• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2001년도 정기학술대회
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• pp.311-319
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• 2001

All indices that are now in use assume normally distributed data, and any use of the indices on non-normal data results in inaccurate capability measurements. Therefore, $C_{s}$ is proposed which extends the most useful index to date, the Pearn-Kotz-Johnson $C_{pmk}$, by not only taking into account that the process mean may not lie midway between the specification limits and incorporating a penalty when the mean deviates from its target, but also incorporating a penalty for skewness. Therefore we propose, a new process capability index $C_{psk}$( WV) applying the weighted variance control charting method for non-normally distributed. The main idea of the weighted variance method(WVM) is to divide a skewed or asymmetric distribution into two normal distribution from its mean to create two new distributions which have the same mean but different standard distributions. In this paper we propose an example, a distribution generated from the Johnson family of distributions, to demonstrate how the weighted variance-based process capability indices perform in comparison with another two non-normal methods, namely the Clements and the Wright methods. This example shows that the weighted valiance-based indices are more consistent than the other two methods In terms of sensitivity to departure to the process mean/median from the target value for non-normal process.s.s.s.

• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.353-365
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• 2004

In this paper, we define the weighted U-empirical process for simple linear model and show the weak convergence to a Gaussian process under some conditions. Then we illustrate the usage of our result with examples. In the appendix, we derive the variance of the weighted U-empirical distribution function.

• 한국통신학회논문지
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• 제17권9호
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• pp.909-917
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• 1992

본 논문은 출력 오차 분산(分散)를 갖는 상태공간(狀態空間) 디지털 필터의 실현(實現)을 제안하였다. 이 앨고리즘은 가중함수(加重函數)에 의해서 선형 시불변 시스템내의 가제어성 및 가관측성 gramian을 변화시키는 것이며, 상태공간(常態空間) 계수 변동에 대한 출력 오차 분산(分散)을 줄임으로써 디지털 필터의 성능을 개선할 수 있다. 수치예에서, 본 앨고리즘 구조는 다른 4가지 구조(표준형, 병열형, 통계적감도형, 평형형) 보다 더 적은 출력오차 분산(分散)을 갖는 것을 알았다.

• Communications for Statistical Applications and Methods
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• 제19권1호
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• pp.23-32
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• 2012

본 논문은 유한모집단에서 회귀계수추정량의 근사편향과 근사분산을 다루고 있다. 유한모집단에서 고정크기 포함확률비례표본을 추출하고 이 표본에서 조사된 데이터에 기초하여 회귀계수를 일반최소제곱추정량과 가중최소제곱추정량으로 추정할 때 두 추정량의 편향, 분산 그리고 평균제곱오차의 근사식을 유도하였다. 그리고 두 추정량의 효율을 비교하기 위하여 두 추정량의 분산을 비교하는 필요충분조건을 제시하였다. 또한 수치적인 비교를 위하여 간단한 예제를 소개하였다.

• Journal of the Korean Statistical Society
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• 제18권2호
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• pp.118-124
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• 1989

In the one-way random effect model, we often estimate the variance components by the ANOVA method and then estimate the population mean. Whe there are only two distint group sizes, the conventional mean estimator is represented as a weighted average of two normal means with weights being the function of variance component estimators. In this paper, we will study a method which can compute the exact variance of the mean estimator when we set the negative variance component estimate to zero.

• 한국정보통신학회논문지
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• 제16권8호
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• pp.1773-1778
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• 2012

최근 들어, 디지털 영상처리 장치에 대한 수요가 급격히 증대되면서 영상의 우수한 화질이 요구되고 있다. 그러나 여러 가지 원인에 의해 잡음이 추가되어 영상을 훼손시킨다. 따라서 잡음제거에 대한 필요성이 대두되고 있으며, 잡음제거 기술은 주요한 연구 분야가 되었다. 영상은 AWGN(additive white Gaussian noise)에 의해 많이 훼손되며, 본 논문에서는 AWGN을 제거하기 위해, 에지보호를 위한 개선된 알고리즘을 제안하였다. 제안한 알고리즘은 먼저 공간거리 차이 정보를 고려한 가중치 필터와 적응 가중치 필터로 처리한 결과값의 평균과 마스크내의 분산과 추정된 잡음분산의 관계식에 의해 처리된 값을 합하여, 영상의 최종출력값을 구한다. 따라서 제안한 방법은 우수한 잡음제거 및 에지보존 특성을 나타내었고 영상의 화질을 개선하였다.

• IEIE Transactions on Smart Processing and Computing
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• 제3권5호
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• pp.271-274
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• 2014

Bayesian based Multi-Frame Super-Resolution (MF-SR) has been used as a popular and effective SR model. On the other hand, the texture region is not reconstructed sufficiently because it works on the spatial domain. In this study, the MF-SR method was extended to operate on the frequency domain to improve HF information as much as possible. For this, a spatially weighted bilateral total variation model was proposed as a regularization term for a Bayesian estimation. The experimental results showed that the proposed method can recover the texture region more realistically with reduced noise, compared to conventional methods.

• 대한산업공학회지
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• 제29권2호
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• pp.114-125
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• 2003

This paper proposes multivariate process capability indices (PCIs) for skewed populations using $T^2$rand modified process region approaches. The proposed methods are based on the multivariate version of a weighted standard deviation method which adjusts the variance-covariance matrix of quality characteristics and approximates the probability density function using several multivariate Journal distributions with the adjusted variance-covariance matrix. Performance of the proposed PCIs is investigated using Monte Carlo simulation, and finite sample properties of the estimators are studied by means of relative bias and mean square error.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.565-584
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• 1999

the covariance process of two martingales provides a useful tool to capture the dependence structure for paired censored data. in this paper it is applied to modify the variances of weighted logrank tests in order to take account of dependence between paired subjects. In the process of modification a 'variance correction term' is introduced. Some variance estimators based on separate samples are considered together. Performance of the estimators are compared through simulation studies. Several independence tests for bivariate sruvival date are also proposed which are naturally reduced from the weighted logrank tests accomodating dependence structure. Simulation studies are carried out to compare the independence tests. Both the weighted logrank tests and the independence tests are illustrated by an example.

• 음성과학
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• 제11권1호
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.