• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1992년도 특별강연 및 추계학술발표 개요집
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• pp.91-95
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• 1992

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitive analyses of sampled process variables have been attempted to predict nugget size. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. The prediction by the neural network is in good agreement with the actual nugget size for resistance spot welding of galvanized steel. The results are quite promising in that the quantitative estimation of the invisible nugget size can be achieved without conventional destructive testing of welds.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.285-294
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• 2005

This paper deals with the comparison of parameter estimation methods in a 3-parameter Kappa distribution which is sometimes used in flood frequency analysis. Method of moment estimation(MME), L-moment estimation(L-ME), and maximum likelihood estimation(MLE) are applied to estimate three parameters. The performance of these methods are compared by Monte-carlo simulations. Especially for computing MME and L-ME, three dimensional nonlinear equations are simplified to one dimensional equation which is calculated by the Newton-Raphson iteration under constraint. Based on the criterion of the mean squared error, L-ME (or MME) is recommended to use for small sample size( n$\le$100) while MLE is good for large sample size.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Journal of Electrical Engineering and information Science
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• 제1권2호
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• pp.119-124
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• 1996

Variable step size LMS(VS-LMS) algorithms improve performance of LMS algorithm by means of varying the step size. This paper presents a new VS-LMS algorithm using normalized absolute estimation error. Normalizing the estimation error to the expected valus of the desired signal, we determined the step size using the relative size of estimation error, Because parameters and computational load are less, our algorithm is easy to implement in hardware. The performance of the proposed algorithm is analyzed theoretically and estimated through simulations. Based on the theoretical analysis and computer simulations, the proposed algorithm is shown to be effective compared to conventional VS-LMS algorithms.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2012년 추계학술대회
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• pp.461-471
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• 2012

Most useful statistical techniques in six sigma DMAIC are hypothesis testing and interval estimation. So this paper reviews and derives sample size formula by considering significance level, power of detectability and effect difference. The quality practioners can effectively interpret the practical and statistical significance with the rational sample sizing.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제12권3호
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• pp.153-160
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• 2008

A size-biased Poisson distribution is defined. Its characterization by using a recurrence relation for first order negative moment of the distribution is obtained. Different estimation methods for the parameter of the model are also discussed. R-Software has been used for making a comparison among the three different estimation methods.

• Journal of Welding and Joining
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• 제11권2호
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• pp.27-41
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• 1993

The estimation of nugget sizes was attempted by utilizing the artificial neural networks method. Artificial neural networks is a highly simplified model of the biological nervous system. Artificial neural networks is composed of a large number of elemental processors connected like biological neurons. Although the elemental processors have only simple computation functions, because they are connected massively, they can describe any complex functional relationship between an input-output pair in an autonomous manner. The electrode head movement signal, which is a good indicator of corresponding nugget size was determined by measuring the each test specimen. The sampled electrode movement data and the corresponding nugget sizes were fed into the artificial neural networks as input-output pairs to train the networks. In the training phase for the networks, the artificial neural networks constructs a fuctional relationship between the input-output pairs autonomusly by adjusting the set of weights. In the production(estimation) phase when new inputs are sampled and presented, the artificial neural networks produces appropriate outputs(the estimates of the nugget size) based upon the transfer characteristics learned during the training mode. Experimental verification of the proposed estimation method using artificial neural networks was done by actual destructive testing of welds. The predicted result by the artifficial neural networks were found to be in a good agreement with the actual nugget size. The results are quite promising in that the real-time estimation of the invisible nugget size can be achieved by analyzing the process variable without any conventional destructive testing of welds.

• 방송공학회논문지
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• 제19권6호
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• pp.916-924
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• 2014

가변 블록크기를 적용하면 영상의 움직임 추정의 정확도를 향상시킬 수 있다. 그러나, 신축 움직임 추정에는 한계를 갖고 있다. 본 논문에서는 가변 블록크기로 신축 움직임을 추정하는 방법을 제안한다. 제안된 방법은 깊이 카메라로부터 얻어진 깊이 정보로부터 영상의 객체와 배경을 분리하고, 객체에 대해서만 깊이 정보를 이용하여 신축을 적용하며, 배경은 신축을 적용하지 않는다. 또한, 객체영역에는 움직임 추정의 정확도와 발생되는 움직임 벡터를 동시에 고려하여 효율적으로 가변블록 크기 모드가 선택된다. 모의실험을 바탕으로 제안된 방법의 움직임 추정의 정확도와 움직임 벡터수를 측정하였으며, 기존 신축 움직임 추정 방법에 비하여 동일한 움직임 추정의 정확도를 유지하면서 움직임 벡터의 수가 감소함을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제14권12호
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• pp.225-232
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• 2009

소프트웨어 규모 측정에 국제표준(IFPUG, FPA)에 기반한 기능점수 방식은 개발자 관점이 아닌 사용자 관점의 접근이라는 장점으로 널리 활용되고 있다. 그러나 현재의 기능점수 측정 방식은 복잡도 적용에서의 상한이 존재하는 등 문제점을 내포하고 있다. 본 논문에서는 이러한 복잡도 상한의 문제를 극복하고, 회귀식을 내장하고 있어 쉽게 소프트웨어 규모를 측정할 수 있으며, 특히 대형 애플리케이션에도 적용할 수 있는 개선된 기능점수 측정모델을 제시한다. 조직내에서 운영중인 10개 애플리케이션에 적용하여 적합성을 평가한 결과, 제시한 개선 모형이 기존의 FPA(Function Point Analysis) 방식보다 LOC(Line of Code) 규모를 비교하여 상관관계가 더 높은 장점을 확인할 수 있었다.

• 한국통신학회논문지
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• 제35권8B호
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• pp.1211-1216
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• 2010

임베디드 SW는 제품 내장형 SW라는 특징으로 인해 임베디드 SW를 탑재한 제품의 시장 규모가 아닌 임베디드 SW 만의 시장을 명확히 파악한 자료는 거의 없다. 본 논문에서는 임베디드 SW만의 시장규모를 최대한 정확히 알아내기 위해 생산액 규모를 추정하는 방법을 사용하였다. 임베디드 SW 시장규모 추정에 적합한 산업분류체계를 도출하였고 이에 따른 산업 분류별 시장 규모를 추정하였다. 또한, 임베디드 SW의 산업 분류별 비중을 산출하고 최종적으로 임베디드 S/W의 시장 규모를 추정하였다. 임베디드 산업 분류별 SW의 시장 규모를 추정한 결과 산업자동화, 군사, 항공, 우주, 사무자동화 순으로 추정되었고, 임베디드 SW의 부가가치는 약 27조로 나타났다.