• 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.

• Journal of the Korean Statistical Society
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• v.36 no.1
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• pp.77-91
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• 2007

This paper considers development of noninformative priors for the familial data when the families have equal number of offspring. Several noninformative priors including the widely used Jeffreys' prior as well as the different reference priors are derived. Also, a simultaneously-marginally-probability-matching prior is considered and probability matching priors are derived when the parameter of interest is inter- or intra-class correlation coefficient. The simulation study implemented by Gibbs sampler shows that two-group reference prior is slightly edge over the others in terms of coverage probability.

• International Journal of Safety
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• v.10 no.1
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• pp.5-9
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• 2011

Two popular activity coefficient models, Wilson and NRTL equations have been used to correlate the published flash point data on the n-propanol + propionic acid and n-butanol + propionic acid systems through the optimization method. The results of these correlation were compared with the results calculated by Raoult's law. The optimization method were found to be better than those based on the Raoult's law. The optimization method based on the Wilson equation described the published data more effectively than was the case when the optimization method was based upon the NRTL equation.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.1
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• pp.85-93
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• 2020

The basic information of mean sea level data of all tidal monitoring stations in Korea was reviewed, and the correlation coefficients between the stations were analyzed. Mean sea level changes expected due to global climate change effects were found to show a high correlation of more than 0.75 regardless of the distance between the stations were analyzed. The data between certain stations were found to have negative correlation and low correlation of 0.25 or less, but this was determined by the influence of small data numbers and outliers. However, since these correlations assume a linear increase and a linear relationship, the estimation results may be distorted for data with fluctuating trends that deviate from this assumption. Based on the results of the changing patterns of the MSL data, it shows that a number of the MSL data do not follow the linear trend.

• 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.

• Smart Structures and Systems
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• v.20 no.2
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• pp.197-205
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• 2017

The dynamic performance of railway bridges under high-speed trains draws the attention of bridge engineers. The vibration issue for long-span bridges under high-speed trains is still not well understood due to lack of validations through structural health monitoring (SHM) data. This paper investigates the correlation between bridge acceleration and train speed based on structural dynamics theory and SHM system from three foci. Firstly, the calculated formula of acceleration response under a series of moving load is deduced for the situation that train length is near the length of the bridge span, the correlation between train speed and acceleration amplitude is analyzed. Secondly, the correlation scatterplots of the speed-acceleration is presented and discussed based on the transverse and vertical acceleration response data of Dashengguan Yangtze River Bridge SHM system. Thirdly, the warning indexes of the bridge performance for correlation scatterplots of speed-acceleration are established. The main conclusions are: (1) The resonance between trains and the bridge is unlikely to happen for long-span bridge, but a multimodal correlation curve between train speed and acceleration amplitude exists after the resonance speed; (2) Based on SHM data, multimodal correlation scatterplots of speed-acceleration exist and they have similar trends with the calculated formula; (3) An envelope line of polylines can be used as early warning indicators of the changes of bridge performance due to the changes of slope of envelope line and peak speed of amplitude. This work also gives several suggestions which lay a foundation for the better design, maintenance and long-term monitoring of a long-span high-speed bridge.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• Ocean and Polar Research
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• v.43 no.1
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• pp.15-30
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• 2021

In this study, using 16 ORA-IP (Ocean Reanalysis Intercomparison Project) data, we investigated spatial and temporal changes of net surface heat flux in the East Asian seas and presented a new ensemble net surface heat flux index. The ensemble net surface heat flux index is produced considering the data distribution and the standard deviation of each ORA-IP. From the correlation analysis with air temperature averaged over the Korean Peninsula, ensemble net heat flux around the Korea Strait shows the highest correlation (0.731) with a 3 month time lag. For the correlation study regarding precipitation over the Korean Peninsula, it also shows significant correlation especially in winter and spring seasons. Similar results are also found in comparison with climate indices (AO, PDO, and NINO3.4), but ensemble net surface heat flux data in winter season reveals the strongest correlation patterns especially with winter temperature and spring precipitation.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.262-265
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• 2007

Preliminary results are reported on ship detection using coherence images computed from cross-correlating images of multi-look-processed dual-polarization data (HH and HV) of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, cross-correlation of multi-look images yields the different degrees of coherence between the images and water. The polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV. In the next step, we examine the technique when the dual-polarization data are split into two multi-look Images.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.425-428
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• 1999

The radar interferogram represents phase differences between the two synthetic aperture radar observations acquired in slightly different angle. The success of the radar interferometric application largely depends on the quality of the interferogram generated from two or more synthetic aperture radar data sets. We propose here to apply the wavenumber correlation analysis to the in-phase and quadrature phase of the radar interferogram. The wavenumber correlation analysis is to resolve the highly correlated components from the low correlation components by estimating correlation coefficients for each wavenumber component. Through this approach, one can easily distinguish the signal components from the noise components in the wavenumber domain. Therefore, the wavenumber correlation analysis of the radar interferogram can be utilized to design post filter and to estimate the quality of interferogram. We have tested the wavenumber correlation analysis using a Radarsat SAR data pair to demonstrated the effectiveness of