• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.88.1-88.1
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• 2011

본 연구에서는 SOFC 시스템 설계기술 개발을 위한 기초 연구로서 전산해석을 이용한 SOFC 성능예측 기법을 개발하였다. 기본설계 단계에서 SOFC의 성능을 개략적으로 예측할 수 있는 1차원 예측 모델을 정립하였으며, 온도, 조성, 전해질, 전극 두께 등을 비롯한 다양한 조건 변화에 따른 성능예측을 수행하여 실험값과 비교한 결과 최대전력밀도 조건에서 23%의 오차를 갖는 것으로 나타났다. 또한 Stack 제작단계에서 다양한 운전조건과 형상변화에 따른 SOFC 성능 변화를 예측할 수 있는 3차원 해석기법을 정립하였으며, 최대전력밀도에서 5.1%의 오차를 보였다. 포괄적인 열 및 물질 전달 현상과 전기화학반응을 3차원적으로 해석함으로써 보다 정확한 예측이 가능하였다. 또한 수소와 적당량의 수분을 함께 공급할 경우 SOFC 성능이 향상되는 것으로 나타났다. 본 연구에서 개발된 기술을 활용할 경우 시제품 제작 전에 전지시스템의 성능을 미리 예측할 수 있으므로, 향후 제품 개발시 제작비용 절감과 설계기간 단축에 기여할 수 있을 것으로 기대된다.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.82-85
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• 2012

In this paper, we present the forward link ACM method to improve the link availability and system throughput. Also, we compare the prediction algorithm between slope based prediction and LMS algorithm. The simulation results show that the 99% of predicted values in LMS algorithm is within 3dB and that of predicted values in the slope based prediction method is within 4.5dB.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.25-33
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• 2009

Recently, the TS fuzzy models that include the linear equations in the consequent part are widely used for time series forecasting, and the prediction performance of them is somewhat dependent on the characteristics of time series such as stationariness. Thus, a new prediction method is suggested in this paper which is especially effective to nonstationary time series prediction. First, data preprocessing is introduced to extract the patterns and regularities of time series well, and then multiple model TS fuzzy predictors are constructed. Next, an appropriate model is chosen for each input data by an adaptive model selection mechanism based on rough sets, and the prediction is going. Finally, the error compensation procedure is added to improve the performance by decreasing the prediction error. Computer simulations are performed on typical cases to verify the effectiveness of the proposed method. It may be very useful for the prediction of time series with uncertainty and/or nonstationariness because it handles and reflects better the characteristics of data.

• Journal of IKEEE
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• v.24 no.4
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• pp.1176-1179
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• 2020

Vessel path prediction methods generally predict the latitude and longitude of a future location directly. However, in the case of direct prediction, errors could be large since the possible output range is too broad. In addition, error accumulation could occur since recurrent neural networks-based methods employ previous predicted data to forecast future data. In this paper, we propose a vessel path prediction method that does not directly predict the longitude and latitude. Instead, the proposed method predicts the acceleration of the vessel. Then the acceleration is employed to generate the velocity and direction, and the values decide the longitude and latitude of the future location. In the experiment, we show that the proposed method makes smaller errors than the direct prediction method, while both methods employ the same model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.119-127
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• 2009

Even though there are next generation Global Navigation Systems in development, only GPS and GLONASS are currently available for satellite positioning. In this study, GLONASS orbits were predicted using Keplerian elements in almanac and the orbit equation. For accuracy validation, predicted orbits were compared with precise ephemeris. As a result, the 3-D maximum and RMS (Root Mean Square) errors were 155.4 km and 56.3 km for 7-day predictions. Also, the GLONASS satellite visibility predictions were compared with real observations, and they agree perfectly except for several epochs when the satellite signal was blocked nearby buildings.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.396-400
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• 2007

This paper proposes the fast partial difference elimination (PDE) algorithm. When the conventional PDE cannot skip the rest of matching procedure in a candidate block using a partial matching error, the proposed algorithm estimates to skip it again using the block matching error predicted from the computed partial matching error. The proposed algorithm can eliminate impossible candidate blocks earlier than the conventional PDE since the predicted block matching error is always bigger than the partial matching error. The simulation results show that the proposed algorithm can significantly reduce the computations while keeping image quality as good as the conventional PDE.

• The Journal of Natural Sciences
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• v.8 no.1
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• pp.5-15
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• 1995

The equation error formulation in the adaptive IIR filtering provides convergence to a global minimum regardless a local minimum with a large stability margin. However, the equation error formulation suffers from the bias in the coefficient estimates. In this paper, a new algorithm, which does not require a prespecification of the noise variance, is proposed for the equation error formulation. This algorithm is based on the equation error smoothing and provides an unbiased parameter estimate in the presence of white noise. Through simulations, it is demonstrated that the algorithm eliminates the bias in the parameter estimate while retaining good properties of the equation error formulation such as fast convergence speed and the large stability margin.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.522-530
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• 2002

This paper introduces a neural network (NN) -based nonlinear predictor for the LP (Linear Prediction) residual. To evaluate the effectiveness of the NN-based nonlinear predictor for LP-residual, we first compared the average prediction gain of the linear long-term predictor with that of the NN-based nonlinear long-term predictor. Then, the effects on the quantization noise of the nonlinear prediction residuals were investigated for the NN-based nonlinear predictor A new NN predictor takes into consideration not only prediction error but also quantization effects. To increase robustness against the quantization noise of the nonlinear prediction residual, a constrained back propagation learning algorithm, which satisfies a Kuhn-Tucker inequality condition is proposed. Experimental results indicate that the prediction gain of the proposed NN predictor was not seriously decreased even when the constrained optimization algorithm was employed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.4
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• pp.411-420
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• 2009

To plan a GPS survey, they have to decide if a survey can be conducted at a specific point and time based on the predicted GPS ephemeris. In this study, to predict ephemeris, we used the repeat time of a GPS satellite. The GPS satellite repeat time was determined by analysing correlation among three-dimensional satellite coordinates provided by the 48-hour GPS ephemeris in the ultra-rapid orbits. By using the calculated repeat time and Lagrange interpolation polynomials, we predicted GPS orbits f3r seven days. As a result, the RMS of the maximum errors in the X, Y, and Z coordinates were 39.8 km 39.7 km and 19.6 km, respectively. And the maximum and average three-dimensional positional errors were 119.5 km and 48.9 km, respectively. When the maximum 3-D positioning error of 119.5 km was translated into the view angle error, the azimuth and elevation angle errors were 9.7'and 14.9', respectively.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.77-86
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• 2004

This paper presents a reservoir operation plan coupled with storage forecasting model to maintain a target storage and a critical storage. The observed storage data from 1990 to 2001 in the Geum-Gang agricultural reservoir in Korea have been applied to the low flow frequency analysis, which yields storage for each return period. Two year return period drought storage is then designated as the target storage and ten year return period drought storage as the critical storage. Storage in reservoir should be forecasted to perform reasonable reservoir operation. The predicted storage can be effectively utilized to establish a reservoir operation plan. In this study the autoregressive error (ARE) model and the ARIMA model are adopted to predict storage of reservoir. The ARIMA model poorly generated reservoir storage in series because only observed storage data were used, but the autoregressive error model made to enhance the reliability of the forecasted storage by applying the explanation variables to the model. Since storages of agricultural reservoir with respect to time have been affected by irrigation area, high or mean temperature, precipitation, previous storage and wind velocity, the autoregressive error model has been adopted to analyze the relationship between storage at a period and affecting factors for storage at the period. Since the equation for predicting storage at a period by the autoregressive error model is similar to the continuity equation, the predicting storage equation may be practical. The results from compared the actual storage in 2002 and the predicted storage in the Geum-Gang reservoir show that forecasted storage by the autoregressive error model is reasonable.