• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.11
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• pp.47-56
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• 2019

A deep neural network (DNN) model was proposed to predict the upset in the inertia friction welding process using a database comprising results from a series of FEM analyses. For the database, the upset length, upset beginning time, and upset completion time were extracted from the results of the FEM analyses obtained with various of axial pressure and initial rotational speed. A total of 35 training sets were constructed to train the proposed DNN with 4 hidden layers and 512 neurons in each layer, which can relate the input parameters to the welding results. The mean of the summation of squared error between the predicted results and the true results can be constrained to within 1.0e-4 after the training. Further, the network model was tested with another 10 sets of welding input parameters and results for comparison with FEM. The test showed that the relative error of DNN was within 2.8% for the prediction of upset. The results of DNN application revealed that the model could effectively provide welding results with respect to the exactness and cost for each combination of the welding input parameters.

• Journal of Information Technology Applications and Management
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• v.25 no.4
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• pp.115-122
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• 2018

Software reliability has the greatest impact on computer system reliability and software quality. For this software reliability analysis, In this study, we compare and analyze the trends of the properties affecting the reliability according to the shape parameters of Erlang distribution based on the finite fault NHPP. Software failure time data were used to analyze software failure phenomena, the maximum likelihood estimation method was used for parameter estimation. As a result, it can be seen that the intensity function is effective because it shows a tendency to decrease with time when the shape parameters a = 1 and a = 3. However, the pattern of the mean value function showed an underestimation pattern for the true values when the shape parameters a = 1 and a = 2, but it was found to be more efficient when a = 3 because the error width from the true value was small. Also, in the reliability evaluation of the future mission time, the stable and high trend was shown when the shape parameters a = 1 and a = 3, but on the contrary, when a = 2, the reliability decreased with the failure time. Through this study, the property of finite fault NHPP Erlang model according to the change of shape parameter without existing research case was newly analyzed, and new research information that software developers can use as basic guideline was presented.

• Journal of ILASS-Korea
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• v.28 no.4
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• pp.169-175
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• 2023

Multi-injection scheme is being applied to GDI combustion to reduce PM and PN emission to meet the EU7 regulation. However, very short injection duration encounters the ballistic injection region, which injection quantity does not increase linearly with injection duration when applying multi-injection. In this study, numerical studies were conducted to reveal the cause of ballistic injection and the effect of design parameters on ballistic region using 1-D simulation, AMESim. Injection rate and injection quantity were compared with experiment to validate the established model, which showed the accuracy with 10% error. The model revealed that the tendency of ballistic region coincides with the needle motion behavior, which means that parameters at the upper part of needle such as electro-magnetic force, needle spring force and needle friction force have dominant effect on ballistic injection. To figure out the effect of electro-magnetic and needle friction force on ballistic, those parameters were varied to plus and minus 10% with model. The result showed that those parameters clearly changed the ballistic region characteristics, however, the impact became insignificant for outside of ballistic region, which means that the ballistic injection is mainly influenced by initial motion of injector needle.

• Journal of Korean Society on Water Environment
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• v.40 no.4
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• pp.179-194
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• 2024

Biophysicochemical processes in water environments and treatment systems have been great concerns of engineers and scientists for controlling the fate and transport of contaminants. These processes are practically formulated as mathematical models written in coupled differential equations. However, because these process-based mathematical models consist of a large number of model parameters, they are complicated in analytical or numerical computation. Users need to perform substantial trials and errors to achieve the best-fit simulation to measurements, relying on arbitrary selection of fitting parameters. Therefore, this study adopted a Bayesian calibration method to estimate best-fit model parameters in a systematic way and evaluated the applicability of the calibration method to biophysicochemical processes of water environments and treatment systems. The Bayesian calibration method was applied to the microbial growth-decay kinetics and flocculation kinetics, of which experimental data were obtained with batch kinetic experiments. The Bayesian calibration method was proven to be a reasonable, effective way for best-fit parameter estimation, demonstrating not only high-quality fitness, but also sensitivity of each parameter and correlation between different parameters. This state-of-the-art method will eventually help scientists and engineers to use complex process-based mathematical models consisting of various biophysicochemical processes.

• Journal of Power System Engineering
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• v.13 no.4
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• pp.68-73
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• 2009

The mathematical model has a different response character with the real system because this mathematical model has the modeling errors and the imprecise value of system's parameters. Therefore to find the value of system parameters as possible as near by real value in the model is necessary to design the controlled system. This study concern about the identification method to estimate the parameter for the magnetic bearing system with RCGA(Real Coded Genetic Algorithm). Firstly, we will get the mathematical model from the current amplifier circuit and the magnetic bearing system. Secondly we will get the step response data in this circuit and system. Finally, we will estimate the unknown parameter's value from the data.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.4
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• pp.421-427
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• 2008

UV disinfection is widely used in water treatment facilities and wastewater treatment plant because of its effectiveness to removal of pathogen and Giardia which is resistant to traditional chemical disinfection. As a design and performance tool of UV disinfection system, 3 dimensional UV intensity models were composed and simulated to compare each other and to find affecting factors in this study. Reflection, refraction and absorption are important parameters in UV intensity model and MPSS and MSSS model can reflect these parameters while LSI model can not. Absorption is the most important parameters among the reflection, refraction, absorption and shadowing so, this should not be neglect. Based on this simulation, shadowing effect is negligible when the number of installed lamp is a few but, this effect can not be neglectable when the number of installed lamp is quite a few. The errors according to shadowing effect is increased as the number of lamp installed increased.

• Communications of the Korean Mathematical Society
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• v.34 no.4
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• pp.1335-1352
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• 2019

In this article, a new family of lifetime distributions by adding two additional parameters is introduced. The new family is called, the logarithmic Kumaraswamy family of distributions. For the proposed family, explicit expressions for some mathematical properties are derived. Maximum likelihood estimates of the model parameters are also obtained. This method is applied to develop a new lifetime model, called the logarithmic Kumaraswamy Weibull distribution. The proposed model is very flexible and capable of modeling data with increasing, decreasing, unimodal or modified unimodal shaped hazard rates. To access the behavior of the model parameters, a simulation study has been carried out. Finally, the potentiality of the new method is proved via analyzing two real data sets.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.2
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• pp.3-10
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• 2008

The main objectives of this study are to calibrate the RICEWQ model with Korean field data and then analyse the sensitivity of the parameters to identify sensitive parameters. The RICEWQ is widely used to predict pesticide fate in a paddy plot. An experimental paddy plot of 0.2 ha($100{\times}20\;m$) at Seobyeon-dong, Daegu, Korea was selected, and field observations for water and pesticide balance were performed from 4 June to 2 September 2006. The molinate, which is a herbicide widely used for weed control in rice culture, was selected. The RICEWQ model was successfully calibrated both for the water and pesticide mass balance. The calibrated model showed a RMSE of 0.537 cm for ponded water depths and a RMSE of 0.036 mg/L for the molinate concentrations in the ponded water. The most sensitive parameters for molinate concentrations in ponded water were the metabolism degradation rate in water, volatilization coefficient, and release rate for slow release formulation. In contrast, the RICEWQ model was not sensitive to parameters such as hydrolysis degradation rate in water and degradation rate in unsaturated soil.

• Tunnel and Underground Space
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• v.28 no.1
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• pp.25-37
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• 2018

Burger's model was used to analyze creep characteristics of unconsolidated rock. Burger's model should determine four physical parameters from two pairs of data. In this study, physical parameters of Burger's model were determined by applying mathematical concept solution. Creep was accelerated for three years using the determined physical parameters of the Burger's model for unconsolidated rocks. As a result, the creep behavior showed a continuous deformation behavior without convergence. Therefore, in this mine, it is analyzed that the application of U-Beam is more appropriate than roofbolt in terms of stability.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.4
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• pp.327-334
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• 2002

The work presents sensitivity and uncertainty analysis of 2-compartment model for the evaluation of indoor radon pollution in a house. Effort on the development of such model is directed towards the prediction of the generation and transfer of radon in indoor air released from groundwater. The model is used to estimate a quantitative daily human exposure through inhalation of such radon based on exposure scenarios. However, prediction from the model has uncertainty propagated from uncertainties in model parameters. In order to assess how model predictions are affected by the uncertainties of model inputs, the study performs a quantitative uncertainty analysis in conjunction with the developed model. An importance analysis is performed to rank input parameters with respect to their contribution to model prediction based on the uncertainty analysis. The results obtained from this study would be used to the evaluation of human risk by inhalation associated with the indoor pollution by radon released from groundwater.