• East Asian mathematical journal
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• v.5 no.1
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• pp.95-110
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• 1989

In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

• Journal of the Korean Geophysical Society
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• v.4 no.3
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• pp.175-182
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• 2001

We consider the problem of determining smoothing parameters of Gibbs priors for Bayesian methods used in the medical imaging application of emission tomographic reconstruction. We address a simple smoothing prior (membrane) whose global hyperparameter (the smoothing parameter) controls the bias/variance tradeoff of the solution. We base our maximum-likelihood (ML) estimates of hyperparameters on observed training data, and argue the motivation for this approach. Good results are obtained with a simple ML estimate of the smoothing parameter for the membrane prior.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.2 s.21
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• pp.49-57
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• 2005

The target localization using the line arrays buried in the seabed is a difficult problem due to the complex sea bottom characteristics and need to compensate the wave propagation effect to localize the target accurately Sound speed mismatch in the seabed causes a bias in the target bearing estimation and induces the localization error. In this paper we describe a target localization method with improved accuracy of target bearing and localization by calibration the sound speed in the seabed. The proposed algorithm is verified through the ocean data.

• Journal of the Korean institute of surface engineering
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• v.48 no.3
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• pp.87-92
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• 2015

Carbon thin films were deposited on STS 316L sheets by inductively coupled plasma enhanced magnetron sputtering with or without substrate bias voltage. Typical Raman spectrum for amorphous diamond-like carbon (DLC) was obtained, and the interfacial contact resistance (ICR) was measured to show its conductive nature. The electrochemical impedance spectroscopy (EIS) was used to investigate the corrosion mechanism of the carbon coating under the polymer electrolyte membrane fuel cell (PEMFC) condition. According to the pore-corrosion mechanism, the electrolyte penetrates the carbon coating through the pores and reacts with the substrate. As the substrate corrosion proceeds, the pore enlargement occurs and the surface area of the substrate exposed to the electrolyte. Applicability of the carbon coating for the PEMFC bipolar plate was evaluated by potentiodynamic polarization experiments. Finally, an adhesion problem was briefly considered.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.970-973
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• 1996

In this paper, Modified GPS Kalman filter algorithms which allow user to estimate its position when the number of visible GPS satellites becomes less than four are presented. They are derived using the previous estimation of altitude and clock bias. Thus, it is possible to estimate 3-dimensional user position even when only two GPS satellites are visible. The algorithms are ideally suited to car navigation in urban areas where lack of GPS visibility is the major problem because of the frequent blockage of the GPS signals by tall buildings and other structures. Simulation results in this paper show that modified GPS Kalman filter provide better performances than a general GPS Kalman filter or any other instantaneous GPS solution algorithm, especially in the case which the number of visible GPS satellites becomes less than four.

• Transactions on Electrical and Electronic Materials
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• v.7 no.4
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• pp.180-183
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• 2006

We report for the first time two-bit operational characteristics of a high-density NOR-type polysilicon-oxide-nitride-oxide-silicon (SONOS) array with common source line (CSL). An undesired disturbance, especially drain disturbance, in the NOR array with CSL comes from the two-bit-per-cell operation. To solve this problem, we propose an efficient bulk-biased programming technique. In this technique, a bulk bias is additionally applied to the substrate of memory cell for decreasing the electric field between nitride layer and drain region. The proposed programming technique shows free of drain disturbance characteristics. As a result, we have accomplished reliable two-bit SONOS array by employing the proposed programming technique.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.23-25
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• 2005

Infrared bolometer sensor's variation is detected by voltage drop between reference resistor and bolometer resistor in this architecture. One of the serious problems in this architecture is that these resistors value has a process variation. So common-mode level could be different from expectation in room temperature. Different common-mode level could lead to wrong output at the end of readout circuit. We suggest useful method to solve this problem. Difference correction using capacitor has reduced CM level difference to 86% for 1 $M\Omega$. bolometer and reference resistor's 10% variation.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.329-339
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• 1993

Kernel regression is a nonparametric regression technique which requires only differentiability of the true function. If one wants to use the kernel regression technique to produce smooth estimates of a curve over a finite interval, one can realize that there exist distinct boundary problems that detract from the global performance of the estimator. This paper develops a kernel to handle boundary problem. In order to develop the boundary kernel, a generalized jacknife method by Gray and Schucany (1972) is adapted. Also, it will be shown that the boundary kernel has the same order of convergence rate as non-boundary.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.445-462
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• 2015

This article deals with the problem of estimation of the population mean in presence of multi-auxiliary information in two occasion rotation sampling. A multivariate exponential ratio type estimator has been proposed to estimate population mean at current (second) occasion using information on p-additional auxiliary variates which are positively correlated to study variates. The theoretical properties of the proposed estimator are investigated along with the discussion of optimum replacement strategies. The worthiness of proposed estimator has been justified by comparing it to well-known recent estimators that exist in the literature of rotation sampling. Theoretical results are justified through empirical investigations and a detailed study has been done by taking different choices of the correlation coefficients. A simulation study has been conducted to show the practicability of the proposed estimator.

• The Journal of Engineering Research
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• v.4 no.1
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• pp.47-54
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• 2002

We consider the problem of determining smoothing parameters of Gibbs priors for Bayesian methods used in the medical imaging application of emission tomographic reconstruction. We address a simple smoothing prior (membrane) whose global hyperparameter (the smoothing parameter) controls the bias/variance tradeoff of the solution. We base our maximum-likelihood(ML) estimates of hyperparameters on observed training data, and argue the motivation for this approach. Good results are obtained with a simple ML estimate of the smoothing parameter for the membrane prior.