• Journal of Electrical Engineering and Technology
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• 제7권4호
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• pp.606-614
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• 2012

A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omnidirectional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2012년도 추계 학술논문 발표대회
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• pp.141-142
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• 2012

Estimating the productivity of labor has relied on the experience and intuition of an estimator, or productivity data, such as standard in practical estimation. However, when new technologies and new construction methods employed in the construction industry, Dependence of a quantity surveyor's intuition is increased. This is because of the lack of a systematic methodology which models, estimates, and measures the labor productivity, Due to the historical productivity data is unavailable. Even thought project history data contains information that is important to predict the performance of a project, It can not provide valuable information to model, estimate, and measure the labor productivity. This is because 1) the absence of the productivity measurement system with consistency, 2) the difficulty of reusability of historical data collected. Therefor, this study suggests a methodology which build a productivity model by measuring the productivity of labor and collecting the historical data systematically. In addition, this methology is applied to develop a productivity model of shop-drawing and manufacturing process using descrete event simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.175-177
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• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.

• 한국조사연구학회:학술대회논문집
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• 한국조사연구학회 2006년도 춘계학술대회 발표논문집
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• pp.131-159
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• 2006

This paper consider multinomial group testing which is concerned with classification each of N given units into one of k disjoint categories. In this paper, we propose exact Bayesian, approximate Bayesian, bootstrap methods for estimating individual category proportions using the multinomial group testing model proposed by Bar-Lev et al (2005). By the comparison of Mcan Squre Error (MSE), it is shown that the exact Bayesian method has a bettor efficiency and consistency than maximum likelihood method. We suggest an approximate Bayesian approach using Markov Chain Monte Carlo (MCMC) for posterior computation. We derive exact credible intervals based on the exact Bayesian estimators and present confidence intervals using the bootstrap and MCMC. These intervals arc shown to often have better coverage properties and similar mean lengths to maximum likelihood method already available. Furthermore the proposed models are illustrated using data from a HIV blooding test study throughout California, 2000.

• 품질경영학회지
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• 제32권4호
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• pp.229-241
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• 2004

Process capability index is used to determine whether a production process is capable of producing items within a specified tolerance. The index $C_{pmk}$ is the third generation process capability index. This index is more powerful than two useful indices $C_p$ and $C_{pk}$. Whether a process distribution is clearly normal or nonnormal, there may be some questions as to which any process index is valid or should even be calculated. As far as we know, yet there is no result for statistical inference with process capability index $C_{pmk}$. However, asymptotic method and bootstrap could be studied for good statistical inference. In this paper, we propose various bootstrap confidence limits for our process capability Index $C_{pmk}$. First, we derive bootstrap asymptotic distribution of plug-in estimator $C_{pmk}$ of our capability index $C_{pmk}$. And then we construct various bootstrap confidence limits of our capability index $C_{pmk}$ for more useful process capability analysis.

• Korean Journal of Mathematics
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• 제21권4호
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• pp.429-437
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• 2013

Let $X_1$, $X_2$, ${\cdots}$, $X_n$ be independent and identically distributed random variables having common exponential density with unknown mean ${\mu}$. In the sequential confidence interval estimation for the exponential hazard rate ${\theta}=1/{\mu}$, when the loss function is strictly convex, the following stopping rule is proposed with the half length d of prescribed confidence interval $I_n$ for the parameter ${\theta}$; ${\tau}$ = smallest integer n such that $n{\geq}z^2_{{\alpha}/2}\hat{\theta}^2/d^2+2$, where $\hat{\theta}=(n-1)\bar{X}{_n}^{-1}/n$ is the minimum risk estimator for ${\theta}$ and $z_{{\alpha}/2}$ is defined by $P({\mid}Z{\mid}{\leq}{\alpha}/2)=1-{\alpha}({\alpha}{\in}(0,1))$ Z ~ N(0, 1). For the confidence intervals $I_n$ which is required to satisfy $P({\theta}{\in}I_n){\geq}1-{\alpha}$. These estimated intervals $I_{\tau}$ have the asymptotic consistency of the sequential procedure; $$\lim_{d{\rightarrow}0}P({\theta}{\in}I_{\tau})=1-{\alpha}$$, where ${\alpha}{\in}(0,1)$ is given.

• 산업경영시스템학회지
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• 제42권1호
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• pp.151-157
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• 2019

This study is about the process capability index (PCI). In this study, we introduce several indices including the index $C_{PR}$ and present the characteristics of the $C_{PR}$ as well as its validity. The difference between the other indices and the $C_{PR}$ is the way we use to estimate the standard deviation. Calculating the index, most indices use sample standard deviation while the index $C_{PR}$ uses range R. The sample standard deviation is generally a better estimator than the range R. But in the case of the panel process, the $C_{PR}$ has more consistency than the other indices at the point of non-conforming ratio which is an important term in quality control. The reason why the $C_{PR}$ using the range has better consistency is explained by introducing the concept of 'flatness ratio'. At least one million cells are present in one panel, so we can't inspect all of them. In estimating the PCI, it is necessary to consider the inspection cost together with the consistency. Even though we want smaller sample size at the point of inspection cost, the small sample size makes the PCI unreliable. There is 'trade off' between the inspection cost and the accuracy of the PCI. Therefore, we should obtain as large a sample size as possible under the allowed inspection cost. In order for $C_{PR}$ to be used throughout the industry, it is necessary to analyze the characteristics of the $C_{PR}$. Because the $C_{PR}$ is a kind of index including subgroup concept, the analysis should be done at the point of sample size of the subgroup. We present numerical analysis results of $C_{PR}$ by the data from the random number generating method. In this study, we also show the difference between the $C_{PR}$ using the range and the $C_P$ which is a representative index using the sample standard deviation. Regression analysis was used for the numerical analysis of the sample data. In addition, residual analysis and equal variance analysis was also conducted.

• 응용통계연구
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• 제34권1호
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• pp.1-8
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• 2021

사회조사에서 추상적 개념은 타당성과 신뢰성이 보장된 측정도구를 사용하여야 한다. 그와 같은 측정도구에 의해 도출된 관측점수는 타당한 관측점수와 편향된 관측점수와 오차로 나눌 수 있으며 편향된 값의 유무가 타당성과 연관되어 있으며 오차값의 유무가 신뢰성과 연관되어 있다. 측정도구가 타당도 및 신뢰도를 만족하는 지를 보기 위한 기법들이 많이 존재한다. 예를 들면 요인분석을 통한 구성타당도, 크론바흐 알파 값에 의한 내적일치도 등을 들 수 있다. 본 연구에서 크론바흐알파값의 계산은 표본을 통해서 도출되는 데 복잡한 표본설계와 무응답이 발생했을 때 크론바흐알파값의 추정법에 대해 살펴본다. 제안된 기법에 대한 모의실험으로 다변량정규분포를 사용하여 기존의 여러 다른 크론바흐알파값의 추정기법과 비교분석한다.

• 응용통계연구
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• 제26권3호
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• pp.431-440
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• 2013

우리는 두꺼운 꼬리를 갖는 분포의 모수를 추정하는 방법론을 연구하였다. 일반적으로 MLE(최대우도 추정량)가 모수추정 방법론중에 가장 많이 사용되는데, 이는 MLE가 점근적 일치성과 정규성 그리고 효율성을 가지고 있기 때문이다. 하지만 MLE가 늘 가장 좋은 추정법은 아니다. 어떤 경우에는 MLE가 존재하지 않을 수도 있고 계산이 안정적이지 않을 수도 있다. 본 논문에서는 비선형 최소제곱추정법을 이용한 모수추정 방법론을 제시하고 그 성능을 MLE와 비교하였다. NLS 추정량은 empirical CDF와 이론적 CDF의 차이의 제곱을 최소화 하는 방법론이다. 본 논문에서는 두꺼운 꼬리를 가지는 다양한 분포하에서 우리가 제안하는 NLS방법론과 MLE와의 성능을 비교하였다. 그 결과, Burr 분포에서 표본의 수가 적을 때 우리의 방법론이 MLE보다 좋은 성능을 보여주었고, Frechet 분포에서도 좋은 결과를 얻을 수 있었다.

• 정신신체의학
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• 제27권2호
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• pp.77-84
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• 2019

연구목적 질병침습도 평가 척도(Illness Intrusiveness Rating Scale)란, 만성 질환을 앓는 환자에서 질병이나 치료가 삶의 질에 얼마나 영향을 미치는지 평가하기 위한 타당도가 입증된 자가보고식 검사이다. 여러 연구를 통하여 만성 질환자에서 요인 모델의 적합도가 보고되었으나, 정신 질환자를 대상으로는 그 요인 타당도가 확인되지 않았다. 본 연구는 불안 및 우울장애가 주요 진단인 정신건강의학과 외래환자 군을 대상으로 한국판 질병침습도 평가 척도(Illness Intrusiveness Rating Scale-Korean version, IIRS-K)의 요인 구조를 확인하고, 요인 타당도를 조사하기 위하여 진행되었다. 방 법 2010년부터 2013년까지 3년간 한양대학교 구리병원 정신건강의학과 외래 초진 환자를 연속적 수집하여 총 307명의 자료를 분석하였다. IIRS-K, Zung 자가평가 우울척도, Zung 자가평가 불안척도를 이용하였다. Cronbach's α계수를 통해 신뢰도를 분석하였고, Varimax 직각회전을 이용한 탐색적 주성분 분석과 최대우도법 중 보수적인 방법을 추정자(standard maximum likelihood estimator)로 확인적 요인분석을 시행하였다. IIRS-K의 하위 요인과 우울, 불안 증상간의 타당도는 Spearman 상관 분석으로 조사하였다. 결 과 IIRS-K의 내적 일치도는 0.9로 높은 신뢰도를 보였다. 13개 문항에서 3개 요인이 추출되었고, 총 분산의 63.2%를 설명하여, 58.3%를 설명한 원본 영문판과 유사한 요인 구조를 보였다. 3요인 분석이 가장 모델에 적합한 것을 확인적 요인분석으로 확인하였으며, 우울 및 불안 증상과도 정적인 상관관계를 보여 수렴 타당도를 입증하였다. 결 론 IIRS-K의 3요인구조는 조용한 취미 활동, 개인 발전 등 일부 문항 구성에서 원본과 차이를 보였고, 가족 관계나 배우자와의 관계, 경제 문제가 한 요인에 추출되는 것이 특징적이었다. 이는 문화적 특징을 반영한 결과라고 볼 수 있다. 이 연구는 정신건강의학과 임상군을 대상으로 IIRS-K의 요인 타당도를 입증한 첫 연구이며, 이를 통해 국내 정신건강의학과 환자의 삶의 질에 사용될 유용한 도구를 제공했다는 의의가 있다.