• Journal of the Korean Society of Safety
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• v.27 no.3
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• pp.141-146
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• 2012

This study deals with the accident model using panel data which are composed of time series data of 2005 through 2007 and cross sectional data of link sections in Cheongju. Panel data are repeatedly collected over time from the same sample. The purpose of the study is to develop the traffic accident model using the above panel data. In pursuing the above, this study gives particular attentions to deriving the optimal models among various models including TSCSREG (Time Series Cross Section Regression). The main results are as follows. First, 8 panel data models which explained the various effects of accidents were developed. Second, $R^2$ values of fixed effect models were analyzed to be higher than those of random effect models. Finally, such the variables as the sum of the number of crosswalk on intersections and sum of the number of intersections were analyzed to be positive to the accidents.

• Structural Engineering and Mechanics
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• v.10 no.3
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• pp.245-262
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• 2000

The objective of this study is to analyze the deformational characteristics of a high-vacuum insulation panel that is evacuated to eliminate significant gas-phase conductance through its thickness. The panel is composed of a metal envelope and low thermal conductance spacers. The problem is very challenging because several nonlinearities are involved concurrently. Not only are various finite element models such as triangular, rectangular, beam and circular plate models used to simulate the panel, but also several finite element programs are used to solve the problem based on the characteristics of the finite element model. The numerical results indicate that the effect of the diameter of the spacer on the vertical deformation of the plate panel is negligibly small. The parameter that mainly influences the maximum sag is the spacing between the spacers. The maximum vertical deformation of the panel can be predicted for a practical range of the spacing between the spacers and the thickness of the plate. Compared with the numerical results obtained by the finite element models and the experimental tests, they have a good agreement. The results are represented in both tabular and graphical forms. In order to make the results useful, a curve fitting technique has been applied to predict the maximum deformation of the panel with various parameters. Moreover, the panel was suggested to be a "smart" structure based on thermal effect.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.3 s.234
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• pp.387-394
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• 2005

Accurate predictions and simulations of the behavior of space structures based on analytical models become more important. In order to perform analysis to support the design of Ku-band transponder panel for the Communications and Broadcasting Satellite(CBS), mathematical models of the panel were generated in the form of finite element models. Test verification of these models is required before the transponder panel can be certified for launch environments. A modal identification was performed to obtain modal parameters which can be compared with the test results using correlation techniques. This paper approaches the sensor placement from the standpoint of the structural dynamicist who uses the modal parameter obtained during launch environmental test. The models were validated by performing a test-analysis correlation and updating analysis. It was proved that the Ku-band transponder panel satisfies the environmental test requirements.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.523-542
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• 2007

In this paper we consider semiparametric random effect panel models that contain AR(p) disturbances. We derive the efficient score function and the information bound for estimating the slope parameters. We make minimal assumptions on the distribution of the random errors, effects, and the regressors, and provide semiparametric efficient estimates of the slope parameters. The present paper extends the previous work of Park et al.(2003) where AR(1) errors were considered.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.395-409
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• 2014

Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.444-445
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• 2010

Predictive modeling schemes have been developed to characterize the heat Transfer and thermo-mechanical behavior for the plasma display panel (PDP) in operation. The inverse approach was adopted to predict the accurate temperature distribution and deformation in PDP. The predictive models were validated with the measurements from real panel. The developed models could be utilized to predict and/or improve the product quality of PDP.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.167-173
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• 2005

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. The residual-based GMM t-test is a significance test that is applied after estimating a dynamic model by using the instrumental variable(IV) method and is directly applicable to any other consistently estimated residuals. Monte Carlo simulations show that the t-test has considerably more power than the $m_2$ test or the Sargan test under both forms of serial correlation (i.e., AR(1) and MA(1)).

• Journal of the Korean Statistical Society
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• v.34 no.4
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• pp.367-375
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• 2005

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. The residual-based GMM t-test is a significance test that is applied after estimating a dynamic model by using the instrumental variable (IV) method and is directly applicable to any other consistently estimated residuals. Monte Carlo simulations show that the t-test has considerably more power than the $m_2$ test or the Sargan test under both forms of serial correlation (i.e., AR(1) and MA(1)).

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.10a
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• pp.569-576
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• 2003

Fundamental issues in static finite element analysis of reinforced concrete panel subjected to biaxial tensile loads are discussed. This paper is trying to bring our attention to the appropriate use of concrete material models such as cracking criteria, tension stiffening model and the steel models which are basically used in the nonlinear finite element analysis of reinforced concrete panels. We mainly investigate the sensitivity of available material models and finite element technologies to the finite element analysis result using our recent reinforced concrete panel experiment result. Throughout this study, we found that the judicious use of the material models and finite element technologies with the sound understanding of structural characteristics can only guarantee the accurate prediction of panel behaviour.

• Ocean and Polar Research
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• v.41 no.1
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• pp.35-46
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• 2019

The purpose of this study is to identify factors influencing fish production of shallow-sea aquaculture in South Korea. This study employed the two-way fixed effect and random effect models based on the panel models and also the difference between GMM and system GMM models based on the dynamic panel models using the amount of fish farming production, the number of stocked fry, the number of cultured fish, the amount of inputted feed, the farming area, the number of workers, and the sales price data from 2010 to 2017. First, the two-way fixed effect model of the panel models was selected by panel characteristics, time characteristics and Hausman tests and also the model was statistically significant. As a result of the two-way fixed effect model, the number of stocked fry, the amount of inputted feed, and the number of workers were identified as factors that increase the fish production of shallow-sea aquaculture. However, the number of cultured fish and the sales price were analyzed as factors that reduce the fish production of shallow-sea aquaculture. Second, the system GMM model of the dynamic panel models was selected by Hansen test and Arellano-Bond test in order to identify whether or not the over-discrimination condition is appropriate. Based on the system GMM model, the number of stocked fry, the amount of inputted feed, the number of workers in this year and 1 year ago, the number of cultured fish 2 years ago, and the sale price 3 years ago were analyzed as factors that increase the fish production of shallow-sea aquaculture. However, the amount of fish farming production 1, 2, 3 years ago, the farming area in this year, and the number of cultured fish in this year and 1 year ago were identified as factors that reduce the fish production of shallow-sea aquaculture. In conclusion, this study suggests that it is desirable to control the amount of stocked fry rather than to expand the farming area for fish farming in shallow-sea aquaculture, so as to keep the sale price at a certain level by maintaining the appropriate amount of fish production.