• 한국산업정보학회논문지
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• 제12권1호
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• pp.80-86
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• 2007

The concept of the coverage has been played an important role in the area of reliability evaluation of a system. The widely used measures of reliability include the m time between failures, the availability and so on. In this paper, we propose an estimator of the coverage probability in a redundant system with a control unit and investigate some moments of the proposed estimator. And assuming exponential distribution of all units, we conduct a simulation study for calculating the estimates of the coverage probability and its confidence bounds. An example of evaluating the availability of an optical transportation system is illustrated.

• ETRI Journal
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• 제28권6호
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• pp.822-825
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• 2006

Based on superimposed training methods, a novel time-varying multipath channel estimation scheme is proposed for orthogonal frequency division multiplexing systems. We first develop a linear least square channel estimator, and meanwhile find the optimal superimposed sequences with respect to the channel estimates' mean square error. Next, a low-rank approximated channel estimator is obtained by using the singular value decomposition. As demonstrated in simulations, the proposed scheme achieves not only better performance but also higher bandwidth efficiency than the conventional pilot-aided approach.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2013년도 전력전자학술대회 논문집
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• pp.269-270
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• 2013

This paper proposes the estimation algorithm to find the rotor time constant of induction motors, which is very important for induction motor drive system. This strategy is based on flux estimator theory. Proposed method has been demonstrated through simulation using MATLAB SIMULINK.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제2권2호
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• pp.83-89
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• 1995

A Bayes estimator of the survival distribution function due to Susarla and Van Ryzin(1976) is used to estimate the mth moment of a survival time on the basis of censored observations in a random censorship model. Asymptotic normality of the estimator is proved using the functional version of the delta method.

• 한국조사연구학회:학술대회논문집
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• 한국조사연구학회 2002년도 추계학술대회 발표논문집
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• pp.255-274
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• 2002

We classify rotation sampling designs into two classes. The first class replaces sample units within the same rotation group while the second class replaces sample units between different rotation groups. The first class is specified by the three-way balanced design which is a multi-level version of previous balanced designs. We introduce an extended generalized composite estimator (EGCE) and derive its variance and mean squared error for each of the two classes of design, cooperating two types of correlations and three types of biases. Unbiased estimators are derived for difference between interview time biases, between recall time biases, and between rotation group biases. Using the variance and mean squared error, since any rotation design belongs to one of the two classes and the EGCE is a most general estimator for rotation design, we evaluate the efficiency of EGCE to simple weighted estimator and the effects of levels, design gaps, and rotation patterns on variance and mean squared error.

• 대한기계학회논문집
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• 제17권2호
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• pp.393-406
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• 1993

In resistance spot welding process, size of molten nuggest have been utilized to assess the integrity of the weld quality. However real-time monitoring of the nugget size is an extremely difficult problem. This paper describes the design of an artificial neural networks(ANN) estimator to predict the nugget size for on-line use of weld quality monitoring. The main task of the ANN estimator is to realize the mapping characteristics from the sampled dynamic resistance signal to the actual negget size through training. The structure of the ANN estimator including the number of hidden layers and nodes in a layer is determined by an estimation error analysis. A series of welding experiments are performed to assess the performance of the ANN estimator. The results are quite promissing in that real-time estimation of the invisible nugget size can be achieved by analyzing the dynamic resistance signal without any conventional destructive testing of welds.

• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.225-233
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• 2000

수명시험에서 시험에 장기간 노출된 대상 부품이나 실험 대상자의 수명은 관측되는 경우보다 관측중단이 일어나기가 쉽다. 이와 같은 경우에 임의중단모형에서 생존함수 추정량으로 흔히 이용되는 Kaplan과 Meier의 추정량은 수명분포의 오른쪽 꼬리부분에서 심각한 편의가 발생된다. 이러한 문제점에 대한 대안으로 정상적으로 관측된 최장수명보다 큰 관측중단수명이 많은 극단적인 오른쪽 관측중단모형에서 새로운 비모수적 생존함수 추정량을 제안하고 그 특성을 몬테칼로 모의실험을 통하여 기존의 추정량과 비교 분석하였다.

• 로봇학회논문지
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• 제16권2호
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• pp.130-136
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• 2021

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

• Communications for Statistical Applications and Methods
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• 제24권3호
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• pp.193-209
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• 2017

The power Lindley distribution with some of its properties is considered in this article. Maximum likelihood, least squares, maximum product spacings, and Bayes estimators are proposed to estimate all the unknown parameters of the power Lindley distribution. Lindley's approximation and Markov chain Monte Carlo techniques are utilized for Bayesian calculations since posterior distribution cannot be reduced to standard distribution. The performances of the proposed estimators are compared based on simulated samples. The waiting times of research articles to be accepted in statistical journals are fitted to the power Lindley distribution with other competing distributions. Chi-square statistic, Kolmogorov-Smirnov statistic, Akaike information criterion and Bayesian information criterion are used to access goodness-of-fit. It was found that the power Lindley distribution gives a better fit for the data than other distributions.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.