• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.9
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• pp.916-922
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• 2009

Because thermal design of satellite carrying out mission in space is performed by thermal analysis result using thermal mathematical model, accuracy of thermal mathematical model is important and it can be improved by model correlation. Correlation steps of satellite thermal math model are composed of modeling of satellite configuration placed in thermal vacuum chamber, verification of correspondence between thermal math model and real satellite configuration, and adjustment of modeling parameters from major part to minor part etc. In this study, correlation success criteria was established and correlation for satellite thermal math model was performed using result of thermal vacuum test of satellite structure-thermal model to meet the success criteria. The overall results satisfied the criteria and this correlated thermal model was applied for detailed thermal design of satellite.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.3
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• pp.413-420
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• 2018

This paper documents the result of a preliminary analysis on the influence of hull-ice friction coefficient on model resistance and power predictions and their correlation to full-scale measurements. The study is based on previous model-scale/full-scale correlations performed on the National Research Council - Ocean, Coastal, and River Engineering Research Center's (NRC/OCRE-RC) model test data. There are two objectives for the current study: (1) to validate NRC/OCRE-RC's modeling standards in regarding to its practice of specifying a CFC (Correlation Friction Coefficient) of 0.05 for all its ship models; and (2) to develop a correction methodology for its resistance and propulsion predictions when the model is prepared with an ice friction coefficient slightly deviated from the CFC of 0.05. The mean CFC of 0.056 and 0.050 for perfect correlation as computed from the resistance and power analysis, respectively, have justified NRC/OCRE-RC's selection of 0.05 for the CFC of all its models. Furthermore, a procedure for minor friction corrections is developed.

• Communications for Statistical Applications and Methods
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• v.21 no.4
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• pp.335-347
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• 2014

This paper employs dynamic conditional correlation (DCC) model to examine time-varying comovement in the Korean stock market with a focus on the financial industry. Analyzing the daily returns of KOSPI 200 eight sector indices from January 2008 to December 2013, we find that stock market correlations significantly increased during the GFC period. The Financial Sector had the highest correlation between the Constructions-Machinery Sector; however, the Consumer Discretionary and Consumer Staples sectors indicated a relatively lower correlation between the Financial Sector. In terms of model fitting, the DCC with t distribution model concludes as the best among the four alternatives based on BIC, and the estimated shape parameter of t distribution is less than 10, implicating a strong tail dependence between the sectors. We report little asymmetric effect in correlation dynamics between sectors; however, we find strong asymmetric effect in volatility dynamics for each sector return.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.541-550
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• 2001

This paper considers a panel data regression model in which the disturbances follow a nested error components with serial correlation. Given this model, this paper derives several Lagrange Multiplier(LM) testis for the presence of serial correlation as well as random individual effects, nested effects, and for existence of serial correlation given random individual and nested effects.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.12
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• pp.1004-1013
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• 2002

Turbulent carbon dioxide flows and cooling heat transfers under supercritical state in a straight duct with a square cross-section are numerically analyzed employing low Reynolds number $\kappa-\varepsilon$ model and algebraic stress model. The flow is assumed to be (quasi-incompressible. Predicted Nusselt number and friction factor are compared with the experimental data, Blasius correlation for friction factor and Dittus-Boelter correlation for Nusselt number. Computational results for the Fanning's friction factor agree well with the all Rohsenow and Choi's correlation, Liou and Hwang's experimental data and Blasius correlation. The results obtained by algebraic stress model agree more with the Liou and Hwang's experimental data, while the results obtained by low Reynolds number $\kappa-\varepsilon$ model agree more with Blasius correlation. In the computation of Nusselt number, Dittus-Boelter correlation can not exactly fit the computational results. Therefore we propose the new correlation$Nu=0.053Re^{0.73}Pr^{0.4}$for the turbulent cooling heat transfer of carbon dioxide under supercritical state.

• Journal of Institute of Convergence Technology
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• v.6 no.2
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• pp.29-33
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• 2016

Recently, the authors of this paper proposed newly the correlation model to calculate the horizontal global solar radiation in Korea based the Zhang-Huang (ZH) model proposed in 2002 for China. Previous study was pronounced the correlation with a new term of the duration of sunshine proved as being closely related with the hourly solar radiation in Korea into ZH model. And then another modified correlation for the regions without measuring cloud cover was proposed and evaluated the accuracy and validity for those regions. So, this study was performed to propose modified correlation to calculate the horizontal global solar irradiance of non-measuring cloud-cover regions. Finally, this study proposed the new correlation that could well predict hourly and daily total solar radiation for all regions, various seasons, and various weather conditions including overcast and clear, with higher accuracy and lower error than other models proposed ever before in Korea for non-measuring cloud-cover regions.

• Atmosphere
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• v.26 no.4
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• pp.711-716
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• 2016

The dynamical model forecasts using state-of-art general circulation models (GCMs) have some limitations to simulate the real climate system since they do not depend on the past history. One of the alternative methods to correct model errors is to use the canonical correlation analysis (CCA) correction method. CCA forecasts at the present time show better skill than dynamical model forecasts especially over the midlatitudes. Model outputs are adjusted based on the CCA modes between the model forecasts and the observations. This study builds a canonical correlation prediction model for subseasonal (June) precipitation. The predictors are circulation fields over western North Pacific from the Global Seasonal Forecasting System version 5 (GloSea5) and observed snow cover extent over Eurasia continent from Climate Data Record (CDR). The former is based on simultaneous teleconnection between the western North Pacific and the East Asia, and the latter on lagged teleconnection between the Eurasia continent and the East Asia. In addition, we suggest a technique for improving forecast skill by applying the ensemble canonical correlation (ECC) to individual canonical correlation predictions.

• ETRI Journal
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• v.45 no.5
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• pp.768-780
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• 2023

The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three-dimensional multiple-input multiple-output concentric two-hemisphere model is proposed to characterize the scattering environment around a vehicle in an urban UAV-to-vehicle communication scenario. Multipath components of the model consisted of lineof-sight and single-bounced components. This study focused on the key parameters that determine the scatterer distribution. A time-variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space-time-frequency correlation function, Doppler power spectral density, level-crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level-crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level-crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry-based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.

• Journal of Distribution Research
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• v.1 no.2
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• pp.29-52
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• 1996

The objective of this study is to test Parasurman, Zeithaml & Berry(1985)'s service quality gap analysis model, and to confirm reliability and to confirm reliability and validity of the model. The reliability of the model is adopted, but the validity must be retested in the further study. In the results of the gap analysis model, the correlation between consumer expectations and management perceptions of consumer expectations(gap1) was not accepted. The correlation between management perceptions of consumer expectations and service quality specifications(gap2), the correlation between service quality specifications and the service actually delivered(gap3), and the correlation between services delivered and services promised to consumers(gap4) were accepted. To improve domestic telecommunication service quality, practical guides such as standardization of job roles, cooperation among customer-contact persons, communication between managers and employees, evaluation programs for employee's improvement of service quality, appropriate supports for customer-contact persons were suggested. For generalizing the gap model, additional studies under the different contexts and industries will be needed.

• Journal of Korean Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.