• Water for future
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• v.23 no.3
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• pp.319-328
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• 1990

The Parameter Identification of 2-demensional estuarine model was carried out using new output ADI-FDM numerical semi-implicit schem transformed in boundary fitted(BF) - coordinate. The hydrodynamic equations which is coupled with the transport equations were used as basic equations in the model. Thompson's equations were used to transform governing equations into rectangular plane equations and his elliptic grid generation scheme was used to generate curvilinear grid system. in BF - coordinates. The parameters to be identified are friction coefficient and disperse coefficient embedded in the governing equations. The numerical output scheme is tidally averaged salinity model in BF - coordinates. The algorithm to optimize norm of error between observations and calculations is the influence coefficinet algorithm associated with least square criterion. The lumped model is conssidered in identification. This paper was concetrated on checking whether the new output scheme might be useful to identify parameters in estuarine salinity model or not. The proposed method was tested through experimental application with hypothetical simple model. The result of the test shows that the proposed method can be used for parameter identification in estuarine model.

• Journal of Industrial Convergence
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• v.19 no.6
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• pp.145-153
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• 2021

This study empirically verified whether there is a dual mediating effect of individual optimistic bias and information security intention in the relationship between information security attitude and information security behavior of social welfare college students. The subjects were 295 college students majoring in social welfare. Spss Process macro was used for analysis. As a result. first there was a significant positive correlation between the variables. Second in the relationship between information security attitude and information security behavior, individual optimistic bias and information security intent each had a simple mediating effect. Third when an individual's optimistic bias and information security intent were simultaneously input, each had a simple mediating effect. Fourth there was a double mediating effect between individual optimistic bias and information security intent. This study provided basic data for the expansion of information security model and information security education of social welfare students.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.127-133
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• 2021

This study studies the effect of social welfare service quality on facility reuse for users of social welfare facilities. The purpose of this study is to study the effect of the parallel triple mediation effect of facility image user loyalty and facility satisfaction. To this end 219 users of social welfare facilities were analyzed using SPSS 23.0 for Windows and Process macro. As a result, first the variables of service quality facility image user loyalty facility satisfaction and facility reuse all formed significant positive relationships. Second facility image user loyalty and facility satisfaction had multiple mediating effects. This study presented a new model using multivariate in response to social welfare facilities in pandemic situations.

• Industry Promotion Research
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• v.9 no.3
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• pp.119-127
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• 2024

This study aims to confirm the dual mediating effect of gratitude and mindfulness in the relationship between grit and flourishing in middle and high school students and to provide basic data that can improve flourishing. The study subjects were middle and high school students who were selected from a city in South Chungcheong Province, and data were collected through a survey. For data analysis, SPSS PC+ Win. ver. 25.0 and SPSS PROCESS macro ver. 4.2 were utilized. Data were analyzed using descriptive statistics, reliability analysis, correlation analysis, and dual mediation effect analysis techniques. First, correlation analysis showed that there was a significant and positive correlation between grit, gratitude, mindfulness, and flourishing and the highest correlation between gratitude and mindfulness. Second, gratitude and mindfulness had a serial dual mediating effect in the link between grit and flourishing in middle and high school students. These results can be used as a new model to improve flourishing by utilizing positive psychological variables such as grit, gratitude, and mindfulness for middle and high school students.

• Journal of Wetlands Research
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• v.22 no.1
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• pp.15-23
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• 2020

The purpose of this study is to investigate how the degree of distribution influences the calibration of snow and runoff in distributed hydrological models using a multi-criteria calibration method. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-National Weather Service (NWS) is employed to estimate optimized parameter sets. We have 3 scenarios depended on the model complexity for estimating best parameter sets: Lumped, Semi-Distributed, and Fully-Distributed. For the case study, the Durango River Basin, Colorado is selected as a study basin to consider both snow and water balance components. This study basin is in the mountainous western U.S. area and consists of 108 Hydrologic Rainfall Analysis Project (HRAP) grid cells. 5 and 13 parameters of snow and water balance models are calibrated with the Multi-Objective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm. Model calibration and validation are conducted on 4km HRAP grids with 5 years (2001-2005) meteorological data and observations. Through case study, we show that snow and streamflow simulations are improved with multiple criteria calibrations without considering model complexity. In particular, we confirm that semi- and fully distributed models are better performances than those of lumped model. In case of lumped model, the Root Mean Square Error (RMSE) values improve by 35% on snow average and 42% on runoff from a priori parameter set through multi-criteria calibrations. On the other hand, the RMSE values are improved by 40% and 43% for snow and runoff on semi- and fully-distributed models.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.

• Journal of Radiation Protection and Research
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• v.33 no.4
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• pp.135-141
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• 2008

This paper describes a dynamic compartment model to predict the time-dependent $^{14}C$ activity in a plant as a result of a direct exposure to an amount of $^{14}CO_2$ for a short period of time, and experimental results for the model validation. In the model, the plant consists of two compartments of the body and ears, and five carbon fluxes between the compartments, which are the function of parameters relating to the growth and photosynthesis of a plant, are considered. Model predictions were made for an investigation into the effects of the exposure time, the elapsed exposure time, and the model parameters on the $^{14}C$ radioactivity of a plant. The present model converged to a region where the specific activity model is applicable when the elapsed time of the exposure was extended up to the harvest time of a plant. The $^{14}C$ activity of a plant was predicted to be the greatest when the exposure had happened in the period between the flowering and ears-maturity on account of the most vigorous photosynthesis rate for the period. Comparison of model predictions with the observed 14C radioactivity of rice plants showed that the present model could predict the $^{14}C$ radioactivity of the rice plants reasonably well.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.5
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• pp.513-521
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• 2012

The aim of this study was to determine the brittle fracture behavior of reactor pressure vessel steel by considering the temperature dependence of a damage model. A multi-island genetic algorithm was linked to a Weibull stress model, which is the model typically used for brittle fracture evaluation, to improve the calibration procedure. The improved calibration procedure and fracture toughness test data for SA508 carbon steel at the temperatures $-60^{\circ}C$, $-80^{\circ}C$, and $-100^{\circ}C$ were used to decide the damage parameters required for the brittle fracture evaluation. The model was found to show temperature dependence, similar to the case of NUREG/CR-6930. Finally, on the basis of the quantification of the difference between 2- and 3-parameter Weibull stress models, an engineering equation that can help obtain more realistic fracture behavior by using the simpler 2-parameter Weibull stress model was proposed.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.3
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• pp.204-208
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• 1991

Five models for net radiation estimation reported by Linacre(1968), Berljand(1956), Nakayama et al. (1983), Chang (1970) and Doorenbos et al. (1977) were tested for the adaptability to Korea. A new model with effective longwave radiation term parameterized by air temperature, solar radiation and vapor pressure was formulated and tested for its accuracy. Above five models with original parameter values showed large absolute mean deviations ranging from 0.86 to 1.64 MJ/$m^2$/day. The parameters of the above five models were reestimated by using net radiation and meteorological elements measured in Suwon, Korea. These five models with new parameter values showed absolute mean deviations ranging from 0.74 to 0.88 MJ/$m^2$/day. The following model was newly formulated: Rn=(1- $\alpha$) Rs- $\sigma$ $T_{k}$$^{4}$ (0.0103 Exp (0 .0731 Rs) -0.0475 (equation omitted) +0 .2478) ($R^2$=0.997, n=63) where $\alpha$ =albedo, $\sigma$=Stefan-Boltzmann constant, Rs=solar radiation in MJ/$m^2$/day, Tk =air temperature in Kelvin and $e_{a}$=vapor pressure in mb. This model revealed 0.4988 MJ/$m^2$/day in absolute mean deviation when applied to an independent set of meteorological data.a.a.

• Journal of Korea Multimedia Society
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• v.3 no.6
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• pp.566-574
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• 2000

Although a numbed of variants of 2D HMM have been proposed in the literature, they are, in a word, too simple to model the variabilities of images for diverse classes of objects; they do not realize the modeling capability of the 1D HMM in 2D. Thus the author thinks they are poor substitutes for the HMM in 2D. The new model proposed in this paper is a hidden Markov lattice or, we can dare say, a 2D HMM with the causality of top-down and left-right direction. Then with the addition of a lattice constraint, the two algorithms for the evaluation of a model and the maximum likelihood estimation of model parameters are developed in the theoretical perspective. It is a more natural extension of the 1D HMM. The proposed method will provide a useful way of modeling highly variable patterns such as offline cursive characters.