• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집A
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• pp.956-963
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• 2001

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is predicted using the artificial neural network. The neural network has trained training examples of stress ratio, normalized eccentricity, off-centered direction and stress error using backpropagation loaming process. The prediction results of the error using the trained neural network are good agreement with FE analyzed ones.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권7호
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• pp.423-427
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• 2005

In this paper, we proposed a new ARMA model identification which estimate the parameters to make the current prediction error uncorrelated with the past one. As good properties of the proposed method, we show the uniqueness, consistency of the estimate and asymptotic normality of the estimation error. Via simulation results, we show that the proposed method give good estimates for various systems which have different power spectrum. Moreover, the estimation of gyroscope random errors shows that the proposed method is applicable to the real data.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1994년도 가을 학술발표회 논문집
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• pp.95-102
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• 1994

This paper presents a Cable Stayed Bridge Construction Management System, which consists of Structural System Identification Method (SSIM), Error Sensitivity Analysis and Optimum Error Adjustment & Prediction System. The 1st System Identification Method builds an error influence matrix using the linear superposition of each error modes. The 2nd SSIM also considers the second error mode term, which shows good error factor estimation. The optimal cable adjustment can be accomplished within the allowable range of both cable tension and camber. The Post processor, constituted with Motif and GL library on SGI platform, is useful for monitoring construction stage management by displaying construction data, adjustment and prediction results at each construction step.

• 대한산업공학회지
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• 제39권2호
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• pp.109-118
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• 2013

We propose a method for estimating coefficients of AR (autoregressive) model which named MLPAR (Maximum Likelihood of Pearson system for Auto-Regressive model). In the present method for estimating coefficients of AR model, there is an assumption that residual or error term of the model follows the normal distribution. In common cases, we can observe that the error of AR model does not follow the normal distribution. So the normal assumption will cause decreasing prediction accuracy of AR model. In the paper, we propose the MLPAR which does not assume the normal distribution of error term. The MLPAR estimates coefficients of auto-regressive model and distribution moments of residual by using pearson distribution system and maximum likelihood estimation. Comparing proposed method to auto-regressive model, results are shown to verify improved performance of the MLPAR in terms of prediction accuracy.

• 한국통신학회논문지
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• 제28권2C호
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• pp.181-192
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• 2003

통신 채널에서 블라인드 등화는 전송효율을 저하시키는 훈련신호나 채널의 사전 정보가 필요치 않은 장점 때문에 많은 연구가 진행되어 왔다. 선형예측을 이용한 블라인드 등화는 등화기의 차수 추정 오차에 강인하며 적응 알고리듬을 이용하여 효율적으로 구현할 수 있는 장점이 있다. 하지만 기존의 one-step 선형예측을 이용한 블라인드 등화기는 임의의 결정 지연에 대해서는 구현할 수 없는 단점이 있다. 본 논문에서는 SIMO 채널에서 one-step 순방향 선형예측 필터를 이용하여 임의의 결정 지연을 갖는 블라인드 등화기를 제안한다. 제안한 알고리듬은 순방향 추정 오차를 훈련신호로 사용하여 최적의 결정 지연을 갖는 블라인드 등화기를 구하였으며 모의실험을 통하여 본 논문에서 제안한 알고리듬의 성능을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1504-1514
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• 2018

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%.

• 품질경영학회지
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• 제22권3호
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• pp.179-190
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• 1994

Financial variables have been used in bankruptcy prediction. Despite of possible errors in prediction, most existing approaches do not consider the causal time sequence of prediction activity and bankruptcy phenomena. This paper proposes a prediction method using Neyman-Pearson Theorem and Bayes' rule. The proposed method uses posterior probability concept and determines a prediction policy with appropriate error rate.

• 응용통계연구
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• 제28권6호
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• pp.1103-1111
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• 2015

본 논문에서는 상대오차의 개념과 벌점회귀를 결합한 새로운 예측방법을 제시하였다. 제안된 방법은 오차항의 분포가 정규성을 크게 벗어나 있어 이상점을 포함하거나 오차항의 분포가 심각하게 비대칭인 경우에도 안정적으로 예측력이 유지할 뿐 아니라 벌점회귀를 통한 변수선택의 성능도 우수하다. 또한 개념적으로 쉽고, 계산 속도가 빠르며, 기존의 알고리즘을 활용해 구현하는 것이 매우 쉽다. 한국교통연구원의 일일 차량통행량 자료 실제 분석 및 모의실험을 통해 제안된 방법의 우수한 성질을 확인하였다.

• Journal of the Korean Statistical Society
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• 제13권2호
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• pp.107-113
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• 1984

In a linear regression model the idependent variables are frequently subject to measurement errors. For this case, the problem of estimating unknown parameters has been extensively discussed in the literature while very few has been concerned with the effect of measurement errors on prediction. This paper investigates the behavior of the predicted values of the dependent variable in terms of the average mean square error of prediction (AMSEP). AMSEP may be used as a criterion for selecting an appropriate estimation method, for designing an estimation experiment, and for developing cost-effective future sampling schemes.

• 산업경영시스템학회지
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• 제23권54호
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• pp.155-165
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• 2000

The fault diagnosis is a systematic and unified method to find based on the observing data resulting in noises. This paper presents the fault prediction and diagnosis using fuzzy expert system technique to manipulate the uncertainties efficiently in predictive perspective. We apply a fuzzy event tree analysis to the computer system, and build up the fault prediction and diagnosis using fuzzy expert system that predicts and diagnoses the error of the system in the advance of error.