• Korean Journal of Childcare and Education
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• v.18 no.3
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• pp.37-59
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• 2022

Objective: The purpose of this study is to develop and validate a coparenting scales(mother's version) suitable for the Korean situation. Methods: In this study, mothers with one or more children were targeted. First, factor structure and construct validity were verified(N=412), and second, cross-validation and concurrent validity were verified(N=312). Results: The coparenting scale(mother's version) is largely composed of the mother's own coparenting and their spouse's coparenting. First, as a result of performing an exploratory factor analysis, three factors were extracted from the mother's own coparenting and their spouse's coparenting, and they were labeled parenting cooperation, parenting agreement, and parenting sharing. Through confirmatory factor analysis, 13 items were identified with three factors. Second, cross-validation was performed on a new group with confirmatory factor analysis, and as a result, validity was secured by satisfying the model validation criteria. In addition, the correlation between the existing scale and parenting efficacy was significant, thus securing concurrent validity. Conclusion/Implications: Through this study, the coparenting scale(mother's version) that was developed may provide practical guidelines for family coparenting by identifying mothers' perceptions of coparenting, and can be used in parent education and child-rearing policies.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.395-409
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• 2022

The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.1-16
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• 2023

Accurate traffic information prediction is considered to be one of the most important aspects of intelligent transport systems(ITS), as it can be used to guide users of transportation facilities to avoid congested routes. Various deep learning models have been developed for accurate traffic prediction. Recently, ensemble techniques have been utilized to combine the strengths and weaknesses of various models in various ways to improve prediction accuracy and stability. Therefore, in this study, we developed and evaluated a traffic information prediction model using various deep learning models, and evaluated the performance of the developed deep learning models as a stacking ensemble. The individual models showed error rates within 10% for traffic volume prediction and 3% for speed prediction. The ensemble model showed higher accuracy compared to other models when no cross-validation was performed, and when cross-validation was performed, it showed a uniform error rate in long-term forecasting.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1001-1009
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• 2015

In Smart Grid, smart meter sends our electric signal data to the main server of power supply in real-time. However, the more efficient the management of power loads become, the more likely the user's pattern of usage leaks. This paper points out the threat of privacy and the need of security measures in smart device environment by showing that it's possible to identify the appliances and the specific usage patterns of users from the smart meter's data. Learning algorithm PCA is used to reduce the dimension of the feature space and k-NN Classifier to infer appliances and states of them. Accuracy is validated with 10-fold Cross Validation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.1
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• pp.32-40
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• 2018

In this research, a method of deriving statistical weight prediction formula which is used during the conceptual design phase was studied and it was programmed using Microsoft Excel and verified by applying to jet transport aircraft. The database was built while referencing the variables of conventional wing weight estimation formulas and it was used for modeling the jet transport wing weight regression equation. The model was evaluated using the K-fold cross validation method to solve the overfitting problem of the model.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.8 s.251
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• pp.923-930
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• 2006

A Kriging model is a sort of approximation model and used as a deterministic model of a computationally expensive analysis or simulation. Although it has various advantages, it is difficult to assess the accuracy of the approximated model. It is generally known that a mean square error (MSE) obtained from the kriging model can't calculate statistically exact error bounds contrary to a response surface method, and a cross validation is mainly used. But the cross validation also has many uncertainties. Moreover, the cross validation can't be used when a maximum error is required in the given region. For solving this problem, we first proposed a modified mean square error which can consider relative errors. Using the modified mean square error, we developed the strategy of adding a new sample to the place that the MSE has the maximum when the MSE is used for the assessment of the kriging model. Finally, we offer guidelines for the use of the MSE which is obtained from the kriging model. Four test problems show that the proposed strategy is a proper method which can assess the accuracy of the kriging model. Based on the results of four test problems, a convergence coefficient of 0.01 is recommended for an exact function approximation.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.352-361
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• 2003

We have evaluated the extent of saltwater intrusion from electrical resistivity distribution in a coastal aquifer system in the southeastern part of Busan, Korea. This aquifer system is divided into four layers according to the hydrogeologic characteristics and the horizontal extent of intruded saltwater is determined at each layer through the geostatistical interpretation of electrical resistivity data. In order to define the statistical structure of electrical resistivity data, variogram analysis is carried out to obtain best generalized covariance models. IRF-k (intrinsic random function of order k) kriging is performed with covariance models to produce the plane of spatial mean resistivities. The kriged estimates are evaluated by cross validation to show a good agreement with the true values and the statistics of cross validation represented low errors for the estimates. In the resistivity contour maps more than 5 m below the surface, we can see a dominant direction of saltwater intrusion beginning from the east side. The area of saltwater intrusion increases with depth. The northeast side has low resistivities less than 5 ohm-m due to the presence of saline water in the depth range of 20 m through 70 m. These results show that the application of geostatistical technique to electrical resistivity data is useful for assessing saltwater intrusion in a coastal aquifer system.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.125-133
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• 2013

In this paper, penalized binary logistic regression models are employed as statistical models for determining the discharge of 668 patients with a chief complaint of dyspnea based on 11 blood tests results. Specifically, the ridge model based on $L^2$ penalty and the Lasso model based on $L^1$ penalty are considered in this paper. In the comparison of prediction accuracy, our models are compared with the logistic regression models with all 11 explanatory variables and the selected variables by variable selection method. The results show that the prediction accuracy of the ridge logistic regression model is the best among 4 models based on 10-fold cross-validation.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.1-10
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• 2021

This study examines the dynamic pattern of the exchange rate volatilities of the ASEAN-5 currencies from January 2006 to August 2020. The exchange rates applied in this study comprise bilateral and effective exchange rates in order to investigate the influence of the US dollar on the stability of the ASEAN-5 currencies. Since a volatility model employed in this study is a natural cubic spline volatility model, the Monte Carlo simulation is consequently conducted to determine an appropriate criterion to select a number of quantile knots for this model. The simulation results reveal that, among four candidate criteria, Generalized Cross-Validation is a suitable criterion for modeling the ASEAN-5 exchange rate volatilities. The estimated volatilities showed the inconstant dynamic patterns reflecting the uncertain exchange rate risk arising in international transactions. The bilateral exchange rate volatilities of the ASEAN-5 currencies to the US dollar are more variable than their corresponding effective exchange rate volatilities, indicating the influence of the US dollar on the stability of the ASEAN-5 currencies. The findings of this study suggest that the natural cubic spline volatility model with the quantile knots selected by Generalized Cross-Validation is practical and can be used to examine the dynamic patterns of the financial volatility.

• Proceedings of the Korean Society of Applied Entomolohy Conference
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• 2003.10a
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• pp.71-71
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• 2003