• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.156-157
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• 2022

The purpose of this study is to propose a 40MPa mortar mixed design model that applies the neural network theory to minimize wasted effort in trial and error. A mixed design model was applied to each of the 60 data using fly ash, blast furnace slag fine powder and thickened rice husk powder. And in the neural network model, the optimized connection weight was obtained by repeatedly applying it to the MATLAB. The completed mixed design model was demonstrated by analyzing and comparing the predicted values of the mixed design model with those measured in the actual compressive strength test. As a result of the mixed design verification experiment, the error rates of the double mixed non-cement mortar using blast furnace slag fine powder and rice husk powder at a height of 40MPa were 3.24% and 3.4%. Mixed with fly ash and rice husk powder had an error rate of 3.94% and 5.8%. The error rate of the triple mixed non-cement mortar of the rice husk powder, fly ash, and blast furnace slag fine powder was 2.5% and 5.1%.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.179-180
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• 2023

In this study, the performance of the prediction model was compared and analyzed using DNN and LSTM learning models to predict the amount of dry shrinkage of the concrete. As a result of the analysis, DNN model had a high error rate of about 51%, indicating overfitting to the training data. But, the LSTM learning model showed a relatively higher accuracy with an error rate of 12% compared to the DNN model. Also, the Pre_LSTM model which preprocess data, showed the performance with an error rate of 9% and a coefficient of determination of 0.887 in the LSTM learning model.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.31-44
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• 1995

In fitting a model, there always exists a discrepancy between the fitted model and the true functional relationship. In measuring this discrepancy, Box and Drapper (1959) used the criterion dividing the discrepancy into two parts which are the bias error part and the variance error one in single response case. In this paper, an optimum design which makes these two types of errors as small as possible is found by extending the Box and Drapper criterion to multiple response situation. Especially, a design is found to meat rotatability conditions when we fit a quadratic model to each response fearing cubic bias. Using the central composite design, an application of general results to a specific case is shown to help understanding the material.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.692-699
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• 2003

A novel rotor speed estimation method using Model Reference Adaptive Control(MRAC) is proposed to improve the performance of a sensorless vector controller. In the proposed mettled, the stator current is used as the model variable for estimating the speed. In conventional MRAC methods, the relation between the two model errors and the speed estmation error is unclear. Yet, in the proposed method, the stator current error is represented as a function of the first degree for the error value in the speed estimation. Therefore, the proposed method can produce a fast speed estimation and is robust to the parameters error In addition, the proposed method of offers a considerable improvement in the performance of a sensorless vector controller at a low speed. The superiority of the proposed method is verified by simulation and experiment in a low speed region and at a zero-speed.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.

• 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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.2899-2908
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• 2000

Methodology of volumetric error identification is presented to improve the accuracy of NC machine tools by using a reference artifact and a touch trigger probe. Homogeneous transformation matrix and kinematic chain are used for modeling the geometric and thermal errors of a three-axis vertical machining center. The reference artifact is designed and fabricated to identify the model parameters by machine tool metrology. Parameters in the error model are able to be identified and updated by direct measurement of the reference artifact on the machine tool under the actual conditions which include the thermal interactions of error sources. The proposed method can speed up and simplify volumetric error identification processes.

• 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 advanced smart convergence
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• v.2 no.1
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• pp.21-23
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• 2013

The antenna pattern in this case has a main beam pointed in the desired signal direction, and has a null in the direction of the interference.The conventional antenna pattern concepts of beam width, side lobes, and main beams are not used, as the antenna weights are designed to achieve a set performance criterion such as maximization of the output SNR.A new direction of arrival estimation method using effects of model errors and sensitivity analysis is proposed. Two subspaces are used to form a signal space whose phase shift between the reference signal and its effects of model error signal. Through simulation, the performance showed that the proposed method leads to increased resolution and improved accuracy of DOA estimation relative to those achieved with existing method. Since a desired signal is obtained after interference rejection through correction effects of model error, the effect of channel interference on the estimation is significantly reduced.