• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.5
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• pp.926-932
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• 2007

A recently developed methods for on-site calibration of the current transformer (CT) comparator system have been reviewed in the paper. The method utilizes several traveling standards, which consist of the CT, non-reactive standard resistors, wide ratio error CT, and shunt resistors. The traveling CT is used for absolute evaluation of a standard CT belonging to industry. The non-reactive standard resistors and a wide ratio error CT are used for the linearity check of errors in the current comparator. The shunt resistors are used for evaluation of CT burden of industry.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1268-1274
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• 2007

A recently developed methods for the on-site calibration of the voltage transformer (VT) comparator system have been reviewed in the paper. The method utilizes the several traveling standards consisting of the VT, the non-reactive standard resistors, the wide ratio error VT, and the decade resistors. The VT is used for the absolute evaluation of a standard VT belonging to the industry. The non-reactive standard resistor and wide ratio error VT are used for the linearity check of errors in the voltage comparator of the industry. The decade resistors are used for evaluation of a VT burden of the industry.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05c
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• pp.53-56
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• 2004

This paper describes the method of position error reduction in Linear Pulse Motor (LPM). Though the micro step exiting method is applied, the vibration and position error caused by trust distortion from mechanism still remains. This paper presents the method for reduction of position error on moving the mover at whole cycle, by compensating a scale factor through real-time control of the PWM pulse width corresponding to exiting current command and absolute position error from linear encoder.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.273-289
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• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.702-708
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• 2013

Underwater TRN (Underwater Terrain Referenced Navigation) estimates an underwater vehicle state by measuring a distance between the vehicle and undersea terrain, and comparing it with the known terrain database. TRN belongs to absolute navigation methods, which are used to compensate a drift error of dead reckoning measurements such as IMU (Inertial Measurement Unit) or DVL (Doppler Velocity Log). However, underwater TRN is different to other absolute methods such as USBL (Ultra-Short Baseline) and LBL (Long Baseline), because TRN is independent of the external environment. As a magnetic-field-based navigation, TRN is a kind of geophysical navigation. This paper develops an EKF (Extended Kalman Filter) formulation for underwater TRN. A filter propagation part is composed by an inertial navigation system, and a filter update is executed with echo-sounder measurement. For large-initial-error cases, an adaptive EKF approach is also presented, to keep the filter be stable. At the end, simulation studies are given to verify the performance of the proposed TRN filter. With simplified sensor and terrain database models, the simulation results show that the underwater TRN could support conventional underwater navigation methods.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.104-111
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• 2002

This paper presents a fuzzy rule extraction method using EM(Expectation-Maximization) algorithm and a design method of adaptive neuro-fuzzy control. EM algorithm is used to estimate a maximum likelihood of a GMM(Gaussian Mixture Model) and cluster centers. The estimated clusters is used to automatically construct the fuzzy rules and membership functions for ANFIS(Adaptive Neuro-Fuzzy Inference System). Finally, we applied the proposed method to the water temperature control system and obtained better results with respect to the number of rules and SAE(Sum of Absolute Error) than previous techniques such as conventional fuzzy controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.5
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• pp.105-111
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• 2004

This paper shows that active and reactive power load have significant positive relationship and there exist two types of relationship between them using Test Statistics. In investigating the cross plots at every hour, we found out that from 0 to 8 hours, there relationships are linear, while from 9 to 23 hours, they are two piece-wise linear. Also, reactive power loads was estimated and forecasted using active power load as the explanary variable with OLS (Ordinary Least Squares) regression methods. MAPE (Mean Absolute Percentage Error) for each model is calculated for one-hour ahead forecasting.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.843-853
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• 2014

Accurate electricity demand forecasting for daily peak load is essential for management and planning at electrical facilities. In this paper, we rst, introduce the several time series models that forecast daily peak load and compare the forecasting performance of the models based on Mean Absolute Percentage Error(MAPE). The results show that the Reg-AR-GARCH model outperforms other competing models that consider Cooling Degree Day(CDD) and Heating Degree Day(HDD) as well as seasonal components.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.3
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• pp.8-14
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• 2009

In this paper, it is proposed that the design of the PID controller with the internal model control structure for improved performance. Internal model was identification that is second-order plus dead time structure using final-value theorem and genetic algorithm The parameters of Controller are determined to minimize IAE(Integral of the Absolute value of the Error) and ITAE(Integral of the Time multiplied by the Absolute value of the Error) of performance index by internal model and numerical method. Simulation examples are given to show the better performance of the proposed method than conventional methods.