• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.269-276
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• 2012

The objective of this short communication is to introduce several evaluation methods to crop model users because the evaluation of crop model performance is an important step to develop or select crop model. In this paper, mean error, mean absolute error, index of agreement, root mean square error, efficiency of model, accuracy factor and bias factor were explained and compared in terms of dimension and observed number. Efficiency of model and index of agreement are dimensionless and independent of number of observation. Relative root mean square, accuracy factor and bias factor are dimensionless and not independent of number of observation. Mean error and mean absolute error are affected by dimension and number of observation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.108-115
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• 2009

In this paper, we propose a novel true-motion estimation technique supporting efficient frame error concealment for error-resilient video coding. In general, it is important to accurately obtain the true-motion of objects in video sequences for effectively recovering the corrupted frame due to transmission errors. However, the conventional motion estimation (ME) technique, which minimizes a sum of absolute different (SAD) between pixels of the current block and the motion-compensated block, does not always reflect the true-movement of objects. To solve this problem, we introduce a new metric called an absolute difference of motion vectors (ADMV) which is the distance between motion vectors of the current block and its motion-compensated block. The proposed ME method can prevent unreliable motion vectors by minimizing the weighted combination of SAD and ADMV. In addition, the proposed ME method can significantly improve the performance of error concealment at the decoder since error concealment using the ADMV can effectively recover the missing motion vector without any information of the lost frame. Experimental results show that the proposed method provides similar coding efficiency to the conventional ME method and outperforms the existing error-resilient method.

• Physical Therapy Korea
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• v.14 no.3
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• pp.90-96
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• 2007

The purpose of this study was to compare the joint position sense at the knee joint at 3 different surface conditions by using the active knee joint angle reproduction test in the standing position. Twenty healthy volunteers (10 males and 10 females) age 20~29 years were recruited for this study. The knee joint position senses were assessed at three different surface conditions: on the floor (stable condition), TOGU (soft condition), and seat fit (unstable condition) in a closed kinetic chain. Testing orders were selected randomly. The absolute angle error was defined as the absolute difference between target angles ($30^{\circ}{\sim}45^{\circ}$ knee flexion) and subject perceived angle of the knee flexion. One way ANOVA was used to compare the absolute angle of error among 3 different conditions. The Independent t-test was used to compare the absolute angle of error between male and female. The error angles were significantly different among surface conditions ($1.3^{\circ}{\pm}1.2^{\circ}$ for the floor, $2.1^{\circ}{\pm}0.9^{\circ}$ for the TOGU, and $4.4^{\circ}{\pm}1.8^{\circ}$ for the seat fit, p<.05). There was no significant difference in error angle between male and female. In conclusion, the joint position sense of the knee joint in the closed kinetic chain decreased at unstable surface conditions. The result of this study indicates that surface conditions should be considered when assessing and training the joint position sense of the knee joint in clinical setting.

• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.187-199
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• 2000

In this paper, we deal with the asymptotic properties of the least absolute deviation estimators in the nonlinear time series regression model. For the sinusodial model which frequently appears in a time series analysis, we study the strong consistency and asymptotic normality of least absolute deviation estimators. And using the derived limiting distributions we show that the least absolute deviation estimators is more efficient than the least squared estimators when the error distribution of the model has heavy tails.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.561-568
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• 2015

In this study we predict apartment prices per unit in Daegu-Gyeongbuk areas by spatial lag and spatial error models, both of which belong to so-called spatial regression model. A spatial weight matrix is constructed by k-nearest neighbours method and then the models for the apartment prices in March, 2012 are fitted using the weight matrix. The apartment prices in March, 2013 are predicted by the fitted spatial regression models and then performances of two spatial regression models are compared by RMSE (root mean squared error), RRMSE (root relative mean squared error), MAE (mean absolute error).

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.4
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• pp.431-436
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• 2008

We have extended an absolute evaluation method to obtain the ratio error and phase displacement of a current transformer (CT) up to primary current of 40,000 A by measuring four parameters of equivalent circuit in CT. The method was applied to CTs under test with the current ratios in the range of 5,000 A / 5 A - 40,000 A / 5 A. The ratio error and phase displacement of the CTs under test obtained in this study are consistent with those measured at the national institutes in Canada and Germany using the same CTs under test within an expanded uncertainty (k = 2) in the overall current ratios.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.349-360
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• 2013

Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.119-124
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• 1996

Variable step size LMS(VS-LMS) algorithms improve performance of LMS algorithm by means of varying the step size. This paper presents a new VS-LMS algorithm using normalized absolute estimation error. Normalizing the estimation error to the expected valus of the desired signal, we determined the step size using the relative size of estimation error, Because parameters and computational load are less, our algorithm is easy to implement in hardware. The performance of the proposed algorithm is analyzed theoretically and estimated through simulations. Based on the theoretical analysis and computer simulations, the proposed algorithm is shown to be effective compared to conventional VS-LMS algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.457-459
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• 2018

Solar power generation system is a energy generation technology that produces electricity from solar power, and it is growing fastest among renewable energy technologies. It is of utmost importance that the solar power system supply energy to the load stably. However, due to unstable energy production due to weather and weather conditions, accurate prediction of energy production is needed. In this paper, an Artificial Neural Network(ANN) that predicts solar energy using 15 kinds of meteorological data such as precipitation, long and short wave radiation averages and temperature is implemented and its performance is evaluated. The ANN is constructed by adjusting hidden parameters and parameters such as penalty for preventing overfitting. In order to verify the accuracy and validity of the prediction model, we use Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) as performance indices. The experimental results show that MAPE = 19.54 and MAE = 2155345.10776 when Hidden Layer $Sizes=^{\prime}16{\times}10^{\prime}$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.626-628
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• 2018

In this paper, we compare the performances of SVM (Support Vector Machine) and ANN (Artificial Neural Network) machine learning models for predicting solar energy by using meteorological data. Two machine learning models were built by using fifteen kinds of weather data such as long and short wave radiation average, precipitation and temperature. Then the RBF (Radial Basis Function) parameters in the SVM model and the number of hidden layers/nodes and the regularization parameter in the ANN model were found by experimental studies. MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) were considered as metrics for evaluating the performances of the SVM and ANN models. Sjoem Simulation results showed that the SVM model achieved the performances of MAPE=21.11 and MAE=2281417.65, and the ANN model did the performances of MAPE=19.54 and MAE=2155345.10776.