• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.123-134
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• 2000

In the survey, it is very hard to get the complete response. Because the respondents tend to refuse to the questionnaire with something like incomes of the individual or may not be at home in the survey time. These nonresponses are classified into two groups as the item nonresponse and the unit nonresponse. When the nonresponse happen to the special item of the questionnaire, it is caned item nonresponse. On the other hand the unit nonresponse occurs to the totally missing in questionnaire. In this paper, we only consider to the unit nonresponse situation. We propose that the optimal estimator which is minimized the variance of the estimator under a fixed cost function for the survey and response.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.209-217
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• 1993

The multicollinearity problem in a multiple linear regression model may present deleterious effects on predictions. Thus, its is desirable to consider the optimal fractions with respect to the unbiased estimate of the mean squares errors of the predicted values. Interstingly, the optimal fractions can be also illuminated by the Bayesian inerpretation of the general James-Stein estimators.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.6
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• pp.57-63
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• 1998

An adaptive control in the robotic GMA welding is employed to monitor information about weld characteristics and process parameters as well as to modify those parameters to hold weld quality within acceptable limits. Typical characteristics are the bead geometry, composition, microstructure, appearance, and process parameters which govern the quality of the final weld. The main objectives of this thesis are to realize the mapping characteristics of bead width through learning. After learning, the neural estimation can estimate the bead width desired form the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead width with reasonable accuracy and guarantee the uniform weld quality.

• ETRI Journal
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• v.33 no.1
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• pp.27-31
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• 2011

This paper addresses estimating the frequency of a cisoid in the presence of white Gaussian noise, which has numerous applications in communications, radar, sonar, and instrumentation and measurement. Due to the nonlinear nature of the frequency estimation problem, there is threshold effect, that is, large error estimates or outliers will occur at sufficiently low signal-to-noise ratio (SNR) conditions. Utilizing the ideas of averaging to increase SNR and weighted linear prediction, an optimal frequency estimator with smaller threshold SNR is developed. Computer simulations are included to compare its mean square error performance with that of the maximum likelihood (ML) estimator, improved weighted phase averager, generalized weighted linear predictor, and single weighted sample correlator as well as Cramer-Rao lower bound. In particular, with smaller computational requirement, the proposed estimator can achieve the same threshold and estimation performance of the ML method.

• Structural Engineering and Mechanics
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• v.74 no.4
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• pp.547-557
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• 2020

In this paper, an automatic adaptive mesh refinement procedure is presented for two-dimensional problems on the basis of a new probabilistic error estimator. First-order perturbation theory is employed to determine the lower and upper bounds of the structural displacements and stresses considering uncertainties in geometric sizes, material properties and loading conditions. A new probabilistic error estimator is proposed to reduce the mesh dependency of the responses dispersion. The suggested error estimator neglects the refinement at the critical points with stress concentration. Therefore, the proposed strategy is combined with the classic adaptive mesh refinement to achieve an optimal mesh refined properly in regions with either high gradients or high dispersion of the responses. Several numerical examples are illustrated to demonstrate the efficiency, accuracy and robustness of the proposed computational algorithm and the results are compared with the classic adaptive mesh refinement strategy described in the literature.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.12
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• pp.971-982
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• 1989

An integrating position estimation algorithm has been developed for the navigation system of a free-ranging AGV system. The navigation system focused in this research work consists of redundant wheel encoders for the relative position measurement and a vision sensor for the absolute position measurement. A maximum likelihood method and an extended Kalman filter are implemented for enhancing the performance of the position estimator. The maximum likelihood estimator processes noisy, redundant wheel encoder measurements and yields efficient estimates for the AGV motion between each sampling interval. The extended Kalman filter fuses inharmonious positional data from the deadreckoner and the vision sensor and computes the optimal position estimate. The simulation results show that the proposed position estimator solves a generalized estimation problem for locating the vehicle accurately in space.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.47-53
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• 2012

In cellular geo-location systems, positioning performance is influenced by hearability of receivers. Hearability can be enhanced by using long integration at the receiver. When unknown residual frequency remains in baseband signals, however, the coherent integration loss increases as the residual frequency becomes larger. Consequently, length of coherent integration is determined by the residual frequency. By precise estimation and compensation of the residual frequency, integration length can be enlarged. This paper presents a residual frequency estimator for WiBro geo-location and analyzes its performance in multipath environment. By computer simulation, an optimal receiver structure to enhance the hearability of WiBro geo-location is proposed.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.49-60
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• 2005

In this paper we introduce a nonparametric method for estimating the probability density function of detection distances in line transect sampling. The estimator is obtained using a frequency histogram density estimation method. The asymptotic properties of the proposed estimator are derived and compared with those of the kernel estimator under the assumption that the data collected satisfy the shoulder condition. We found that the asymptotic mean square error (AMSE) of the two estimators have about the same convergence rate. The formula for the optimal histogram bin width is derived which minimizes AMSE. Moreover, the performances of the corresponding k-nearest-neighbor estimators are studied through simulation techniques. In the absence of our knowledge whether the shoulder condition is valid or not a new semi-parametric model is suggested to fit the line transect data. The performances of the proposed two estimators are studied and compared with some existing nonparametric and semiparametric estimators using simulation techniques. The results demonstrate the superiority of the new estimators in most cases considered.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.685-696
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• 2014

In this paper, we develop Bayesian inference of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation in the presence of auxiliary information under the balanced loss function. We compare the performance of the optimal Bayes estimator under the balanced loss function with ones of the classical ratio estimator and the usual Bayes estimator in terms of the posterior expected losses, risks and Bayes risks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.445-451
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• 2002

In this paper, we propose a new tracking algorighm that achieves good tracking performance in manuevering targets while capping the computation load to "low". Kalman Filler (KF) is generally known to be poor in tracking maneuvering targets. IMM, on the other hand, compensates the weakness inherent in the mundane KF and is considered as a promising alternative for tracking maneuvering targets. However, IMM suffers from substantially increased computational load as the number of models increases. To remedy this problem, we propose a new method focused to reducing the computational load and attaining the desirable tracking performance at least as good that of IMM. It is achieved by essentially adopting the structure of IMM and injecting Optimal Two-Stage Kalman Estimator (OTSKE). The representative simulation shows a reduction in computational load with the proposed OTSKE but further reduction is shown achieved (by about 58%) with the Interacting Acceleration Compenstation (IAC)-OTSKE approach. approach.