• International Journal of Highway Engineering
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• v.10 no.3
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• pp.129-138
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• 2008

The finite element method(FEM) was one of tools used to solve problem of previous Concrete Pavement and was applied to Korea Pavement Research Program Study. This study used the ABAQUS and the fortran analysis program to calculate the critical stress on jointed concrete pavement and compared and analyzed the results by using neural networks and linear regression model. In that case, which are not enough analysises by using FEM programs though many input variables, when the results of FEM with NN and linear regression models are compared, there are some differences. The other cases, which are reduced input variables and a lot of analysises each of them, results of Neural Networks(NN) and linear regression models are simulated to them of FEM. But, the result of NN is more exact than them of linear regression at the (0,0), (1,1). On the results of this study, it is suggested that the calculation of stress using NN is more compatible to Korea Pavement Research Program Study.

• Structural Engineering and Mechanics
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• v.83 no.6
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• pp.709-727
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• 2022

Fastening systems have a significant role in the response of railway slab track systems. Although experimental tests indicate nonlinear behavior of fastening systems, they have been simulated as a linear spring-dashpot element in the available literature. In this paper, the influence of the nonlinear behavior of fastening systems on the slab track response was investigated. In this regard, a nonlinear model of vehicle/slab track interaction, including two commonly used fastening systems (i.e., RFFS and RWFS), was developed. The time history of excitation frequency of the fastening system was derived using the short time Fourier transform. The model was validated, using the results of a comprehensive field test carried out in this study. The frequency response of the track was studied to evaluate the effect of excitation frequency on the railway track response. The results obtained from the model were compared with those of the conventional linear model of vehicle/slab track interaction. The effects of vehicle speed, axle load, pad stiffness, fastening preload on the difference between the outputs obtained from the linear and nonlinear models were investigated through a parametric study. It was shown that the difference between the results obtained from linear and nonlinear models is up to 38 and 18 percent for RWFS and RFFS, respectively. Based on the outcomes obtained, a nonlinear to linear correction factor as a function of vehicle speed, vehicle axle load, pad stiffness and preload was derived. It was shown that consideration of the correction factor compensates the errors caused by the assumption of linear behavior for the fastening systems in the currently used vehicle track interaction models.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.7
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• pp.910-914
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• 2001

The teat number of a sow plays an important role for weaning pigs and has been utilized in selection of swine breeding stock. Various linear models have been employed for genetic analyses of teat number although the teat number can be considered as a count trait. Theoretically, Poisson error mixed models are more appropriate for count traits than Normal error mixed models. In this study, the two models were compared by analyzing data simulated with Poisson error. Considering the mean square errors and correlation coefficients between observed and fitted values, the Poisson generalized linear mixed model (PGLMM) fit the data better than the Normal error mixed model. Also these two models were applied to analyzing teat numbers in four breeds of swine (Landrace, Yorkshire, crossbred of Landrace and Yorkshire, crossbred of Landrace, Yorkshire, and Chinese indigenous Min pig) collected in China. However, when analyzed with the field data, the Normal error mixed model, on the contrary, fit better for all the breeds than the PGLMM. The results from both simulated and field data indicate that teat numbers of swine might not have variance equal to mean and thus not have a Poisson distribution.

• International Journal of Highway Engineering
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• v.17 no.6
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• pp.75-83
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• 2015

PURPOSES : This study deals with the traffic accidents classified by the traffic analysis zone. The purpose is to develop the accident density models by using zonal traffic and socioeconomic data. METHODS : The traffic accident density models are developed through multiple linear regression analysis. In this study, three multiple linear models were developed. The dependent variable was traffic accident density, which is a measure of the relative distribution of traffic accidents. The independent variables were various traffic and socioeconomic variables. CONCLUSIONS : Three traffic accident density models were developed, and all models were statistically significant. Road length, trip production volume, intersections, van ratio, and number of vehicles per person in the transportation-based model were analyzed to be positive to the accident. Residential and commercial area ratio and transportation vulnerability ratio obtained using the socioeconomic-based model were found to affect the accident. The major arterial road ratio, trip production volume, intersection, van ratio, commercial ratio, and number of companies in the integrated model were also found to be related to the accident.

• Journal of Environmental Science International
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• v.21 no.6
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• pp.695-704
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• 2012

The application of disinfection models on the plasma process was investigated. Nine empirical models were used to find an optimum model. The variation of parameters in model according to the operating conditions (first voltage, second voltage, air flow rate, pH) were investigated in order to explain the disinfection model. In this experiment, the DBD (dielectric barrier discharge) plasma reactor was used to inactivate Ralstonia Solanacearum which cause wilt in tomato plantation. Optimum disinfection models were chosen among the nine models by the application of statistical SSE (sum of squared error), RMSE (root mean sum of squared error), $r^2$ values on the experimental data using the GInaFiT software in Microsoft Excel. The optimum model was shown as Weibull+talil model followed by Log-linear+ Shoulder+Tail model. Two models were applied to the experimental data according to the variation of the operating conditions. In Weibull+talil model, Log10($N_o$), Log10($N_{res}$), ${\delta}$ and p values were examined. And in Log-linear+Shoulder+Tail model, the Log10($N_o$), Log10($N_{res}$), $k_{max}$, Sl values were calculated and examined.

• International Journal of Highway Engineering
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• v.15 no.2
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• pp.121-129
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• 2013

PURPOSES : This study deals with the traffic accidents of rotary in Korea. The objective of this study is to develop the accident models by age group based on the various data of rotaries. METHODS : In pursuing the above, this study gives particular attentions to classifying the accident data of 17 rotaries by age, collecting the data of geometric structure, traffic volume and others, and developing the models using SPSS 17.0 and EXCEL. RESULTS : First, 3 multiple linear regression models which were all statistically significant were developed. The value of model of under 30-49 age group were, however, evaluated to be 0.688 and be less than those of other models. Second, the most powerful variables were analyzed to be traffic volume in the model of under 30 age group, circulatory roadway width in the model of 30-49 age group, and the number of approach lane in the model of above 50 age group. Finally, the test results of accident models using RMSE were all evaluated to be fitted to the given data. CONCLUSIONS : This study propose install streetlights, speed humps and widen Circulatory as effective improvements for reduction of accident in rotary.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1322-1327
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• 2004

There are some previous studies that utilize constant impedance models or constant current models for electric trains to perform the static analysis of AT feeding systems. These mentioned models have some merits of linear systems but yield erroneous results because of the innate restraints of the models since linear models cannot represent the features of constant power in inverter-driven trains. From these reasons, it is suitable that the train be considered as a constant load model when it drives or as a constant source model when it applies regenerative brake. However, excessive rise of regenerative voltage during the braking may damage the vehicle itself and the feeding systems so the voltage must be restricted below a certain value. Keeping these facts in minds, we suggest new methods of analyzing AT feeding systems using the constant power models with the conditions of voltage constraints. The simulation results from a sample system using the proposed method illustrate both the states of system variables and the supply-demand relation of power among the trains and the systems very clearly, so it is believed that the proposed method yields more accurate results than conventional methods do.

• Earthquakes and Structures
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• v.23 no.6
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• pp.549-580
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• 2022

This paper assesses the influences of modeling assumptions and uncertainties on the performance of the non-linear finite element (FE) model updating procedure and model clustering method. The results of a shaking table test on a four-story steel moment-resisting frame are employed for both calibrations and clustering of the FE models. In the first part, simple to detailed non-linear FE models of the test frame is calibrated to minimize the difference between the various data features of the models and the structure. To investigate the effect of the specified data feature, four of which include the acceleration, displacement, hysteretic energy, and instantaneous features of responses, have been considered. In the last part of the work, a model-based clustering approach to group models of a four-story frame with similar behavior is introduced to detect abnormal ones. The approach is a composition of property derivation, outlier removal based on k-Nearest neighbors, and a K-means clustering approach using specified data features. The clustering results showed correlations among similar models. Moreover, it also helped to detect the best strategy for modeling different structural components.

• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.