• Advances in concrete construction
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• v.2 no.3
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• pp.229-247
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• 2014

A numerical study of the influence of shear-span/depth ratio on the cohesive crack fracture parameters and double - K fracture parameters of concrete is carried out in this paper. For the study the standard bending specimen geometry loaded with four point bending test is used. For four point loading, the shear - span/depth ratio is varied as 0.4, 1 and 1.75 and the ao/D ratio is varied from 0.2, 0.3 and 0.4 for laboratory specimens having size range from 100 - 500 mm. The input parameters for determining the double - K fracture parameters are taken from the developed fictitious crack model. It is found that the cohesive crack fracture parameters are independent of shear-span/depth ratio. Further, the unstable fracture toughness of double-K fracture model is independent of shear-span/depth ratio whereas, the initial cracking toughness of the material is dependent on the shear-span/depth ratio.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.193-205
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• 1997

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we consider the multiprocess discount generalized model with parameters having a dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt change of pattern in parameters.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.447-458
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• 2002

The influence of observations in the equicorrelation model is investigated using the local influence approach when all parameters or subsets of parameters are of interest. When a parameter of interest is scalar, an analytical form of the local influence measure can be found. We will derive a measure for identifying observations that have a large influence on the test of fitting the equicorrelation model. An example is given for illustration.

• Kyungpook Mathematical Journal
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• v.47 no.4
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• pp.601-608
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• 2007

In this paper we introduce a discrete model of brucellosis happened in Korean livestock farms and numerically analyze its dynamical features. To do it, we consider parameters data supported by Livestock Cooperatives. To control brucellosis, we investigate the relationship among key parameters, as applications of our model. We hope that our model may be used to reduce brucellosis in Korean livestock farms.

• Ocean Science Journal
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• v.43 no.4
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• pp.165-173
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• 2008

In this article, we describe a simple yet effective method for insertion of observational datasets in a mesoscale atmospheric model used in one-dimensional configuration through Nudging. To demonstrate the effectiveness of this technique, vertical profiles of meteorological parameters obtained from GLASS Sonde launches from a tiny island of Kaashidhoo in the Republic of Maldives are injected in a mesoscale atmospheric model - Advanced Regional Prediction System (ARPS), and model simulated parameters are compared with the available observational datasets. Analysis of one-time nudging in the model simulations over Kaashidhoo show that incorporation of this technique reasonably improves the model simulations within a time domain of +6 to +12 Hrs, while its impact on +18 Hrs simulations and beyond becomes literally null.

• Geotechnical Engineering
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• v.8 no.4
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• pp.41-50
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• 1992

In general, the conceptual lumped-parameter groundwater flow model to predict the groundwater fluctuations in hillside slopes has unknown model parameters to be estimated from the known input -output data. The purpose of this study is to estimate the optimal model parameters of the groundwater flow model developed by authors. The Mazilnum A Posteriori( MAP) estimation method is utilized for this purpose and it is applied to a site which shows the typical landslide in Korea. The result of application shows tllat the 반AP estimation method can estimate the unknown parameters properly well. The groundwater model developed along with estimation technique applied in this paper will be used for assessing risk of landslides.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.607-612
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• 1994

The static and dynamic model of IGBT for the SPICE simulation has been successfully developed. The various circuit model parameters are extracted from the I-V and C-V characteristics of IGBT and implemented into our model. The static model of IGBT consists of the MOSFET, bipolar transistor and series resistance. The parameters to be extracted are the threshold voltage of MOSFET, current gain $\beta$ of bipolar transistor, and the series resistance. They can be extracted from the measured I-V characteristics curve. The C-V characteristics between the terminals are very important parameters to determine the turn-on and turn-off waveform. Especially, voltage dependent capacitance are polynomially approximated to obtain the exact turn-on and turn-off waveforms. The SPICE simulation results employing new model agree well with the experimental values.

• Journal of Korean Society for Quality Management
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• v.39 no.4
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• pp.507-515
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• 2011

MIL-HDBK-217 has played a pivotal role in reliability prediction of electronic equipments for more than 30 years. Recently, RIAC developed a new methodology $217Plus^{TM}$which officially replaces MIL-HDBK-217. Sensitivity analysis of the 217Plus component models to various parameters has been performed and meaningful observations have been drawn in this study. We first briefly reviewed the $217Plus^{TM}$ methodolog and compared it with the conventional model, MIL-HDBK-217. We then performed sensitivity analysis $217Plus^{TM}$ component models to various parameters. Based on the six parameters and an orthogonal array selected, we have performed indepth analyses concerning parameter effects on the model. Our result indicates that, among various parameters, operating temperature and temperature rise during operation have the most significant impacts on the life of a component, and thus a design robust to high temperature is the most importantly required. Next, year of manufacture, duty cycle, and voltage stress are weaker but may be significant when they are in heavy load conditions. Although our study is restricted to a specific type of diodes, the results are still valid to other cases. The results in this study not only figure out the behavior of the predicted failure rate as a function of parameters but provide meaningful guidelines for practical applications.

• Computers and Concrete
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• v.13 no.4
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• pp.547-567
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• 2014

Precast Seismic Structural Systems (PRESSS) provided an iterative procedure for obtaining optimum design of unbonded post-tensioned coupled precast concrete wall systems. Although PRESSS procedure is effective, however, it is lengthy and laborious. The purpose of this research is to employ Artificial Neural Network (ANN) to predict the optimum design parameters for such wall systems while avoiding the demanding iterative process. The developed ANN model is very accurate in predicting the nondimensional optimum design parameters related to post-tensioning reinforcement area, yield force of shear connectors and ratio of moment resisted by shear connectors to the design moment. The Mean Absolute Percent Error (MAPE) for the test data for these design parameters is around %1 and the correlation coefficient is almost equal to 1.0. The developed ANN model is then used to study the effect of different design parameters on wall behavior. It is observed that the design moment and the concrete strength have the most influence on the wall behavior as compared to other parameters. Several design examples were presented to demonstrate the accuracy and effectiveness of the ANN model.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.1
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• pp.1-9
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• 2009

The objective of this paper was to evaluate the auto-calibration with multi-objective optimization method to calibrate the parameters of the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated by using nine years (1996-2004) of measured data for the 384-ha Baran reservoir subwatershed located in central Korea. Multi-objective optimization was performed for sixteen parameters related to runoff. The parameters were modified by the replacement or addition of an absolute change. The root mean square error (RMSE), relative mean absolute error (RMAE), Nash-Sutcliffe efficiency index (EI), determination coefficient ($R^2$) were used to evaluate the results of calibration and validation. The statistics of RMSE, RMAE, EI, and $R^2$ were 4.66 mm/day, 0.53 mm/day 0.86, and 0.89 for the calibration period and 3.98 mm/day, 0.51 mm/day, 0.83, and 0.84 for the validation period respectively. The statistical parameters indicated that the model provided a reasonable estimation of the runoff at the study watershed. This result was illustrated with a multi-objective optimization for the flow at an observation site within the Baran reservoir watershed.