• Journal of Advanced Marine Engineering and Technology
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• v.21 no.1
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• pp.66-70
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• 1997

In this paper a new learning algorithm for curvature smoothing and improved generalization for multi-layer neural networks is proposed. To enhance the generalization ability a constraint term of hidden neuron activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. When the total cost consisted of the output error and hidden error is minimized by gradient-descent methods, the additional descent term gives not only the Hebbian learning but also the synaptic weight decay. Therefore it incorporates error back-propagation, Hebbian, and weight decay, and additional computational requirements to the standard error back-propagation is negligible. From the computer simulation of the time series prediction with Santafe competition data it is shown that the proposed learning algorithm gives much better generalization performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.907-914
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• 1999

This paper studies on the model identification of electro-hydraulic servo systems, which are composed of servo valves, double-rod cylinder and load mass. The identified plant is described as a discrete-time ARX or ARMAX model which is respectively obtained from the identification algorithms of least square error method, instrumental variable method and prediction error method. where a nominal model and the variation of model parameters are quantitatively evaluated.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.152-160
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• 1993

In developing a quadratic regression relationship, independent variable is frequently measured with error. In this paper the integrated mean square error of prediction is developed for a quadratic functional relationship model as a measure of the effect of measurement error of the independent variable on the predicted values. The amount of the effect of error is presented and illustrated with an example.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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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%.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of Information Technology Applications and Management
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• v.18 no.4
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• pp.41-53
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• 2011

Trust prediction between users in social network based on the trust propagation assumes properties of transitivity and composability of trust propagation. But it has been hard to find studies which test on how those properties have been operated in real social network. This study aims to validate if the longer the distance of trust paths and the less the numbers of trust paths, the higher prediction error occurs using two real social network data set. As a result, the longer the distance of trust paths, we can find higher prediction error when predicting level of trust between source and target users. But we can not find decreasing trend of prediction error though the possible number of trust paths between source and target users increases.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.869-872
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• 2001

Blind identification and equalization of communication channel is important because it does not need training sequence, nor does it require a priori channel information. So, we can increase the bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind channel estimator and equalizer length mismatch as well as for its simple adaptive algorithms. In this paper, we propose method for fractionally spaced blind equalizer with arbitrary delay using one-step forward prediction error filter from second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as training sequences for data estimation and desired signal for channel estimation.