• Advances in nano research
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• v.13 no.5
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• pp.499-512
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• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.53-63
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• 2023

This study developed a cognitive impairment predictive model as one of the screening tests for preventing dementia in the elderly by using Automated Machine Learning(AutoML). We used 'Wearable lifelog data for high-risk dementia patients' of National Information Society Agency, then conducted using PyCaret 3.0.0 in the Google Colaboratory environment. This study analysis steps are as follows; first, selecting five models demonstrating excellent classification performance for the model development and lifelog data analysis. Next, using ensemble learning to integrate these models and assess their performance. It was found that Voting Classifier, Gradient Boosting Classifier, Extreme Gradient Boosting, Light Gradient Boosting Machine, Extra Trees Classifier, and Random Forest Classifier model showed high predictive performance in that order. This study findings, furthermore, emphasized on the the crucial importance of 'Average respiration per minute during sleep' and 'Average heart rate per minute during sleep' as the most critical feature variables for accurate predictions. Finally, these study results suggest that consideration of the possibility of using machine learning and lifelog as a means to more effectively manage and prevent cognitive impairment in the elderly.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.735-749
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• 2008

This article suggests application of logit model for analysis of multicategory responses. Referring to the reference category, characteristic of each category is obtained from analysis of polytomous logit model. With National Pension data it is illustrated that application of logit model helps it possible to find significant factors which may not be found only with polytomous logit model. Application of the logit model is done by reducing the number of categories. Categories are grouped into the former and the latter group according to reference category. Extra finding of significant factor was possible from logistic regression analysis for the two groups after removing the reference category. It is expected that this application would be helpful for finding information and characteristics on ordered multicategory responses where the proportional odds model does not fit.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.115-123
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• 2020

This study empirically examined the effect of married women's self-awareness on the acceptance of extra-marital relationships and the mediating effects of individuals' criticism of television's drama about infidelity. The data was obtained by conducting a survey of married women (304 persons) and was analyzed based on the Structural Equal Model (SEM). The notable findings are as follows. First, the respondents are generally less receptive to extramarital relationships. Of the total respondents, 46.7% do not accept extramarital relations. 43.8% of the respondents showed an unclear position on extra-marital relationships, and about 10% of respondents showed an accepting position on extramarital affairs. This suggests that extramarital relationships can emerge as a real issue that is important for married women. Second, self-awareness does not directly affect married women's acceptance of extramarital affairs, however, 'self-awareness' proved to have a significant effect on 'acceptance to extramarital relationships' by using 'criticism' as a full mediation effect for affair-themed TV dramas. The effects of critical thinking and the judgment of married women on the content of TV dramas containing adultery has been confirmed to be a very important aspect of married women's understanding of extramarital relationships. Some practical and political implications are discussed based on this study's findings.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.585-594
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• 2003

It has been generally recognized that conventional binomial or Poisson model provides poor fits to the actual correlated binary data due to the extra-binomial variation. A number of generalized statistical models have been proposed to account for this additional variation. Among them, beta-binomial, correlated-binomial, and modified-binomial models are binomial-related models which are frequently used in modeling the sum of n correlated binary data. In many situations, it is reasonable to assume that n correlated binary data are exchangeable, which is a special case of correlated binary data. The sum of n exchangeable correlated binary data is modeled relatively well when the above three binomial-related models are applied. But the estimation results of correlation coefficient turn to be quite different. Hence, it is important to identify which model provides better estimates of model parameters(success probability, correlation coefficient). For this purpose, a small-scale simulation study is performed to compare the behavior of above three models.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.3
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• pp.139-146
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• 1996

An extended $k-\varepsilon$ tuebulence model modified by considering the streamline curvature effect and standard $k-\varepsilon$ turbulence model have been applied for three dimensional analysis of turbulece flow in a $90^{\circ}$ curved duct. By comparision of the results with the experimental data, the modified extended $k-\varepsilon$ model gave closer agreement with experimental data than the results from standard $k-\varepsilon$ model owing to an extra time scale of the production rate and parameter describing effects of streamline curvature included in the dissipation rate equation.

• Applied Chemistry for Engineering
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• v.24 no.1
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• pp.10-17
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• 2013

Heavy oil residue upgrading process was being used in conventional refinery process. Recently, as the importance of non conventional energy development is growing up, the commercial projects of heavy oil upgrading are getting more active than before. For having competitive business model in the resource competition, non conventional energy development should be considered as an important business strategy. In developing oil sands, extra heavy oil, and shale gas, canadian oil sands and extra heavy oil have great importance in substitution of conventional oil consumption. In oil sands development, the bitumen, which is extracted from oil sands, has great value after upgrading or refining process. Similar process is being used current conventional refinery process. The bitumen is highly viscous hydrocarbon. This bitumen includes impurities which can not be treated in conventional refinery process. As this reason, specified process is needed in bitumen or extra heavy oil upgrading process. Moreover, there will be additional specified facilities in the process of production, transportation and marketing. In oil sands, there are various kinds of commercial upgrading process. Extraction, dilution, coking and cracking method were being used commercially.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.8
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• pp.2593-2600
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• 1996

A modified k-$\varepsilon$ turbulence model having a generality is proposed in the present study, in which the constant $C_{\varepsilon2}$in the $\varepsilon$-equation is simply changed as a functional form of a new parameter both satisfying the tensor invariant condition and representing the extra straining effect on complex shear flows. With this model turbulent shear flows over two-dimensional obstacles placed in a channel are numerically studied for different blockage ratios and aspect ratios. Comparing with the available experimental data, the predicted results with the present model provide definite improvements over the standard model's results and work fairly well with the experimental data on the size of the recirculation zone, as well as mean velocity, wall static pressure, turbulent kinetic energy and Reynolds stresses.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.8
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• pp.1046-1055
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• 1997

An anisotropic k-.epsilon. turbulence model for predicting the rotating flows is proposed with the simple inclusion of a new parameter dealing with the extra straining effects in the .epsilon.-equation. This model is employed to compute the effects of Coriolis forces on fully-developed flow in a rotating channel. The predicted results indicate that the present model captures fairly well the striking rotational-induced effects on the Reynolds stresses and the mean flow distributions, including the argumentation of turbulent transport on the unstable side (pressure surface) of the channel and its damping on the stable side (suction surface).

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.3
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• pp.72-81
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• 2022

Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.