• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.701-707
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• 2000

In this work, we propose to employ magnetostrictive sensors to develop a new non-contacting modal testing method. Specific applications are made in the modal testing of a beam in bending. The role of bias magnetic fields in measuring bending waves is addressed and an approximate analysis to explain the principle to measure bending signals is carried out. The measured modal data by the present method agree well with those by conventional methods using accelerometers.

• 한국항공우주학회지
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• 제43권1호
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• pp.88-95
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• 2015

본 논문에서는 액체 엔진의 혼합비 오차와 추진제 탑재 오차가 성능에 미치는 영향에 대해서 분석하였다. 75톤급 엔진 4개를 클러스터링하는 발사체 모델에 대해 이런 오차에 의해 발생하는 추진제 잔류량을 확률적인 Monte-Carlo 방법을 사용하여 계산하고 잔류량을 최소화할 수 있는 최적 연료 바이어스를 도출하였다. 다른 발사체에 사용된 근사식을 사용한 해석적인 방법과 비교함으로써 얻어진 결과의 타당성을 검토하였다.

• 한국정보통신학회논문지
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• 제17권11호
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• pp.2621-2626
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• 2013

비대칭 이중게이트 MOSFET의 전위분포에 대하여 고찰하였으며 이를 위하여 포아송방정식의 해석학적 해를 구하였다. 대칭 DGMOSFET는 3단자 소자로서 상하단의 게이트단자가 상호 연결되어 있어 상하단 동일한 제어능력을 가지고 있으나 비대칭 DGMOSFET 소자는 4단자 소자로서 상하단 게이트단자의 전류제어능력을 각각 설정할 수 있다는 장점이 있다. 전위분포를 구할 때 포아송방정식을 이용하였으며 도핑분포함수에 가우시안 함수를 적용함으로써 보다 실험값에 근사하게 해석하였다. 비대칭 이중게이트 MOSFET의 게이트 단자전압 및 게이트 산화막 두께 그리고 채널도핑의 변화에 따라 전위분포의 변화를 관찰하였다. 비대칭 DGMOSFET의 전위분포를 관찰한 결과, 게이트단자 전압 및 게이트 산화막 두께 등에 따라 전위분포는 크게 변화하는 것을 알 수 있었다. 특히 게이트 산화막 두께가 증가하는 단자에서 전위분포의 변화가 더욱 크게 나타나고 있었으며 채널도핑이 증가하면 드레인 측보다 소스 측 전위분포가 크게 변화하는 것을 알 수 있었다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.691-694
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• 2013

비대칭 이중게이트 MOSFET의 전위분포에 대하여 고찰하였으며 이를 위하여 포아송방정식의 해석학적 해를 구하였다. 대칭 DGMOSFET는 3단자 소자로서 상하단의 게이트단자가 상호 연결되어 있어 상하단 동일한 제어능력을 가지고 있으나 비대칭 DGMOSFET 소자는 4단자 소자로서 상하단 게이트단자의 전류제어능력을 각각 설정할 수 있다는 장점이 있다. 전위분포를 구할 때 포아송방정식을 이용하였으며 전하분포함수에 가우시안 함수를 적용함으로써 보다 실험값에 근사하게 해석하였다. 비대칭 이중게이트 MOSFET의 게이트 단자전압 및 게이트 산화막 두께 그리고 채널도핑의 변화에 따라 전위분포의 변화를 관찰하였다. 비대칭 DGMOSFET의 전위분포를 관찰한 결과, 게이트단자 전압 및 게이트 산화막 두께 등에 따라 전위분포는 크게 변화하는 것을 알 수 있었다. 특히 게이트 산화막 두께가 증가하는 단자에서 전위분포의 변화가 더욱 크게 나타나고 있었으며 채널도핑이 증가하면 드레인 측보다 소스 측 전위분포가 크게 변화하는 것을 알 수 있었다.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• Journal of the Korean Data and Information Science Society
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• 제28권5호
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• pp.1205-1215
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• 2017

In this paper, we aim to estimate two scale-parameters of exponentiated Pareto distribution (EPD) based on lower record values. Record values arise naturally in many real life applications involving data relating to weather, sport, economics and life testing studies. We calculate the Bayesian estimators for the two parameters of EPD based on lower record values. The Bayes estimators of two parameters for the EPD with lower record values under the squared error loss (SEL), linex loss (LL) and entropy loss (EL) functions are provided. Lindley's approximate method is used to compute these estimators. We compare the Bayesian estimators in the sense of the bias and root mean squared estimates (RMSE).

• 한국정보시스템학회지:정보시스템연구
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• 제29권3호
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• pp.103-117
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• 2020

Purpose Recently, various issues related to toxic comments on web portal sites and SNS are becoming a major social problem. Toxic comments can threaten Internet users in the type of defamation, personal attacks, and invasion of privacy. Over past few years, academia and industry have been conducting research in various ways to solve this problem. The purpose of this study is to develop the deep learning modeling for toxic comments classification. Design/methodology/approach This study analyzed 7,878 internet news comments through CNN classification modeling based on Highway Network and OOV process. Findings The bias and hate expressions of toxic comments were classified into three classes, and achieved 67.49% of the weighted f1 score. In terms of weighted f1 score performance level, this was superior to approximate 50~60% of the previous studies.

• Communications for Statistical Applications and Methods
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• 제17권2호
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• pp.165-181
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• 2010

This paper presents some chain ratio-type estimators for estimating finite population variance using two auxiliary variables in two phase sampling set up. The expressions for biases and mean squared errors of the suggested c1asses of estimators are given. Asymptotic optimum estimators(AOE's) in each class are identified with their approximate mean squared error formulae. The theoretical and empirical properties of the suggested classes of estimators are investigated. In the simulation study, we took a real dataset related to pulmonary disease available on the CD with the book by Rosner, (2005).

• Communications for Statistical Applications and Methods
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• 제30권4호
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• pp.403-410
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• 2023

Two-Phase sampling, which was first introduced by Neyman (1938), has various applications in different forms. Variance estimation for two-phase sampling has been an important research topic because conventional variance estimators used in most softwares are not working. In this paper, we considered a variance estimation for two-phase sampling in which stratified two-stage cluster sampling designs are used in both phases. By defining a conditionally unbiased estimator of an approximate variance estimator, which is calculable when all elements in the first phase sample are observed, we propose an explicit form of variance estimator of the double expanded estimator for a two-phase sample. A small simulation study shows the proposed variance estimator has a negligible bias with small variance. The suggested variance estimator is also applicable to other linear estimators of the population total or mean if appropriate residuals are defined.

• Journal of Microbiology
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• 제37권3호
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• pp.154-158
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• 1999

The arginine residue at position 243 (Arg 243) of the yeast transcription factor, Gcn4p, is invariably conserved among bZIP transcription factors. Using site-directed oligonucleotide saturation mutagenesis involving two-step polymerase chain reaction (PCR) amplification, random mutations were successfully introduced at the codon of 243 in the basic domain of Gcn4p. This mutant library was transformed ito Gcn4p defective yeast strain and selected for the transcriptionally active colonies. All colonies which were transcriptionally active had arginines in the codon 243. In this study, the strand preference by Taq polymerase during mutagenesis was also tested. Oligonucleotides were specially designed to test whether or not the polymerase was preferred using the strand as a template. A population of randomly mutated products were cloned into an appropriate vector and characterized by DNA sequencing analysis. Saturation mutagenesis which was performed efficiently by this method revealed a strong bias in terms of strand preference of Taq polymerase by an approximate ratio of 3 to 1 in this study.