• Wind and Structures
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• v.4 no.3
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• pp.177-196
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• 2001

At present the most popular turbulence models used for engineering solutions to flow problems are the $k-{\varepsilon}$ and Reynolds stress models. The shortcoming of these models based on the isotropic eddy viscosity concept and Reynolds averaging in flow fields of the type found in the field of Wind Engineering are well documented. In view of these shortcomings this paper presents the implementation of a non-linear model and its evaluation for flow around a building. Tests were undertaken using the classical bluff body shape, a surface mounted cube, with orientations both normal and skewed at $45^{\circ}$ to the incident wind. Full-scale investigations have been undertaken at the Silsoe Research Institute with a 6 m surface mounted cube and a fetch of roughness height equal to 0.01 m. All tests were originally undertaken for a number of turbulence models including the standard, RNG and MMK $k-{\varepsilon}$ models and the differential stress model. The sensitivity of the CFD results to a number of solver parameters was tested. The accuracy of the turbulence model used was deduced by comparison to the full-scale predicted roof and wake recirculation zone lengths. Mean values of the predicted pressure coefficients were used to further validate the turbulence models. Preliminary comparisons have also been made with available published experimental and large eddy simulation data. Initial investigations suggested that a suitable turbulence model should be able to model the anisotropy of turbulent flow such as the Reynolds stress model whilst maintaining the ease of use and computational stability of the two equations models. Therefore development work concentrated on non-linear quadratic and cubic expansions of the Boussinesq eddy viscosity assumption. Comparisons of these with models based on an isotropic assumption are presented along with comparisons with measured data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.137-145
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• 2005

In this paper, we investigate the accuracy of various sensor models developed for linear pushbroom satellite images. We define the accuracy of a sensor model in two aspects: the accuracy of bundle adjustments and the accuracy of estimating exterior orientation parameters. The first accuracy has been analyzed and reported frequently whereas the second accuracy has somewhat been neglected. We argue that the second accuracy is as important as the first one. The second accuracy describes a model's ability to predict satellite orbit and attitude, which has many direct and indirect applications. Analysis was carried out on the traditional collinearity-based sensor models and orbit-based sensor models. Collinearity-based models were originally developed for aerial photos and modified for linear pushbroom-type satellite images. Orbit-based models have been used within satellite communities for satellite control and orbit determination. Models were tested with two Kompsat-1 EOC scenes and GPS-driven control points. Test results showed that orbit-based models produced better estimation of exterior orientation parameters while maintained comparable accuracy on bundle adjustments.

• Structural Engineering and Mechanics
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• v.71 no.5
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• pp.545-552
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• 2019

The analysis and design of complex structures like sandwich-panel elements are difficult; the use of finite element method for the analysis is complicated and time consuming when non-linear effects are considered. On the other hand, artificial neural network (ANN) models can capture the non-linear effects and its application requires lesser computational demand. Two ANN models were trained, tested and validated to compute the force for a given displacement of a sandwich-type roof element; 2555 force and element deformation pairs were used for training the ANN models. For the models trained without considering the damping effect, there were two values in the input layer: maximum displacement and current displacement, and for the model considering damping, displacement from the previous step was used as an additional input. Totally, 400 ANN models were trained. Results show that there is a good agreement between the experimental and simulated data, and the models showed a good performance with a mean square error value of 4548.85. Both the ANN models could simulate the inelastic behaviour, loss of rigidity, and evolution of permanent displacements. The models could also interpolate and extrapolate, which enables them to be used as an analysis and design tool for such complex elements.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.9
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• pp.1227-1232
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• 2008

Carcass characteristics data of Hanwoo (N = 1,084) were collected from two stations of the National Livestock Institute of Animal Science (NIAS), Korea and records from thirteen individual cow-calf operators were analyzed to estimate variance and covariance components and the effect of different slaughter endpoints. Carcass traits analyzed were cold carcass weight (CWT, kg), REA (rib eye area, cm2), back fat thickness (mm) and marbling score (1-7). Four different models were examined. All models included sex and contemporary group as fixed effects and the animal's direct genetic potential and environment as random effects. The first model fitted a linear covariate of age at slaughter. The second model fitted both linear and quadratic covariates of age at slaughter. The third model fitted a linear covariate of body weight at slaughter. The fourth model fitted both linear covariates of age at slaughter and body weight at slaughter. Variance components were estimated using the REML procedure with Gibb's sampler. Heritability estimate of CWT was in the range of 0.08-0.11 depending on the model applied. Heritability estimates of BF, REA and MS were in the ranges of 0.23-0.28, 0.19-0.26, and 0.44-0.45, respectively. Genetic correlations between CWT and BF, between CWT and REA, and between CWT and MS were in the ranges of -0.33 - -0.14, 0.73-0.84, and -0.01- 0.11, respectively. Genetic correlations between REA and BF, between MS and BF and between REA and MS were in the ranges of -0.82 ~ -0.72, 0.04~0.28 and -0.08 ~ -0.02, respectively. Variance and covariance components estimated varied by model with different slaughter endpoints. Body weight endpoint was more effective for direct selection in favor of yield traits and body weight endpoints affected more of the correlated response to selection for the traits of yield and quality of edible portion of beef.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.41-47
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• 2009

This study deals with the side-swipe accidents of 4-legged signalized intersections in Cheongju. The objectives are to analyze the characteristics of the accidents and to develop the related models. In pursuing the above, this study gives particular emphasis to finding the appropriate methodology to modelling. The main results are as follows. First, injuries were analyzed to be twice than property-only accidents in the side-swipe accidents. The accidents were evaluated to occur more in inside-intersection. Also, the accidents were analyzed to be almost the auto-related accidents and to be occurred by the unsafely-driving activity. Second, multiple linear regression models were evaluated to be more statistically significant than multiple non-linear. The most fitted models were analyzed to be the models with the number of accidents as the dependent variable. The factors of side-swipe accidents analyzed in this study were ADT, area of intersection, right-turn-only-lane, number of pedestrian crossings, limited speed of main road, maximum grade and number of signal phase.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.229-243
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• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.5 s.143
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• pp.534-541
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• 2005

Until now, Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how in its own. It is very useful to extract knowledge or information from the accumulated existing data by using data mining technique This paper treats an evolutionary computation based on genetic programming (GP), which can be one of the components to realize data mining. The paper deals with linear models of GP for the regression or approximation problem when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by utilizing the symbolic processing algorithm. In addition to a standard linear model consisting of mathematic functions, one variant form of a linear model, which can be built using low order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper. The suggested model can be utilized as a designing tool to predict design parameters with small accumulated data.

• The Korean Journal of Applied Statistics
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• v.4 no.2
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• pp.117-127
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• 1991

Coefficient of determination $R^2$ is most frequently used descriptive measure in practical use of linear regression analysis. But there have been controversies on defining this measure in the cases of linear regression without the intercept, weighted linear regression and robust linear regression. Several authors such as Kvalseth(1985) and Willet and Singer(1988) proposed many variations of $R^2$ to meet the situations. However, theire measures are not satisfactory due to the lack of a universal principle. In this study, we propose a unfied approach to defining the coefficient of determination $R^2$ using the concept of likelihood distance. This new measure is in good accordance with typical $R^2$ in linear regression and, moreover, can be applied to nonlinear regression models and generalized linear models such as logit and log-linear models.

• Imaging Science in Dentistry
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• v.34 no.1
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• pp.13-18
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• 2004

Purpose : This study was to evaluate the influence of slice thickness of computed tomography (CT) and rapid protyping (RP) type on the accuracy of 3-dimensional medical model. Materials and Methods: Transaxial CT data of human dry skull were taken from multi-detector spiral CT. Slice thickness were 1, 2, 3 and 4 mm respectively. Three-dimensional image model reconstruction using 3-D visualization medical software (V-works /sup TM/ 3.0) and RP model fabrications were followed. 2-RP models were 3D printing (Z402, Z Corp., Burlington, USA) and Stereolithographic Apparatus model. Linear measurements of anatomical landmarks on dry skull, 3-D image model, and 2-RP models were done and compared according to slice thickness and RP model type. Results: There were relative error percentage in absolute value of 0.97, 1.98,3.83 between linear measurements of dry skull and image models of 1, 2, 3 mm slice thickness respectively. There was relative error percentage in absolute value of 0.79 between linear measurements of dry skull and SLA model. There was relative error difference in absolute value of 2.52 between linear measurements of dry skull and 3D printing model. Conclusion: These results indicated that 3-dimensional image model of thin slice thickness and stereolithographic RP model showed relative high accuracy.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.199-215
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• 2018

Interest in $PM_{10}$ concentrations have increased greatly in Korea due to recent increases in air pollution levels. Therefore, we consider a forecasting model for next day $PM_{10}$ concentration based on the principal elements of air pollution, weather information and Beijing $PM_{2.5}$. If we can forecast the next day $PM_{10}$ concentration level accurately, we believe that this forecasting can be useful for policy makers and public. This paper is intended to help forecast a daily mean $PM_{10}$, a daily max $PM_{10}$ and four stages of $PM_{10}$ provided by the Ministry of Environment using various data mining techniques. We use seven models to forecast the daily $PM_{10}$, which include five regression models (linear regression, Randomforest, gradient boosting, support vector machine, neural network), and two time series models (ARIMA, ARFIMA). As a result, the linear regression model performs the best in the $PM_{10}$ concentration forecast and the linear regression and Randomforest model performs the best in the $PM_{10}$ class forecast. The results also indicate that the $PM_{10}$ in Seoul is influenced by Beijing $PM_{2.5}$ and air pollution from power stations in the west coast.