• Atmosphere
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• v.25 no.4
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• Bulletin of the Society of Naval Architects of Korea
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• v.26 no.4
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• pp.14-26
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• 1989

This paper describes the method of statistical analysis and its programs for predicting the ship's powering performance. The equation for the wavemaking resistance coefficient is derived as the sectional area coefficients by using the wavemaking resistance theory and its regression coefficients are determined from the regression analysis of the model test results. The equations for the form factor, wake franction and thrust deduction fraction are derived by purely regression analysis of the principal dimensions, sectional area coefficients and model test results. The statistical analyses are performed using the various descriptive statistic and stepwise regression analysis techniques. The powering performance prognosis program is developed to cover the prediction of resistance coefficients, propulsive coefficients, propeller open-water efficiency and various scale effect corrections.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.1-10
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• 2020

This paper investigates the influence of the Circulating Water Channel (CWC) side wall and support struts on the hydrodynamic coefficient prediction for Autonomous Underwater Vehicles (AUVs) experiments. Computational Fluid Dynamics (CFD) method has been used to model the CWC tests. The hydrodynamic coefficients estimated by CFD are compared with the prediction of experiments to verify the accuracy of simulations. In order to study the effect of side wall on the hydrodynamic characteristics of the AUV in full scale captive model tests, this paper uses the CWC non-dimensional width parameters to quantify the correlation between the CWC width and hydrodynamic coefficients of the chosen model. The result shows that the hydrodynamic coefficients tend to be constant with the CWC width parameters increasing. Moreover, the side wall has a greater effect than the struts.

• The KSFM Journal of Fluid Machinery
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• v.6 no.1 s.18
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• pp.14-22
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• 2003

In this paper the leakage prediction md rotordynamic analysis of an annular seal with a smooth rotor and spiral-grooved stator is performed. For the development of a theoretical model, the three-control-volume analysis of the circumferentially-grooved seal is expanded by considering pressure reduction due to the pumping effect of spiral groove and pressurized flow through the spiral groove. Validation on the present analysis is achieved by comparisons with available experimental data. For the leakage prediction the present analysis generally shows a reasonable agreement with experimental results. Rotordynamic coefficients for rotor speed with spiral angles show same trend, but the magnitudes of rotordynamic coefficients yield differences between analysis and experimental results.

• Wind and Structures
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• v.30 no.3
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• pp.289-298
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• 2020

Gusts generated by downburst have caused a great variety of structural damages in many regions around the world. It is of great significance to accurately evaluate the downburst-induced wind load on high-rise building for the wind resistance design. The main objective of this paper is to propose a computational modeling approach which can satisfactorily predict the mean and fluctuating wind pressure coefficients induced by downburst on high-rise building surfaces. In this study, using an impinging jet to simulate downburst-like wind, and simultaneous pressure measurements are obtained on a high-rise building model at different radial locations. The model test data are used as the database for developing back propagation neural network (BPNN) models. Comparisons between the BPNN prediction results and those from impinging jet test demonstrate that the BPNN-based method can satisfactorily and efficiently predict the downburst-induced wind pressure coefficients on single and overall surfaces of high-rise building at various radial locations.

• Journal of Mechanical Science and Technology
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• v.15 no.7
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• pp.889-894
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• 2001

In this study, the performances of various turbulence closure models are evaluated in the calculation of a transonic flow over axisymmetric bump. k-$\varepsilon$, explicit algebraic stress, and two Reynolds stress models, i.e., GL model proposed by Gibson & Launder and SSG model proposed by Speziale, Sarkar and Gatski, are chosen as turbulence closure models. SSG Reynolds stress model gives best predictions for pressure coefficients and the location of shock. The results with GL model also show quite accurate prediction of pressure coefficients down-stream of shock wave. However, in the predictions of mean velocities and turbulent stresses, the results are not so satisfactory as in the prediction of pressure coefficients.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.2
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• pp.109-118
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• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• Earthquakes and Structures
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• v.8 no.5
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• pp.957-976
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• 2015

Recently, two energy-based response parameters, i.e., the absolute and the relative elastic input energy equivalent velocity, have been receiving a lot of research attention. Several studies, in fact, have demonstrated the potential of these intensity measures in the prediction of the seismic structural response. Although some ground motion prediction equations have been developed for these parameters, they only provide marginal distributions without information about the joint occurrence of the spectral values at different periods. In order to build new prediction models for the two equivalent velocities, a large set of ground motion records is used to calculate the correlation coefficients between the response spectral values corresponding to different periods and components of the ground motion. Then, functional forms adopted in models from the literature are calibrated to fit the obtained data. A new functional form is proposed to improve the predictions of the considered models from the literature. The components of the ground motion considered in this study are the two horizontal ones only. Potential uses of the proposed equations in addition to the prediction of the correlation coefficients of the equivalent velocity spectral values are shown, such as the prediction of derived intensity measures and the development of conditional mean spectra.

• ETRI Journal
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• v.32 no.2
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• pp.345-347
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• 2010

In this letter, we present a new speech hash function based on the non-negative matrix factorization (NMF) of linear prediction coefficients (LPCs). First, linear prediction analysis is applied to the speech to obtain its LPCs, which represent the frequency shaping attributes of the vocal tract. Then, the NMF is performed on the LPCs to capture the speech's local feature, which is then used for hash vector generation. Experimental results demonstrate the effectiveness of the proposed hash function in terms of discrimination and robustness against various types of content preserving signal processing manipulations.

• Journal of the Korean Institute of Navigation
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• v.1 no.1
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• pp.1-16
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• 1977

The aspects of Omega phase prediction are briefly reviewed, and Swanson's Model and Pierce's Model are presented. The equations for the Omega phase prediction and the most probable coefficients of the propagating equations are derived on the base of Pierce's Model by the least square method. The coefficients are calculated from the data which are the phase differences between the pairs of the Station A (Aldra, Norway), C(Haiku, Hawaii), and D(La Mour, North Dakota) observed at Busan Harbor of the South Coast of Korea in June and September, 1976. It is clearly shown that the standard deviations of the observed lane values at Busan Harbor are as followed: 1. June, 1976. Pair (A-C) : 0.1446 Pair (C-D) : 0.2598 2.September, 1976. Pair (A-D) : 0.3958 Pafr (C-D) : 0.3278 As a conclusion of the above investigation, it is shown that the Omega phase velocity can be predicted by the method, proposed in this paper, of analyzing the diurnal and seasonal variations of the Omege phase velocity except SID, PCD and AZD. If more observed data are employed, more exact Omega phase velocity is expected to be obtained.