• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.885-898
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• 2012

In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.44-53
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• 2009

In this paper, Recursive Least Squares (RLS) and Fourier Transform Regression (FTR) methods for estimating stability and control derivatives of small unmanned helicopter are evaluated together with MMLE technique. Flight data simulated by using a commercial small-scale helicopter model are exploited to estimate the parameters with accuracies for hover and cruise modes. The performances of the system identification methods are also compared by analyzing the responses of the reconstructed systems using estimated derivatives.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• International Journal of Highway Engineering
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• v.16 no.5
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• pp.59-66
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• 2014

PURPOSES : This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS : For measuring the friction noise between the surface and vehicle's tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.

• Journal of Mechanical Science and Technology
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• v.16 no.3
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• pp.327-337
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• 2002

This paper applies a recently developed on-line parameter identification (PID) technique to sets of real flight data and compares the results with those of a state-of-the-art off-line PID technique. The on-line PID technique takes Linear Regression from Fourier Transformed equations and the off-line PID is based on the traditional Maximum Likelihood method. Sets of flight data from the NASA F/A-18 High Alpha research Vehicle (HARV) circraft, which has been recorded from specifically designed maneuvers and used for our line parameter estimation, are used for this study. The emphasis is given on the accuracy and on-line measure of reliability of the estimates. The comparison is performed for both longitudinal and lateral-directional dynamics for maneuvers at angles of attack ranging u=20°through $\alpha$=40°. Results of the two estimation processes are also compared with baseline wind tunnel estimates whenever possible.

• International Journal of Highway Engineering
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• v.15 no.6
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• pp.79-91
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• 2013

PURPOSES : The methods of measuring the sound from the noise source are Pass-by method and NCPX (Noble Close Proximity) method. These measuring methods were used to determine the linkage of TAPL (Total Acoustic Pressure Level) and SPL (Sound Pressure Level) in terms of frequencies. METHODS : The frequency analysis methods are DFT (Discrete Fourier Transform) and FFT (Fast Fourier Transform), CPB (Constant Percentage Bandwidth). The CPB analysis was used in this study, based on the 1/3 octave band option configured for the frequency analysis. Furthermore, the regression analysis was used at the condition related to the sound attenuation effect. The MPE (Mean Percentage Error) and RMSE (Root Mean Squared Error) were utilized for calculating the error. RESULTS : From the results of the CPB frequency analysis, the predicted SPL along the frequency has 99.1% maximum precision with the measured SPL, resulting in roughly 1 dB(A) error. The TAPL results have precision by 99.37% with the measured TAPL. The predicted TAPL results at this study by using the SPL prediction model along the frequency have the maximum precision of 98.37% with the vehicle velocity. CONCLUSIONS : The Predicted SPL model along the frequency and the TAPL result by using the predicted SPL model have a high level of accuracy through this study. But the vehicle velocity-TAPL prediction model from the previous study by using the log regression analysis cannot be consistent with the TAPL result by using the predicted SPL model.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.469-473
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• 2010

In this study, combustion tests using liquefying fuels with fast regression rate were performed. The chamber pressure oscillation was analyzed and hazards of combustion instabilities were examined. In case of Liquefying fuel with fast regression rate, the amplitude of chamber pressure oscillation was increased compared to the polymeric fuels. However, the critical combustion instability can hardly occur in liquefying fuel. This is because the rapid change of inner chamber diameter limits the amplification of chamber pressure oscillation. The chamber pressure oscillation due to the large increase of fuel production and the vortex shedding in pre-chamber violently occurs during combustion using single-port axial injector.

• Archives of Pharmacal Research
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• v.11 no.2
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• pp.99-113
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• 1988

Quantitation of ethenzamide, isopropylantipyrine and caffeine takes about 41 hrs by conventional GC method. Quantitation of allylisoprorylacetylurea takes about 40 hrs by conventional UV method. But quantitation of them takes about 6 hrs by DRIFT developing method. Each standard and sample sieved, powdered and acquired DRIFT spectrum. Out of them peak of each component was selected and ratio of each peak to standard peak was acquired, and then linear stepwise multiple regression was performed with these data and concentration. Reflectance value, Kubelka-Munk equation and Inverse-Kubelka-Munk equation were modified by us. Inverse-Kubelka-Munk equation completed the deficit of Kubelka-Munk equation. Correlation coefficients acquired by conventioanl GC and UV against DRIFT were more than 0.95.

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1224-1231
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• 2006

The stability and control derivatives of DURUMI-lI UAV using the flight test are obtained. The flight test data is gathered from the normal flight condition (normal mode) and the flight condition assumed as the right elevator fixed (fault mode). Using real-time parameter estimation techniques, applied to Fourier transform regression method, simulates the aircraft motion. From the result, the fault of control surface is to be detected. In this paper, the results of the real- time parameter estimation techniques are compared with the results of the Advanced Aircraft Analysis (AAA). Using the aerodynamic derivatives, it provides the base line of normal/failure for the control surface by using the on-line parameter estimation of Fourier transform regression. In flight, this approach maybe helpful to detect and isolate the fault of primary control surface. It is explained how to perform the flight condition assumed as the right elevator fixed in the flight test. Also, it is mentioned how to switch between the normal flight condition and the assumed fault flight condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1076-1082
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• 2013

The frequency bands were discovered which maximize the slopes of autocovariances of speech signals in frequency domain to increase the possibility of segregation between speech signals and background noise signal. A speech signal is divided into blocks which include multiples of sampled data, then those blocks are transformed to frequency domain using Fast Fourier Transform(FFT). To find linear equation by Linear Regression, the coefficients of autocovariance within blocks of some frequency band are used. The slope of the linear equation which is called the slope of autocovariance is varied from band to band according to the characteristics of the speech signal. Using speech signals of a man which consist of 200 files, the coefficients of the slopes of autocovariances are analyzed and compared from band to band.