• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.1
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• pp.163-170
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• 1996

An errors-in-variables model (EVM) differs from the classical regression model in that in the former the independent variable is also subject to error. This paper shows that to assess the applicability of the ordinary least squares (OLS) estimation procedure to the EVM, the relative dispersion of the independent variable to its error variance must be also considered in addition to Mandel's criterion. The effect of physically reducing the variance of errors in the independent variable on the performance of the OLS slope estimator is also discussed.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.9
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• pp.660-669
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• 1987

Routh approximation method is the most computationally attractive. But this method may cause time-response error because this method does not match the time-response directly. In this paper a new mixed method for obtaining stable reduced-order models for high-order continuous-time systems is proposed. It makes use of the advantages of the Routh approximation method and the Minimization of Integral Squared Error(MISE) criterion approach. In this mixed method the characteristic polynomial of the reduced-order model is first obtained from that of original system by using the Auxiliary Denominator Polynomial(ADP). The numerator polynomial is then determined so as to minimize the intergral squared-error of unit step responses. The advantages of the propsed method are that the reduced models are always stable if the original system are stable and the frequency domain and time domain characteristic of the original system will be preserved in the reduced models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8A
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• pp.760-767
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• 2010

For complex channel blind equalization, this study presents the performance and characteristics of two complex blind information theoretic learning algorithms (ITL) which are based on minimization of Euclidian distance (ED) between probability density functions compared to constant modulus algorithm which is based on mean squared error (MSE) criterion. The complex-valued ED algorithm employing constant modulus error and the complex-valued ED algorithm using a self-generated symbol set are analyzed to have the fact that the cost function of the latter forces the output signal to have correct symbol values and compensate amplitude and phase distortion simultaneously without any phase compensation process. Simulation results through MSE convergence and constellation comparison for severely distorted complex channels show significantly enhanced performance of symbol-point concentration with no phase rotation.

• International journal of advanced smart convergence
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• v.2 no.1
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• pp.21-23
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• 2013

The antenna pattern in this case has a main beam pointed in the desired signal direction, and has a null in the direction of the interference.The conventional antenna pattern concepts of beam width, side lobes, and main beams are not used, as the antenna weights are designed to achieve a set performance criterion such as maximization of the output SNR.A new direction of arrival estimation method using effects of model errors and sensitivity analysis is proposed. Two subspaces are used to form a signal space whose phase shift between the reference signal and its effects of model error signal. Through simulation, the performance showed that the proposed method leads to increased resolution and improved accuracy of DOA estimation relative to those achieved with existing method. Since a desired signal is obtained after interference rejection through correction effects of model error, the effect of channel interference on the estimation is significantly reduced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.400-406
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• 2006

Knowledge of camera parameters, such as position, orientation and focal length, is essential to 3D information recovery or virtual object insertion. This paper analyzes the error sensitivity of focal length due to position error of feature points which are employed for self-calibration. We verify the dependency of the focal length on the distance from the principal point to feature points with simulations, and propose a criterion for feature selection to reduce the error sensitivity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.3
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• pp.587-599
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• 1996

In general fractal imge compression, each range block is approximated by a contractive transform of the matching domain block under the mean squared error criterion. In this paper, a distortion measure reflecting the properties of human visual system is defined and applied to a fractal image compression. the perceptual distortion measure is obtained by multiplying the mean square error and the noise sensitivity modeled by using the background brightness and spatial masking. In order to compare the performance of the mean squared error and perceptual distortion measure, a simulation is carried out by using the 512*512 Lena and papper gray image. Compared to the results, 6%-10% compression ratio improvements under improvements under the same image quality are achieved in the perceptual distortion measure.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.45-57
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• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10A
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• pp.980-986
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• 2008

Spatial multiuser access is investigated for the reverse link of multiuser multiple-input multiple-output (MIMO) systems. In particular, we consider two alternative a aches to spatial multiuser access that adopt the same detection algorithm at the base station: one is a closed-loop approach based on singular value decomposition (SVD) of the channel matrix, whereas the other is an open-loop approach based in space-time block coding (STBC). We develop multiuser detection algorithms for these two spatial multiuser access schemes based on the minimum mean square error (MMSE) criterion. Then, we compare the bit error rate (BER) performance of the two schemes and a single-user MIMO scheme. Interestingly, it is found that the STBC approach can provide much better BER performance than the SVD approach as well as than a single-user MIMO scheme.

• The Mathematical Education
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• v.42 no.3
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• pp.419-423
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• 2003

The main purpose of this paper is to investigate effects of skewness and kurtosis on the one-sample test. We have found that type I error brought about a little bit change which is ignorable in relation to kurtosis. Also the change of type I error was completely based on skewness under the same size of the sample. We conclude that using t-test is more similar to robust than using z-test. In introductory statistics classes where data analysis includes techniques for detecting skewness, we recommend the t-test when skewness is smaller than the value 1 to the one-sample test for a mean when the variances is unknown using the probability of a type I error as the criterion of interest.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.