile behavior of the aluminum sheets in other similar conditions impose no significant decreases in the forming limit diagram under stretch loading conditions.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.458-467
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• 2019

The purpose of this study is the suggestion of optimized parameters in OI (Optimal Interpolation) by experimental study. The observation of applying optimal interpolation is ADCP (Acoustic Doppler Current Profiler) data at the southwestern sea of Korea. FVCOM (Finite Volume Coastal Ocean Model) is used for the barotropic model. OI is to the estimation of the gain matrix by a minimum value between the background error covariance and the observation error covariance using the least square method. The scaling factor and correlation radius are very important parameters for OI. It is used to calculate the weight between observation data and model data in the model domain. The optimized parameters from the experiments were found by the Taylor diagram. Constantly each observation point requires optimizing each parameter for the best assimilation. Also, a high accuracy of numerical model means background error covariance is low and then it can decrease all of the parameters in OI. In conclusion, it is expected to have prepared the foundation for research for the selection of ocean observation points and the construction of ocean prediction systems in the future.

• Advances in Computational Design
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• v.7 no.3
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.

• Steel and Composite Structures
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• v.49 no.1
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• pp.91-107
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• 2023

An effective approach to promoting sustainability within the construction industry is the use of recycled aggregate concrete (RAC) as a substitute for natural aggregates. Ensuring the frost resilience of RAC technologies is crucial to facilitate their adoption in regions characterized by cold temperatures. The main aim of this study was to use the Random Forests (RF) approach to forecast the frost durability of RAC in cold locations, with a focus on the durability factor (DF) value. Herein, three optimization algorithms named Sine-cosine optimization algorithm (SCA), Black widow optimization algorithm (BWOA), and Equilibrium optimizer (EO) were considered for determing optimal values of RF hyperparameters. The findings show that all developed systems faithfully represented the DF, with an R² for the train and test data phases of better than 0.9539 and 0.9777, respectively. In two assessment and learning stages, EO - RF is found to be superior than BWOA - RF and SCA - RF. The outperformed model's performance (EO - RF) was superior to that of ANN (from literature) by raising the values of R² and reducing the RMSE values. Considering the justifications, as well as the comparisons from metrics and Taylor diagram's findings, it could be found out that, although other RF models were equally reliable in predicting the the frost durability of RAC based on the durability factor (DF) value in cold climates, the developed EO - RF strategy excelled them all.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.395-418
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• 2024

The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (R_e), maximum fault distance (R_f), or maximum hypocentral distance (R_h), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (R_e, R_f, or R_h) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.

• Journal of Environmental Impact Assessment
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• v.29 no.4
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• pp.272-285
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• 2020

We investigated statistical evaluation parameters for 3D meteorological and air quality models and selected several quantitative indicator references, and summarized the reference values of the statistical parameters for domestic air quality modeling researcher. The finally selected 9 statistical parameters are MB (Mean Bias), ME (Mean Error), MNB (Mean Normalized Bias Error), MNE (Mean Absolute Gross Error), RMSE (Root Mean Square Error), IOA (Index of Agreement), R (Correlation Coefficient), FE (Fractional Error), FB (Fractional Bias), and the associated reference values are summarized. The results showed that MB and ME have been widely used in evaluating the meteorological model output, and NMB and NME are most frequently used for air quality model results. In addition, discussed are the presentation diagrams such as Soccer Plot, Taylor diagram, and Q-Q (Quantile-Quantile) diagram. The current results from our study is expected to be effectively used as the statistical evaluation parameters suitable for situation in Korea considering various characteristics such as including the mountainous surface areas.

• Atmosphere
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• v.17 no.1
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• pp.55-68
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• 2007

Future climate changes over East Asia are projected by anthropogenic forcing of greenhouse gases and aerosols using ECHO-G/S (ECHAM4/HOPE-G). Climate simulation in the 21st century is conducted with three standard SRES scenarios (A1B, B1, and A2) and the model performance is assessed by the 20th Century (20C3M) experiment. From the present climate simulation (20C3M), the model reproduced reliable climate state in the most fields, however, cold bias in temperature and dry bias of summer in precipitation occurred. The intercomparison among models using Taylor diagram indicates that ECHO-G/S exhibits smaller mean bias and higher pattern correlation than other nine AOGCMs. Based on SRES scenarios, East Asia will experience warmer and wetter climate in the coming 21st century. Changes of geographical patterns from the present to the future are considerably similar through all the scenarios except for the magnitude difference. The temperature in winter and precipitation in summer show remarkable increase. In spite of the large uncertainty in simulating precipitation by regional scale, we found that the summer (winter) precipitation at eastern coast (north of $40^{\circ}N$) of East Asia has significantly increased. In the 21st century, the warming over the continents of East Asia showed much more increase than that over the ocean. Hence, more enhanced (weakened) land-sea thermal contrast over East Asia in summer (winter) will cause strong (weak) monsoon. In summer, the low pressure located in East Asia becomes deeper and the moisture from the south or southeast is transported more into the land. These result in increasing precipitation amount over East Asia, especially at the coastal region. In winter, the increase (decrease) of precipitation is accompanied by strengthening (weakening) of baroclinicity over the land (sea) of East Asia.

• Journal of Korean Society on Water Environment
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• v.26 no.3
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• pp.482-490
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• 2010

In this study 17 GCMs' simulations of late 20th century climate in Korea are examined. A regionally averaged time series formed by averaging the temperature and precipitation values at all the Korean grid points. In order to compare general circulation models with observations, observed spatially averaged temperature and precipitation is calculated using 24 stations for 1971 to 2000. The annual mean difference between models and observed data are compared. For temperature, most models have a slight cold bias. The models with least bias in annual average temperature are NIES(MIROC3.2 hires), GISS(AOM) and INGV(SXG2005). For precipitation, almost all models have a dry bias, and for some the bias exceeds 50%. Models with lowest bias are NIES(MIROC3.2 hires), CCCma(CGCM3-T47) and MPI-M(ECHAM5-OM). The models' simulated seasonal cycles show that for temperature, CSIRO(Mk3.0) has the best followed by CCCma(CGCM3-T47) and CCCma(CGCM3-T63), and for precipitation, NIES(MIROC3.2 hires) has the best followed by CSIRO(Mk3.0) and CNRM(CM3). In the assessment using Taylor diagram, CCCma(CGCM3-T47) ranks the best for temperature, and NIES(MIROC3.2 hires) ranks the best for precipitation.