• Journal of the Korean Statistical Society
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• v.5 no.1
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• pp.19-28
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• 1976

In empirical study for fitting a multiple linear regression model for successive cross-sections data observed on the same set of independent variables over several time periods, one often faces the problem of poor $R^2$, the multiple coefficient of determination, which provides a standard measure of how good a specified regression line fits the sample data.

• International Journal of Highway Engineering
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• v.19 no.5
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• pp.143-151
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• 2017

PURPOSES : The purpose of this study is to develop models of accidents occurring at circular intersections related to the time of day and night and driver gender, and to provide countermeasures for safer circular intersections. METHODS : Seventy intersections built before 2008 were surveyed for inclusion in the modeling. Traffic accident data from 2008 to 2014 were collected from the TAAS data set of the Road Traffic Authority. Sixteen variables explaining the accidents including geometry and traffic volume were selected from the literature and seven multiple linear regression models were developed using SPSS 20.0. RESULTS : First, the null hypotheses, that the number of traffic accidents are not related to driver gender or time of day, were rejected at a 5% level of significance. Second, seven statistically significant accident models with $R^2$ value of 0.643-0.890 were developed. Third, in daytime models by gender, when the right-turn-only lane was selected as the common variable, the number of lanes, presence of driveways and speed humps, diagrammatic exit destination sign, and total entering traffic volume were evaluated as specific variables. Finally, in nighttime models by gender, when the diagrammatic exit destination sign was selected as the common variable, total entering traffic volume, presence of right-turn-only lanes, number of circulatory road way lanes, and presence of splitter islands and driveways were identified as specific variables. CONCLUSIONS:This study developed seven accident models and analyzed the common and specific variables by time of day and gender. The results suggest approaches to providing countermeasures for safer circular intersections.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.199-215
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• 2020

Purpose - This article tries to test if the conditional consumption capital asset pricing model (CCAPM) with bank credit for household as a conditional variable can explain the cross-sectional variation of stock returns in Korea. The performance of conditional CCAPM is compared to that of multifactor asset pricing models based on Arbitrage Pricing Theory. Design/methodology/approach - This paper extends the simple CCAPM to the conditional version of CCAPM by using bank credit for household as conditioning information. By employing KOSPI and KOSDAQ stocks as test assets from the second quarter of 2003 to the first quarter of 2018, this paper estimates risk premiums of conditional CCAPM and a variety of multifactor linear models such as Fama-French three and five-factor models. The significance of risk factors and the adjusted coefficient of determination are the basis for the comparison in models' performances. Findings - First, the paper finds that conditional CCAPM with bank credit performs as well as the multifactor linear models from Arbitrage Pricing theory on 25 test assets sorted by size and book-to-market. When using long-term consumption growth, the conditional CCAPM explains the cross-sectional variation of stock returns far better than multifactor models. Not only that, although the performances of multifactor models decrease on 75 test assets, conditional CCAPM's performance is well maintained. Research implications or Originality - This paper proposes bank credit for household as a conditional variable for CCAPM. This enables CCAPM, one of the most famous economic asset pricing models, to conform with the empirical data. In light of this, we can now explain the cross-sectional variation of stock returns from an economic perspective: Asset's riskiness is determined by its correlation with consumption growth conditional on bank credit for household.

• Journal of Aerospace System Engineering
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• v.18 no.5
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• pp.1-14
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• 2024

This paper aimed to develop mesh stiffness prediction models using spur gear design parameters as input variables through a machine learning ensemble method. A dataset was generated by calculating individual stiffness using a calculation method presented in previous studies and deriving the minimum and maximum values of total mesh stiffness. Using multivariate linear regression, support vector regression, and decision tree regression, models were created to predict the minimum and maximum values of mesh stiffness. The stacking ensemble method was used to create meta models. Prediction models of three algorithms were used as base models. These Ensemble meta models were verified with specifications of gears used in actual aircraft engine starters, showing very high prediction performances. Thus, feasibility of applying Ensemble meta models to an actual gear system and their effectiveness were confirmed.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.79-85
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• 1996

This paper will show that the rank of the model matrix of a closed, n compartmental model with k sinks is n-k. This statement will be extended to include open compartmental models as a part of theorem.

• Structural Engineering and Mechanics
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• v.32 no.3
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• pp.459-475
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• 2009

This paper presents analytical methodologies for remaining life prediction of plain concrete structural components considering tension softening and size effects. Non-linear fracture mechanics principles (NLFM) have been used for crack growth analysis and remaining life prediction. Various tension softening models such as linear, bi-linear, tri-linear, exponential and power curve have been presented with appropriate expressions. Size effect has been accounted for by modifying the Paris law, leading to a size adjusted Paris law, which gives crack length increment per cycle as a power function of the amplitude of a size adjusted stress intensity factor (SIF). Details of tension softening effects and size effect in the computation of SIF and remaining life prediction have been presented. Numerical studies have been conducted on three point bending concrete beams under constant amplitude loading. The predicted remaining life values with the combination of tension softening & size effects are in close agreement with the corresponding experimental values available in the literature for all the tension softening models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.4
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• pp.797-802
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• 2010

In this paper, a systematic procedure to design a nonlinear controller for nonlinear active magnetic bearing (AMB) systems is presented. To do this, we effectively convert the AMB system into a polytopic quasi-linear parameter varying (LPV) system, which is a representation of nonlinear state-space models and is described by the convex combination of a set of precisely known vertices. Unlike the existing quasi-LPV systems, the nonlinear weighting functions, which construct the polytopic quasi-LPV model of the AMB system by connecting the vertices, include not only state variables but also the input ones. This allows us to treat the input nonlinearity effectively. By means of the derived polytopic quasi-LPV model and linear matrix inequality (LMI) conditions, nonlinear controller that stabilizes the AMB system is obtained. The effectiveness of the proposed controller design methodology is finally demonstrated through numerical simulations.

• International Journal of Highway Engineering
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• v.16 no.5
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• pp.173-182
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• 2014

PURPOSES : The purpose of the study is to a) explore the operating speed of trucks on rural highways affected by road geometry, and thereby b) develop a predictive model for the operating speed of trucks on rural highways. METHODS : Considering that most of the existing studies have focused on cars, the current study aimed to predict the operating speed of trucks by conducting linear regression analysis on the speed data of trucks operating on the linear-curved-linear portions of the road as a single set. RESULTS : The operating speed in the plane curve portion increased with the length of the curve, and decreased with a lower vertical grade and a smaller curve radius. In the straight plane portion, the operating speed increased with a larger curve radius(upstream), and decreased with an increase in the change of the vertical grade, depending on the length of the vertical curve. CONCLUSIONS : This study developed estimation models of truck for operational speed and evaluated the degree of safety for horizontal and vertical alignments simultaneous. In order to represent whole area of the rural highway, the models should be ew-analyzed with vast data related with road alignment factor in the near future.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.3
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• pp.183-190
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• 2016

In this study, we conduct linear and nonlinear modeling of the DC motor driving system of a wheeled mobile robot, which is a nonlinear system involving dead zone, friction, and saturation. The DC motor driving system consists of a DC motor, a wheel, and gears. A linear DC motor driving system is modeled using a steady-state response and parameter measurements. A nonlinear DC motor driving model is identified with the use of the Hammerstein-Wiener method. By using these models, PID controllers for the DC motor system are then established. Each PID controller is applied as a low-level controller in order to achieve posture stabilization control for the real mobile robot. We also compare the performance of the proposed PID controllers in posture stabilization experiments by using several different final robot postures.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.702-708
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• 2010

This paper proposes a novel linearization method for Takagi.sugeno (TS) fuzzy model. A T-S fuzzy controller consists of linear controllers based on local linear models and the local linear controllers cannot be designed independently because of overall stability conditions which are usually conservative. To use linear control theories easily for T-S fuzzy system, the linearization of T-S fuzzy model is required. However, The linearization of T-S fuzzy model is difficult to be achieved by using existing linearization methods because fuzzy rules and membership functions are included in T-S fuzzy models. So, a new linearization method is proposed for the T-S fuzzy system based on the idea of T-S fuzzy state transformation. For the T-S fuzzy system linearized with uncertainties, a robust optimal controller with the robustness of sliding model control(SMC) is designed.