• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.219-225
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• 1998

The parameters in linear models with censored normal responses are usually estimated by the iterative maximum likelihood and least square methods. However, the iterative least square method is simple but hardly has theoretical justification, and the iterative maximum likelihood estimating equations are complicatedly derived. In this paper, we justify these methods via Wedderburn (1974)'s quasi-likelihood approach. This provides an explicit justification for the iterative least square method and also directly the iterative maximum likelihood method for estimating the regression coefficients.

• Journal of applied mathematics & informatics
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• 제5권2호
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• pp.383-392
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• 1998

This paper proposes Generalized Stationary Iterative called GSI method. It is shown that the existing stationary iterative methods are special cases of GSI method. Convergence properties of this method are provided and their numerical experiments for linear systems with symmetric positive definite matrix are also provided.

• Journal of applied mathematics & informatics
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• 제28권5_6호
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• pp.1409-1418
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• 2010

In this paper, we present a preconditioned iterative method for solving linear systems Ax = b, where A is a Z-matrix. We give some comparison theorems to show that the rate of convergence of the new preconditioned iterative method is faster than the rate of convergence of the previous preconditioned iterative method. Finally, we give one numerical example to show that our results are true.

• Journal of applied mathematics & informatics
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• 제29권5_6호
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• pp.1067-1074
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• 2011

In this paper, a preconditioned mixed-type splitting iterative method for solving the linear systems Ax = b is presented, where A is a Z-matrix. Then we also obtain some results to show that the rate of convergence of our method is faster than that of the preconditioned AOR (PAOR) iterative method and preconditioned SOR (PSOR) iterative method. Finally, we give one numerical example to illustrate our results.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.119-136
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• 2007

Block Jacobi and Gauss-Seidel iterative methods are studied for solving $n{\times}n$ fuzzy linear systems. A new splitting method is considered as well. These methods are accompanied with some convergence theorems. Numerical examples are presented to illustrate the theory.

• 한국위성정보통신학회논문지
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• 제8권4호
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• pp.47-52
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• 2013

본 논문에서는, 기존에 연구되었던 간섭정렬 기법들은 주로 레일레이 채널 환경에서 분석이 이뤄졌다. 간섭정렬 기법은 크게 Iterative-method와 Linear-method로 구분되어 지며, Iterative-method는 반복이라는 제약이 있지만 채널정보가 적게 드는 장점이 있다. Linear-method은 광역 채널 정보(global channel info)가 필요하지만 반복에서 자유로우며 비교적 성능이 우수하다. 이 논문에서는 기존의 간섭정렬 기법들을 실외 환경의 레일리에 패이딩 채널과 실내 환경인 IEEE 802.11n 채널에 적용함으로써 성능을 비교하고자 한다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1991년도 춘계공동학술대회 발표논문 및 초록집; 전북대학교, 전주; 26-27 Apr. 1991
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• pp.6-22
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• 1991

This thesis presents a parallel implementation of an iterative method for large scale, sparse linear system and gives result of computational experiments performed on both single transputer and multi transputer parallel computers. To solve linear system, we use conjugate gradient method and develope data storage techinique, data communication scheme. In addition to the explanation of conjugate gradient method, the result of computational experiment is summarized.

• ETRI Journal
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• 제37권3호
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• pp.533-540
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• 2015

In wireless localization, several linear closed-form solution (LCS) methods have been investigated as a direct result of the drawbacks that plague the existing iterative methods, such as the local minimum problem and heavy computational burden. Among the known LCS methods, both the direct solution method and the difference of squared range measurements method are considered in this paper. These LCS methods do not have any of the aforementioned problems that occur in the existing iterative methods. However, each LCS method does have its own individual error property. In this paper, a hybrid LCS method is presented to reduce these errors. The hybrid LCS method integrates the two aforementioned LCS methods by using two check points that give important information on the probability of occurrence of each LCS's individual error. The results of several Monte Carlo simulations show that the proposed method has a good performance. The solutions provided by the proposed method are accurate and reliable. The solutions do not have serious errors such as those that occur in the conventional standalone LCS and iterative methods.

• Nuclear Engineering and Technology
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• 제55권12호
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• pp.4350-4356
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• 2023

Radiation detection systems working at high count rates suffer from the overlapping of their output electric pulses, known as pulse pile-up phenomenon, resulting in spectrum distortion and degradation of the energy resolution. Pulse tail extrapolation is a pile-up correction method which tries to restore the shifted baseline of a piled-up pulse by extrapolating the overlapped part of its preceding pulse. This needs a mathematical model which is almost always nonlinear, fitted usually by a nonlinear least squares (NLS) technique. NLS is an iterative, potentially time-consuming method. The main idea of the present study is to replace the NLS technique by an integration-based non-iterative method (NIM) for pulse tail extrapolation by an exponential model. The idea of linear extrapolation, as another non-iterative method, is also investigated. Analysis of experimental data of a NaI(Tl) radiation detector shows that the proposed non-iterative method is able to provide a corrected spectrum quite similar with the NLS method, with a dramatically reduced computation time and complexity of the algorithm. The linear extrapolation approach suffers from a poor energy resolution and throughput rate in comparison with NIM and NLS techniques, but provides the shortest computation time.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.171-179
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• 2008

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.