• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.4
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• pp.277-288
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• 2020

In this study, we performed algorithms to predict algae of Chlorophyll-a (Chl-a). Water quality and quantity data of the middle Nakdong River area were used. At first, the correlation analysis between Chl-a and water quality and quantity data was studied. We extracted ten factors of high importance for water quality and quantity data about the two weirs. Algorithms predicted how ten factors affected Chl-a occurrence. We performed algorithms about decision tree, random forest, elastic net, gradient boosting with Python. The root mean square error (RMSE) value was used to evaluate excellent algorithms. The gradient boosting showed 10.55 of RMSE value for the Gangjeonggoryeong (GG) site and 11.43 of RMSE value for the Dalsung (DS) site. The gradient boosting algorithm showed excellent results for GG and DS sites. Prediction value for the four algorithms was also evaluated through the Receiver operating characteristic (ROC) curve and Area under curve (AUC). As a result of the evaluation, the AUC value was 0.877 at GG site and the AUC value was 0.951 at DS site. So the algorithm's ability to interpret seemed to be excellent.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Wind and Structures
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• v.31 no.4
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• pp.287-298
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• 2020

This paper presents an experimental investigation on the surface pressures on the CAARC standard tall building model concerning the effects of freestream turbulence. Two groups of incidence turbulence are generated in the wind tunnel experiment. The first group has an approximately constant turbulence intensity of 10.3% but different turbulence integral scale varying from 0.141 m to 0.599 m or from 0.93 to 5.88 in terms of scale ratio (turbulence integral scale to building dimension). The second group presents similar turbulence integral scale but different turbulence intensity ranging from 7.2% to 13.5%. The experimental results show that the mean pressure coefficients on about half of the axial length of the side faces near the leading edge slightly decrease as the turbulence integral scale ratio that is larger than 4.25 increases, but respond markedly to the changes in turbulence intensity. The root-mean-square (RMS) and peak pressure coefficients depend on both turbulence integral scale and intensity. The RMS pressure coefficients increase with turbulence integral scale and intensity. As the turbulence integral scale increases from 0.141 m to 0.599 m, the mean peak pressure coefficient increases by 7%, 20% and 32% at most on the windward, side faces and leeward of the building model, respectively. As the turbulence intensity increases from 7.2% to 13.5%, the mean value of peak pressure coefficient increases by 47%, 69% and 23% at most on windward, side faces and leeward, respectively. The values of cross-correlations of fluctuating pressures increase as the turbulence integral scale increases, but decrease as turbulence intensity increases in most cases.

• Annals of Clinical Neurophysiology
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• v.8 no.2
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• pp.163-170
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• 2006

Backgrounds: MEG can measure the task-specific neurophysiologic activity with good spatial and time resolution. Language lateralization using noninvasive method has been a subject of interest in resective brain surgery. We purposed to develop a paradigm for language lateralization using MEG and validate its feasibility. Methods: Magnetic fields were obtained in 12 neurosurgical candidates and one volunteer for language tasks, with a 306 channel whole head MEG. Language tasks were word listening, reading and picture naming. We tested two word listening paradigms: semantic decision of meaning of abstract nouns, and recognition of repeated words. The subjects were instructed to silently name or read, and respond with pushing button or not. We decided language dominance according to the number of acceptable equivalent current dipoles (ECD) modeled by sequential single dipole, and the mean magnetic field strength by root mean square value, in each hemisphere. We collected clinical data including Wada test. Results: Magnetic fields evoked by word listening were generally distributed in bilateral temporoparietal areas with variable hemispheric dominance. Language tasks using visual stimuli frequently evoked magnetic field in posterior midline area, which made laterality decision difficult. Response during task resulted in more artifacts and different results depending on responding hand. Laterality decision with mean magnetic field strength was more concordant with Wada than the method with ECD number of each hemisphere. Conclusions: Word listening task without hand response is the most feasible paradigm for language lateralization using MEG. Mean magnetic field strength in each hemisphere is a proper index for hemispheric dominance.

• Journal of Biomedical Engineering Research
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• v.40 no.6
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• pp.242-249
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• 2019

Forearm electromyography (EMG) generated by wrist movements has been widely used to develop an electrical prosthetic hand, but EMG generated by finger movements has been rarely used even though 20% of amputees lose fingers. The goal of this study is to improve the classification performance of different finger movements using a deep learning algorithm, and thereby contributing to the development of a high-performance finger-based prosthetic hand. Ten participants took part in this study, and they performed seven different finger movements forty times each (thumb, index, middle, ring, little, fist and rest) during which EMG was measured from the back of the right hand using four bipolar electrodes. We extracted mean absolute value (MAV), root mean square (RMS), and mean (MEAN) from the measured EMGs for each trial as features, and a 5x5-fold cross-validation was performed to estimate the classification performance of seven different finger movements. A long short-term memory (LSTM) model was used as a classifier, and linear discriminant analysis (LDA) that is a widely used classifier in previous studies was also used for comparison. The best performance of the LSTM model (sensitivity: 91.46 ± 6.72%; specificity: 91.27 ± 4.18%; accuracy: 91.26 ± 4.09%) significantly outperformed that of LDA (sensitivity: 84.55 ± 9.61%; specificity: 84.02 ± 6.00%; accuracy: 84.00 ± 5.87%). Our result demonstrates the feasibility of a deep learning algorithm (LSTM) to improve the performance of classifying different finger movements using EMG.

• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.447-461
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• 2018

Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

• Journal of Korean Dental Science
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• v.16 no.2
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• pp.149-155
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• 2023

Purpose: The purpose of this study was to evaluate whether the presence of a space maintainer affects the accuracy of an intraoral scanner. Materials and Methods: The maxillary primary first molar typodont tooth was removed from the primary dentition typodont model and a band and loop type space maintainer was delivered. After the model was connected to a dental phantom, intraoral scan was performed using TRIOS 4 (3Shape A/S, Copenhagen, Denmark). The scan was repeated with the same technique without the space maintainer. Each scan was performed 10 times. All scan files into a GOM inspect 2018 software and evaluated the accuracy. The accuracy was evaluated on trueness and precision, and calculated using the root mean square value. Result: When there was a space maintainer in the oral cavity, the trueness value was 0.10±0.02 mm and the precision value was 0.15±0.03 mm. In the absence of the space maintainer, the trueness value was 0.12±0.03 mm and the precision value was 0.16±0.04 mm. There were no significant differences depending on the presence of a space maintainer (P>0.05). Conclusion: Within the limits of this study, the accuracy of the intraoral scanner was not influenced by the presence of space maintainer.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.16-33
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• 2012

The aim of this study is to estimate aboveground biomass carbon stocks using ordinary kriging(OK) which is the most commonly used type of kriging and regression kriging(RK) that combines a regression of the auxiliary variables with simple kriging. The analysis results shows that the forest carbon stock in Danyang is estimated at 3,459,902 tonC with OK and 3,384,581 tonC with RK in which the R-square value of the regression model is 0.1033. The result of RK conducted with sample plots stratified by forest type(deciduous, conifer and mixed) shows the lowest estimated value of 3,336,206 tonC and R-square value(0.35 and 0.18 respectively) is higher than that of when all sample plots used. The result of leave-one-out cross validation of each method indicates that RK with all sample plots reached the smallest root mean square error(RMSE) value(22.32 ton/ha) but the difference between the methods(0.23 ton/ha) is not significant.