• 산업기술연구
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• 제28권B호
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• pp.199-207
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• 2008

A linear wind prediction program, WAsP, was employed to predict wind speed at two different sites located in complex terrain in South Korea. The reference data obtained at locations more than 7 kilometers away from the prediction sites were used for prediction. The predictions from the linear model were compared with the measured data at the two prediction sites. Two compensation methods such as a self-prediction error method and a delta ruggedness index (RIX) method were used to improve the wind speed prediction from WAsP and showed a good possibility. The wind speed prediction errors reached within 3.5 % with the self prediction error method, and within 10% with the delta RIX method. The self prediction error method can be used as a compensation method to reduce the wind speed prediction error in WAsP.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 추계학술대회 논문집
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• pp.268-273
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• 2008

A linear wind prediction program, WAsP, was employed to predict wind speed at two different sites located in complex terrain in South Korea. The reference data obtained at locations more than 7 kilometers away from the prediction sites were used for prediction. The predictions from the linear model were compared with the measured data at the two prediction sites. Two compensation methods such as a self-prediction error method and a delta ruggedness index (RIX) method were used to improve the wind speed prediction from WAsP and showed a good possibility. The wind speed prediction errors reached within 3.5 % with the self prediction error method, and within 10% with the delta RIX method. The self prediction error method can be used as a compensation method to reduce the wind speed prediction error in WAsP.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.77-86
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• 2006

This study is to investigate the error of prediction value with related variables from the recommended system and to examine the error of prediction value with related variables. To decrease the error on the collaborative recommended system on user based, this research explored the effects on the prediction related response pair between raters' demographic variables and Pearson's coefficient and sparsity. The result shows comparative analysis between existing error of prediction value and conditioned one.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.47-52
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• 2002

There are many factors in machine tool error. These are thermal deformation, geometric error, machine's part assembly error, error caused by tool bending. Among them thermal error is 70% of total error of machine tool . Prediction of thermal error is very difficult. because of nonlinear tendency of machine tool deformation. In this study, we tried thermal error prediction by using multi regression analysis.

• 한국정밀공학회지
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• 제25권5호
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• pp.52-59
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• 2008

The accuracy of the machine tools is degraded because of thermal error of structure due to thermal variation. To improve the accuracy of a machine tools, measurement and prediction of thermal error is very important. The main part of thermal source is spindle due to high speed with friction. The thermal error of spindle is very important because it is over 10% in total thermals errors. In this paper, the suitable thermal error prediction technology for machine tools with open architecture controller is developed and implemented to machine tools. Two thermal error prediction technologies, neural network and multi-linear regression, are investigated in several methods. The multi-linear regression method is more effective for implementation to CNC. The developed thermal error prediction technology is implemented on the internal function of CNC.

• 전기학회논문지
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• 제59권6호
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• pp.1159-1166
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• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• 한국통신학회논문지
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• 제25권6A호
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• pp.782-793
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• 2000

본 논문은 10GHz이상의 주파수 대역을 사용하는 위성 통신 링크에서 강우에 의해 감쇠된 신호 레벨을 동적으로 예측하기 위한 비교적 간단한 예측 기법을 제시한다. 예측 기법은 이산시간 저역 통과 필터링, 기울기에 근거한 예측, 평균 오차 보정, 고정 및 가변 혼합 예측 여유 할당의 4가지 기능 블록을 갖는다. Ku 대역의 측정 데이터로부터 주파수 스케일링에 의해 얻어진 Ka 대역 강우 감쇠 데이터를 이용하여 시뮬레이션을 수행하였다. 평균 오차 보정을 갖는 기울기 예측 기법은 1dB 이하의 표준 편차를 가지며, 평균 오차 보정에 의해 약 1.5~2.5 배의 예측 오차 감소를 보인다. 요구되는 평균 여유 면에서, 혼합 예측 여유 할당은 고정 여유 방법과 가변 여유 방법에 비해 더 적은 평균 여유를 요구한다.

• 전기학회논문지
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• 제57권11호
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• pp.2095-2101
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• 2008

In this paper, we consider a performance improvement of neural network for predicting defect size of steam generator tube using early stopping. Usually, neural network is trained until MSE becomes less than a prescribed error goal. The smaller the error goal, the greater the prediction performance for the trained data. However, as the error goal is decreased, an over fitting is likely to start during supervised training of a neural network, which usually deteriorates the generalization performance. We propose that, for the prediction of an axisymmetric defect size, early stopping can be used to avoid the over-fitting. Through various experiments on the axisymmetric defect samples, we found that the difference bet ween the prediction error of neural network based on early stopping and that of ideal neural network is reasonably small. This indicates that the error goal used for neural network training for the prediction of defect size can be efficiently selected by early stopping.

• 한국안전학회지
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• 제16권3호
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• pp.117-124
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• 2001

This paper introduces m analysis framework and procedure for the support of the cognitive error analysis of emergency tasks in nuclear poler plants. The framework provides a new perspective in the utilization of influencing factors into error prediction. The framework can be characterized by two features. First, influencing factors that affect the occurrence of human error me classified into three groups, i.e., task characteristic factors(TCF), situation factors(SF), and performance assisting factors(PAF). This classification aims to support error prediction from the viewpoint of assessing the adequacy of PAF under given TCF and SF. Second, the assessment of influencing factors is made by each cognitive function. Through this, influencing factors assessment and error prediction can be made in an integrative way according to each cognitive function. In addition, it helps analysts identify vulnerable cognitive functions and error factors, and obtain specific nor reduction strategies. The proposed framework was applied to the error analysis of the bleed and feed operation of nuclear emergency tasks.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.263-266
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• 2009

When video packets are transmitted over error-prone networks, the leaky prediction can be used to mitigate the effect of error propagation. The leaky factor provides trade-off between coding efficiency and error resilience in the leaky prediction. In this paper, we propose an improved leaky prediction method where the leaky factor is adaptively determined for each frame by minimizing the estimated end-to-end distortion at the encoder. Experimental results show that the proposed method with the adaptive leaky factor shows the better performance of the error robustness as compared with the conventional method.