• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.5
• /
• pp.541-547
• /
• 2010

The rigorous validation of the accuracy of metamodels is an important topic in research on metamodel techniques. Although a leave-k-out cross-validation technique involves a considerably high computational cost, it cannot be used to measure the fidelity of metamodels. Recently, the mean$_0$ validation technique has been proposed to quantitatively determine the accuracy of metamodels. However, the use of mean$_0$ validation criterion may lead to premature termination of a sampling process even if the kriging model is inaccurate. In this study, we propose a new validation technique based on the mean and variance of the response evaluated when sequential sampling method, such as maximum entropy sampling, is used. The proposed validation technique is more efficient and accurate than the leave-k-out cross-validation technique, because instead of performing numerical integration, the kriging model is explicitly integrated to accurately evaluate the mean and variance of the response evaluated. The error in the proposed validation technique resembles a root mean squared error, thus it can be used to determine a stop criterion for sequential sampling of metamodels.

• Journal of the Korean Data and Information Science Society
• /
• v.6 no.1
• /
• pp.63-71
• /
• 1995

We have consider the study of local influence for smoothing parameter estimates in spline regression model with heteroscedasticity. Practically, generalized cross-validation does not work well in the presence of heteroscedasticity. Thus we have proposed the local influence measure for generalized cross-validation estimates when errors are heteroscedastic. And we have examined effects of diagnostic by above measures through Hyperinflation data.

• Journal of the Korean Society for Precision Engineering
• /
• v.17 no.2
• /
• pp.114-120
• /
• 2000

Fossil power plant availability is significantly affected by gradual degradations of equipment as operation of the plant continues. It is quite important to determine whether or not to replace some equipment and when to replace the equipment. Performance calculation and analysis can provide the information. Robustness in the performance calculation can be increased by using verification ＆ validation of measured input data. We suggest new algorithm in which estimation relation for validated measurement can be obtained using correlation between measurements. Input estimation model is obtained using design data and acceptance measurement data of domestic 16 fossil power plant. The model consists of finding mostly correlated state variable in plant state and mapping relation based on the model and current state of power plant.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.3
• /
• pp.613-618
• /
• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• Journal of Korea Multimedia Society
• /
• v.24 no.10
• /
• pp.1403-1413
• /
• 2021

In this paper, we investigate automated data validation techniques for artificial intelligence training, and also disclose open-source frameworks, such as Google's TensorFlow Data Validation (TFDV), that support automated data validation in the AI model development process. We also introduce an experimental study using public data sets to demonstrate the effectiveness of the open-source data validation framework. In particular, we presents experimental results of the data validation functions for schema testing and discuss the limitations of the current open-source frameworks for semantic data. Last, we introduce the latest studies for the semantic data validation using machine learning techniques.

• Journal of Ecology and Environment
• /
• v.38 no.4
• /
• pp.485-491
• /
• 2015

This study was conducted to develop stem volume models for the volume estimation of Quercus glauca Thunb. in Jeju Island, Republic of Korea. Furthermore, this study validated the developed stem volume models using an independent dataset. A total of 167 trees were measured for their diameter at breast height (DBH), total height and stem volume using non-destructive sampling methods. Eighty percent of the dataset was used for the initial model development while the remaining 20% was used for model validation. The performance of the different models was evaluated using the following fit statistics: standard error of estimate (SEE), mean bias absolute mean deviation (AMD), coefficient of determination (R²), and root mean square error (RMSE). The AMD of the five models from the different DBH classes were determined using the validation dataset. Model 5 (V = aD^bH^c), which estimates volume using DBH and total height as predicting variables, had the best SEE (0.02745), AMD (0.01538), R² (0.97603) and RMSE (0.02746). Overall, volume models with two independent variables (DBH and total height) performed better than those with only one (DBH) based on the model evaluation and validation. The models developed in this study can provide forest managers with accurate estimations for the stem volumes of Quercus glauca in the subtropical forests of Jeju Island, Korea.

• Journal of Korean Society on Water Environment
• /
• v.31 no.5
• /
• pp.507-522
• /
• 2015

In this study, some of the model verification results of STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model), a newly-developed hybrid watershed model, are presented for the runoff processes of nonpoint source pollution. For verification study of STREAM, the model was applied to a test watershed and a sensitivity analysis was also carried out for selected parameters. STREAM was applied to the Mankyung River Watershed to review the applicability of the model in the course of model calibration and validation against the stream flow discharge, suspended sediment discharge and some water quality items (TOC, TN, TP) measured at the watershed outlet. The model setup, simulation and data I/O modules worked as designed and both of the calibration and validation results showed good agreement between the simulated and the measured data sets: NSE over 0.7 and $R^2$ greater than 0.8. The simulation results also include the spatial distribution of runoff processes and watershed mass balance at the watershed scale. Additionally, the irrigation process of the model was examined in detail at reservoirs and paddy fields.

• Computers and Concrete
• /
• v.23 no.1
• /
• pp.11-23
• /
• 2019

Nowadays, the characterization of Ultra-High Performance Fiber-Reinforced Concrete (UHPFRC) tensile behavior still remains a challenge for researchers. For this purpose, a simplified closed-form non-linear hinge model based on the Third Point Bending Test (ThirdPBT) was developed by the authors. This model has been used as the basis of a simplified inverse analysis methodology to derive the tensile material properties from load-deflection response obtained from ThirdPBT experimental tests. In this paper, a non-linear finite element model (FEM) is presented with the objective of validate the closed-form non-linear hinge model. The state determination of the closed-form model is straightforward, which facilitates further inverse analysis methodologies to derive the tensile properties of UHPFRC. The accuracy of the closed-form non-linear hinge model is validated by a robust non-linear FEM analysis and a set of 15 Third-Point Bending tests with variable depths and a constant slenderness ratio of 4.5. The numerical validation shows excellent results in terms of load-deflection response, bending curvatures and average longitudinal strains when resorting to the discrete crack approach.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.24 no.12
• /
• pp.1625-1634
• /
• 2000

Verification of measurements is required for precise evaluation of turbine cycle performance in nuclear power plant. We assumed that initial acceptance data and design data of the plant could provide correlation information between performance data. The data can be used as sample sets for the correct estimation model of measurement value. The modeling was done practically by using regression model based on plant design data, plant acceptance data and verified plant performance data of domestic nuclear power plant. We can construct more robust performance analysis system for an operation nuclear power plant with this validation scheme.

• International Journal of Fluid Machinery and Systems
• /
• v.2 no.4
• /
• pp.400-408
• /
• 2009

Optimization of seal geometries can reduce significantly the energetic losses in a hydraulic seal [1], especially for high head runner turbine. In the optimization process, a reliable prediction of the losses is needed and CFD is often used. This paper presents numerical experiments to determine an adequate CFD model for straight, labyrinth and stepped hydraulic seals used in Francis runners. The computation is performed with a finite volume commercial CFD code with a RANS low Reynolds turbulence model. As numerical computations in small radial clearances of hydraulic seals are not often encountered in the literature, the numerical results are validated with experimental data on straight seals and labyrinth seals. As the validation is satisfactory enough, geometrical optimization of hydraulic seals using CFD will be studied in future works.