• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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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.

• Journal of Industrial Technology
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• v.28 no.B
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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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• 2008.10a
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• ETRI Journal
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• v.22 no.4
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• pp.32-39
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• 2000

In low earth orbit (LEO) satellite communication systems, more severe phase distortion due to Doppler shift is frequently detected in the received signal than in cases of geostationary earth orbit (GEO) satellite systems or terrestrial mobile systems. Therefore, an estimation of Doppler shift would be one of the most important factors to enhance performance of LEO satellite communication system. In this paper, a new adaptive Doppler compensation scheme using location information of a user terminal and satellite, as well as a weighting factor for the reduction of prediction error is proposed. The prediction performance of the proposed scheme is simulated in terms of the prediction accuracy and the cumulative density function of the prediction error, with considering the offset variation range of the initial input parameters in LEO satellite system. The simulation results showed that the proposed adaptive compensation algorithm has the better performance accuracy than Ali's method. From the simulation results, it is concluded the adaptive compensation algorithm is the most applicable method that can be applied to LEO satellite systems of a range of altitude between 1,000 km and 2,000 km for the general error tolerance level, M = 250 Hz.

• The Journal of Korea Robotics Society
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• v.11 no.4
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• pp.277-284
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• 2016

A new prediction scheme has been proposed for the robust teleoperation in a non-visible environment. The positioning error caused by the time delay in the non-visible environment has been compensated for by the Smith predictor and the sensory data have been estimated by the Grey model. The Smith predictor is effective for the compensation of the positioning error caused by the time delay with a precise system model. Therefore the dynamic model of a mobile robot has been used in this research. To minimize the unstable and erroneous states caused by the time delay, the estimated sensor data have been sent to the operator. Through simulations, the possibility of compensating the errors caused by the time delay has been verified using the Smith predictor. Also the estimation reliability of the measurement data has been demonstrated. Robust teleoperations in a non-visible environment have been performed with a mobile robot to avoid the obstacles effective to go to the target position by the proposed prediction scheme which combines the Smith predictor and the Grey model. Even though the human operator is involved in the teleoperation loop, the compensation effects have been clearly demonstrated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6A
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• pp.782-793
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• 2000

This paper presents a simple dynamical prediction scheme of the signal level which is attenuated and varied due to rain fading in satellite communication links using above 10GHz frequency bands. The proposed prediction scheme has four functional blocks for discrete-time low-pass filtering, slope-based prediction, mean-error correction and hybrid fixed/variable prediction margin allocation. Through simulations using Ka-band attenuation data obtained from the data measured over Ku-band by frequency-scaling, it is shown that the slope-based prediction with the mean-error correction has as small standard deviation of prediction error as below 1 dB, and that the error is about 1.5 to 2.5 times as small as that without the mean-error correction. The hybrid prediction margin allocation requires smaller average margin than those of both fixed and variable methods.

• Journal of Mechanical Science and Technology
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• v.15 no.11
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• pp.1482-1489
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• 2001

Thermally-induced errors originating from machine tool errors have received significant attention recently because high speed and precise machining is now the principal trend in manufacturing proce sses using CNC machine tools. Since the thermal error model is generally a function of temperature, the thermal error compensation system contains temperature sensors with the same number of temperature variables. The minimization of the number of variables in the thermal error model can affect the economical efficiency and the possibility of unexpected sensor fault in a error compensation system. This paper presents a thermal error model with minimum number of variables using a fuzzy logic strategy. The proposed method using a fuzzy logic strategy does not require any information about the characteristics of the plant contrary to numerical analysis techniques, but the developed thermal error model guarantees good prediction performance. The proposed modeling method can also be applied to any type of CNC machine tool if a combination of the possible input variables is determined because the error model parameters are only calculated mathematically-based on the number of temperature variables.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.2
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• pp.128-134
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• 2008

This paper presents a machining error compensation methodology due to deflection of micro cutting tools in side cutting processes. Generally in order to compensate for tool deflection errors it is necessary to carry out a series of simulations, cutting force prediction, tool deflection estimation and compensation method. These can induce numerous calculations and expensive costs. This study proposes an improved approach which can compensate for machining errors without simulation processes concerning prediction of cutting force and tool deflection. Based on SEM images of test cutting specimens, polynomial relationships between machining errors and corrected tool positions were induced. Taking into account changes of cutting conditions caused by tool position variation, an iterative algorithm was applied in order to determine corrected tool position. Experimental works were carried out to validate the proposed approach. Comparing machining errors of nominal cutting with those of compensated cutting, overall machining errors could be remarkably reduced.

• 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.