• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.715-722
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• 2016

In this paper, by applying the linear least squared error algorithm, we derive an estimation technique for three dimensional target location when a number of radars are used in detecting a target. The proposed technique is then enhanced by combining GPS information and by assigning variable weights to information sources. The enhanced performance of proposed techniques is confirmed via simulation. It is also observed from simulation results that the performance is robust to the uncertainty of information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10A
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• pp.958-964
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• 2007

In this paper, we derive the relationship between the bit error probability (BEP) of maximum a posteriori (MAP) bit detection and the bit minimum mean square error (MMSE), that is, the BEP is greater than a quarter of the bit USE and less than a half of the bit MMSE. By using this result, the lower and upper bounds of the derivative of the mutual information are derived from the BEP and the lower and upper bounds are easily obtained in the multiple-input multiple-output (MIMO) communication systems with the bit-linear linear-dispersion (BLLD) codes in the Gaussian channel.

• Journal of Biosystems Engineering
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• v.23 no.6
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• pp.571-582
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• 1998

The PLS(Partial Least Square) and ANN(Artificial Neural Network) were introduced to develop the soluble solids content prediction model of apples which is followed by making a subsequent selection of photosensor. For the optimal PLS model, number of factors needed for spectrum analysis were increased until the convergence of prediction residual error sum of squares. Analysis has shown that even part of the overall wavelength with no pretreatment may turn out better performing. The best PLS model was found in the 800 to 1,100nm wavelength region without pretreatment of second derivation, having $R^2$=0.9236, bias= -0.0198bx, SEP=0.2527bx for unknown samples. On the other hand, for the ANN model the second derivation led to higher performance. On partial range of 800 to 1,100nm wavelengh region, prediction model with second derivation for unknown samples reached $R^2$=0.9177, SEP=0.2903bx in contrast to $R^2$=0.7507, SEP =0.4622bx without pretreatment.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.43-56
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• 2005

In this study, a robust estimation method for the first-order autocorrelation coefficient in the time series model following AR(l) process with additive outlier(AO) is investigated. We propose the L-type trimmed least squares estimation method using the preliminary estimator (PE) suggested by Rupport and Carroll (1980) in multiple regression model. In addition, using Mallows' weight function in order to down-weight the outlier of X-axis, the bounded-influence PE (BIPE) estimator is obtained and the mean squared error (MSE) performance of various estimators for autocorrelation coefficient are compared using Monte Carlo experiments. From the results of Monte-Carlo study, the efficiency of BIPE(LAD) estimator using the generalized-LAD to preliminary estimator performs well relative to other estimators.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.157-169
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• 2016

We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.16-21
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• 2008

In order to protect the environment from the refrigerant pollution, the $CO_2$ may be regarded as one of the most attractive alternative refrigerants for an automotive air-conditioning system. Control methods for a $CO_2$ system should be different because of $CO_2$'s unique properties as a refrigerant. Especially, the high-side pressure of a $CO_2$ system should be controlled for the effective operation of the system. High-side pressure algorithms, which were composed of the pressure setpoint algorithm and the pressure setpoint reset algorithm, were developed. Pressure setpoint algorithms, by using a neural network and by using a least square method, were developed and compared. Pressure setpoint reset algorithms, by using a fuzzy logic and by using a proportional logic, were also developed and compared. Simulation results showed that a least square method was more useful than a neural network for the pressure setpoint algorithm. And a proportional logic was more practical than a fuzzy logic for the pressure setpoint reset algorithm.

• Journal of Ocean Engineering and Technology
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• v.22 no.3
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• pp.41-46
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• 2008

It is a recent trend for advanced ships and submarines to incorporate composite structures with viscoelastically damping material. Much research has been done on curve-fitting techniquesto identify vibration characteristic parameters such as natural frequencies, modal damping ratios, and mode shapes of the composite structure. In this study, an advanced technique for accurately determining vibration characteristic of a circular cylindrical shell-attached viscoelastically damping material is used, based on a multi-degree of freedom (MDOF) curve-fitting method. First, an initial value is obtained by using a linear least square method. Next, using the initial value, the exact modal parameters of the composite circular cylindrical shell are obtained by using a nonlinear least square method. Results show computation time is greatly decreased and accurate results are obtained by the MDOF curve-fitting method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1681-1685
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• 2014

The visual quality of medical image is an important factor for diagnosis accuracy. Therefore, the methods to improve the quality of medical image have studied. Among them, frequency domain filter is very powerful method to impove the visual quality of image. In this paper, the X-ray medical image using optimized high-pass filter was improved edges. The result image was improved edge and contrast of flat area using optimized high frequency emphasis filter. At last, the result image is to minimize the noise using the minimum mean square error(MMSE) filter. As a result, the proposed method has enhanced contrast and edge of the image in the contrast of existing filters, with the noise canceling effect.

• Geophysics and Geophysical Exploration
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• v.19 no.2
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• pp.91-96
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• 2016

From the Earth's polar motion time series (IERS 08 C04, since 1981), after removal of seasonal variation by band-pass filtering, we acquired Earth's free Eulerian motion (Chandler wobble) time series. By successive least square error fittings on it, we analyzed amplitude and phase variation of Chandler wobble. We attempted to identify any precursory behavior of the pole before large earthquakes but only to fail. Unlike Smylie's conjecture there was no appreciable motion of the Earth's pole detected at around the each times of recent six largest earthquakes of magnitude over 8.5.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.1-11
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• 1995

The parameters in the multiplicative model $Y_{1}={\alpha}_{0}{\prod}^{p}_{k=1}X_{kj}^{{\beta}_K}v_{j}$ are usually estimated by the least squares method after logarithmic transformation, and the least square Estimator of ${\alpha}_{0}$ is known to be biased, i.e., $E(e xp(\hat{\beta}_{0})){\neq}{\alpha}_{0})$. In the present study the unbaised estimators of ${\alpha}_{0}$ are examined(1) by modifying the least squares estimator and (2) by applying the Finney's results. The variances are also compared. In addition it has been observed that multiplicative model can be used to express the relationship beetween rice yield and yield components.