• Water Engineering Research
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• v.3 no.2
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.475-484
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• 2018

Gamma generalized linear models are useful for non-negative and skewed responses. However, these models have received less attention than Poisson and binomial generalized linear models. In particular, hypothesis testing for the significance of regression coefficients has not been thoroughly studied. In this paper we assess the performance of various test statistics for gamma generalized linear models based on numerical studies. Our results show that the likelihood ratio test and F-type test are generally recommended and that the partial deviance test should be avoided in practice.

• Computers and Concrete
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• v.4 no.2
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• pp.135-156
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• 2007

Three 3D nonlinear finite-element models are developed to study the behavior of concrete beams and plates with and without external reinforcement by fibre-reinforced plastic (FRP). All three models are formulated based upon the 3D theory of elasticity. The stress model is modified from the element developed by Ramtekkar, et al. (2002) to incorporate material nonlinearity in the formulation. Both transverse stress and displacement components are used as nodal degrees-of-freedom to ensure the continuity of both stress and displacement components between the elements. The displacement model uses only displacement components as nodal degrees-of-freedom. The transition model has both stress and displacement components as nodal degrees-of-freedom on one surface, and only displacement components as nodal degrees-of-freedom on the opposite surface. The transition model serves as a connector between the stress and the displacement models. The developed models are validated by comparing the results of the analyses with an existing experimental result. Parametric studies of the effects of the externally reinforced FRP on the load capacity of reinforced concrete (RC) beams and concrete plates are performed to demonstrate the practicality and the efficiency of the proposed models.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.615-631
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• 2008

Current credit evaluation models based on only financial data except non-financial data are used continuous data and produce credit scores for the ranking. In this work, some problems of the credit evaluation models based on transformed continuous financial data are discussed and we propose improved credit evaluation models based on categorized financial data. After analyzing and comparing goodness-of-fit tests of two models, the availability of the credit evaluation models for categorized financial data is explained.

• Advances in Computational Design
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• v.4 no.4
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• pp.381-395
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• 2019

This paper proposes a new approach to model the screw joints of integrated ceilings via the finite element method (FEM). The simulation models consist of the beam elements. The screw joints used in the main bars and cross bars and in the W bars and cross bars are assumed to be rotation springs. The stiffness of the rotation springs is defined according to the technical standards proposed by the National Institute for Land and Infrastructure Management of Japan. By comparing the results of the sheer tests and the simulation models, the effectiveness and efficiency of the simulation models proposed in this paper are verified. This paper indicates the possibility that the seismic performance of suspended ceilings can be confirmed directly via beam element models using FEM if the stiffnesses of the screw joints of the ceiling substrates are appropriately defined. Because cross-sectional shapes, physical properties, and other variables of the ceiling substrates can be easily changed in the models, it is expected that suspended ceiling manufactures will be able to design and confirm the seismic performance of suspended ceilings with different cross-sectional shapes or materials via computers, instead of spending large amounts of time and money on shake table tests.

• Journal of Forest and Environmental Science
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• v.39 no.3
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• pp.119-127
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• 2023

In this study, quantitative models were applied to case parks to estimate the carbon reduction by trees, which was compared and analyzed at the tree and park levels. At the tree level, quantitative models of carbon storage and uptake differed by up to 7.9 times, even for the same species and size. At the park level, the carbon reduction from quantitative models varied by up to 3.7 times for the same park. In other words, carbon reduction by quantitative models exhibited considerable variation at the tree and park levels. These differences are likely due to the use of different growth environment coefficients and annual diameter at breast height growth rates and the overestimation of carbon reduction due to the substitution of the same genus and group model for each tree species. Extending the annual carbon uptake per unit area of the case park to the total park area of Chuncheon a carbon uptake ranging from a minimum of 370.4 t/yr and a maximum of 929.3 t/yr, and the difference can reach up to 558.9 t/yr. This is equivalent to the carbon emissions from the annual household electricity consumption of approximately 2,430 people. These results suggest that the indiscriminate application of quantitative models to estimate carbon reduction in urban trees can lead to significant errors and deviations in estimating carbon storage and uptake in urban greenspaces. The findings of this study can serve as a basis for estimating carbon reduction in urban greening research, projects, and policies.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.79-89
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• 1997

In order to reduce the outbreaks of short-term high concentrations and its impacts, we developed the models which predicted tomorrow's maximum hourly concentrations of $O_3$, TSP, SO$_2$, NO$_2$ and CO. Statistical methods like multi regressions were used because it must be operated easily under the present conditions. 47 independent variables were used, which included observed concentrations of air pollutants, observed and forcasted meteorological data in 1994 at Seoul and its surrounding areas. We subdivided Seoul into 4 areas coinciding with the present ozone warning areas. 4 kinds of seasonal models were developed due to the seasonal variations of observed concentrations, and 2 kinds of data models for the unavailable case of forecasted meteorological data. By comparing the $R^2$and root mean square error(hearafter 'RMSE') of each model, we confirmed that the models including forecasted data showed higher accuracy than ones using observed only. It was also shown that the higher the seasonal mean concentrations, the larger the RMSE. There was no distinct difference between the results of 4 areal models. In case of test run using 1995's data, the models predicted well the trends of daily variation of concentrations and the days when the possibility of outbreak of high concentarion was high. This study showed that it was reasonable to use those models as operational ones, because the $R^2$ and RMSE of models were smaller than those of operational/research models such as in South Coast Air Basin, CA, USA.

• Journal of ILASS-Korea
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• v.5 no.2
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• pp.35-42
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• 2000

A number of atomization and droplet breakup models have been developed and used to predict the diesel spray characteristics. Of the many atomization and droplet breakup models based on the breakup mechanism due to aerodynamic liquid and gas interaction, four models classified as mathematical models, such as TAB, modified TAB, DDB, WB and one of the hybrid model based on WB and TAB models were selected for the assessment of prediction ability of diesel spray dynamics. The assessment of these models by using KIVA-II code was performed by comparing with the experimental data of spray tip penetration and sauter mean diameter(SMD) from the literature. It is found that the prediction of spray tip penetration and SMD by the hybrid model was only influenced by the initial parcel number. All the atomization and droplet breakup models considered here was strongly dependent on the grid resolution. Therefore it is important to check the grid resolution to get an acceptable results in selecting the models. At low injection pressure, modified TAB model could only give the good agreement with experimental data of spray tip penetration and both of modified TAB and DDB models were recommendable for the prediction of SMD. At high injection pressure, hybrid model could only give the good agreement with the experimental data of spray tip penetration and the prediction of all of the selected models did not match the experimental data. Spray tip penetration was increased with the increase the $B_1$ and the increase of $B_1$ did not affected the prediction of SMD.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.5
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• pp.18-25
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• 2008

A performance analysis program has been developed for the gas generator cycle liquid rocket engine. This program predicts the system performance with the performances of subsystems which are evaluated by the models based on another analyses or experiments. The analysis method has been validated by comparing the engine performance against the published conceptual design. The performance models of the subsystems have been verified to give reasonable results by comparing with the MC-1 engine design and the system analysis of 10 ton thrust engine. The system performance of the 30 ton thrust rocket engine using LOx/Jet-A1 has been presented as an application example.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.233-233
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• 2022

As the world's population grows and food needs diversify, the demand for horticultural crops for beneficial traits is increasing. In order to meet this demand, it is necessary to develop suitable cultivars and breeding methods accordingly. Breeding methods have changed over time. With the recent development of sequencing technology, the concept of genomic selection (GS) has emerged as large-scale genome information can be used. GS shows good predictive ability even for quantitative traits by using various markers, breaking away from the limitations of Marker Assisted Selection (MAS). Moreover, GS using machine learning (ML) and deep learning (DL) has been studied recently. In this study, we aim to build a system that selects phenotype-related markers using the genomic information of the pepper population and trains a genomic selection model to select individuals from the validation population. We plan to establish an optimal genome wide association analysis model by comparing and analyzing five models. Validation of molecular markers by applying linkage markers discovered through genome wide association analysis to breeding populations. Finally, we plan to establish an optimal genome selection model by comparing and analyzing 12 genome selection models. Then We will use the genome selection model of the learning group in the breeding group to verify the prediction accuracy and discover a prediction model.