• Journal of Integrative Natural Science
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• v.6 no.4
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• pp.251-255
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• 2013

For the mean vector of a p-variate normal distribution ($p{\geq}4$), the optimal estimation within the class of modified James-Stein type decision rules under the quadratic loss is given when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}{\theta}-\bar{\theta}1{\parallel}$ it known.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.750-753
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• 1998

This paper gives a simple parameterization of all stable unbiased filters to solve the suboptimal mixed $H_2/H_{\infty}$ filtering problem. Using the central filter, mixed $H_2/H_{\infty}$ filter is designed which minimizes the upper bound for the $H_2$ norm of the transfer matrix from a white noise to the estimation error subject to an $H_{\infty}$ norm constraint on the transfer matrix from an energy-bounded noise to the estimation error. The problem of finding suitable estimator gain can be converted into a convex optimization problem involving linear matrix inequalities.

• Journal of Radiation Protection and Research
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• v.41 no.3
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• pp.291-300
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• 2016

Background: It is necessary to assess radiation dose to workers due to inhalation of airborne particulates containing naturally occurring radioactive materials (NORM) to ensure radiological safety required by the Natural Radiation Safety Management Act. The objective of this study is to develop an internal dose assessment procedure for workers at industries using raw materials containing natural radionuclides. Materials and Methods: The dose assessment procedure was developed based on harmonization, accuracy, and proportionality. The procedure includes determination of dose assessment necessity, preliminary dose estimation, airborne particulate sampling and characterization, and detailed assessment of radiation dose. Results and Discussion: The developed dose assessment procedure is as follows. Radioactivity concentration criteria to determine dose assessment necessity are $10Bq{\cdot}g^{-1}$ for $^{40}K$ and $1Bq{\cdot}g^{-1}$ for the other natural radionuclides. The preliminary dose estimation is performed using annual limit on intake (ALI). The estimated doses are classified into 3 groups ( < 0.1 mSv, 0.1-0.3 mSv, and > 0.3 mSv). Air sampling methods are determined based on the dose estimates. Detailed dose assessment is performed using air sampling and particulate characterization. The final dose results are classified into 4 different levels ( < 0.1 mSv, 0.1-0.3 mSv, 0.3-1 mSv, and > 1 mSv). Proper radiation protection measures are suggested according to the dose level. The developed dose assessment procedure was applied for NORM industries in Korea, including coal combustion, phosphate processing, and monazite handing facilities. Conclusion: The developed procedure provides consistent dose assessment results and contributes to the establishment of optimization of radiological protection in NORM industries.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.105-113
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• 2002

The statistical analysis of incomplete data sometimes requires handling of incomplete observations. Towards this end, each case with some missing values generally should be deleted, namely, resulting in only use of non-missing cases. EM algorithm(Dempster et al., 1977) which involves prediction and estimation steps is a general method among others. In this article, we use the free software NORM developed for multiple imputation, which uses DA(Data Augmentation) algorithm in its imputation, and evaluate its efficiency through a numerical example.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.50-52
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• 2010

통신 시스템의 성능을 향상시키는 핵심 문제 중에 하나인 채널을 추정하는 문제는 다양한 분야에서 연구되고 있다. 채널의 sparse한 특징으로 인해 기존의 linear square나 minimum mean square error보다 발전된 $l_1$-norm minimization 방법 등이 많이 연구되고 있다. 이에 본 논문은 sparse한 채널의 특징과 천천히 변화하는 채널환경 특징을 이용하여 기존의 방법에 비해 더 높은 성능의 채널 추정 기법을 연구한다. 천천히 변화하는 채널환경의 특징으로 인해 이전 채널 정보를 현재 채널 추정에 사용할 수 있고 sparse한 채널의 특징으로 $l_1$-norm minimization을 사용할 수 있다. 이러한 두 가지의 정보를 이용하여 weighted $l_1$-norm minimization 이용한 support detection후 MMSE를 이용한 채널 추정기법을 연구한다.

• Honam Mathematical Journal
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• v.25 no.1
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• pp.95-115
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• 2003

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}(p{\geq}4)$ under the quadratic loss, based on a sample $X_1,\;{\cdots}X_n$. We find an optimal decision rule within the class of Lindley type decision rules which shrink the usual one toward the mean of observations when the underlying distribution is that of a variance mixture of normals and when the norm $||{\theta}-{\bar{\theta}}1||$ is known, where ${\bar{\theta}}=(1/p)\sum_{i=1}^p{\theta}_i$ and 1 is the column vector of ones. When the norm is restricted to a known interval, typically no optimal Lindley type rule exists but we characterize a minimal complete class within the class of Lindley type decision rules. We also characterize the subclass of Lindley type decision rules that dominate the sample mean.

• Journal of the Korean Mathematical Society
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• v.54 no.5
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• pp.1557-1571
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• 2017

The L-curve method is a parametric plot of interrelation between the residual norm of the least squares problem and the solution norm. However, the L-curve method may be hard to apply to the total least squares problem due to its no closed form solution of the regularized total least squares problems. Thus the sequence of the solution norm under the fixed regularization parameter and its corresponding residual need to be found with an efficient manner. In this paper, we suggest an efficient algorithm to find the sequence of the solutions and its residual in order to plot the L-curve for the total least squares problems. In the numerical experiments, we present that the proposed algorithm successfully and efficiently plots fairly 'L' like shape for some practical regularized total least squares problems.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.5
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• pp.39-48
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• 2009

Many super-resolution reconstruction algorithms have been proposed since it was the first proposed in 1984. The spatial domain approach of the super-resolution reconstruction methods is accomplished by mapping the low resolution image pixels into the high resolution image pixels. Generally, a super-resolution reconstruction algorithm by using the spatial domain approach has the noise problem because the low resolution images have different noise component, different PSF, and distortion, etc. In this paper, we proposed the new super-resolution reconstruction method that uses the L1 norm to minimize noise source and also uses the Huber norm to preserve edges of image. The proposed algorithm obtained the higher image quality of the result high resolution image comparing with other algorithms by experiment.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.127-135
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• 2024

The purpose of this paper is to establish the relationship between compression wave velocity and porosity in unsaturated soil using a deep neural network (DNN) algorithm. Input parameters were examined using the error norm method to assess their impact on porosity. Compression wave velocity was conclusively found to have the most significant influence on porosity estimation. These parameters were derived through both field and laboratory experiments using a total of 266 numerical data points. The application of the DNN was evaluated by calculating the mean squared error loss for each iteration, which converged to nearly zero in the initial stages. The predicted porosity was analyzed by splitting the data into training and validation sets. Compared with actual data, the coefficients of determination were exceptionally high at 0.97 and 0.98, respectively. This study introduces a methodology for predicting dependent variables through error norm analysis by disregarding fewer sensitive factors and focusing on those with greater influence.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.123-126
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• 1992

A new class of data transformation matri is introduced for estimation of angles of arrivals by the rank reduction of multiple wideband sources. The proposed unitary focusing matri minimizes the average of the squared norm of focusing error over the angles of interest without a priori knowledge of source locations. The merit that result as a consequence is a lower resolution threshold. These matrices can be applied to the case of the multigroup sources. Simulations and the comparison of statistical performance are compared with the algorithms (especially, spatial resampling method) which does not require the pre-estimation.