• Proceedings of the KSME Conference
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• 2005.11a
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• pp.108.1-108.1
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• 2005

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.453-465
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• 2001

It is often the case that one wants to estimate parameters of the distribution which follows certain parametric model, while the dta are contaminated. it is well known that the maximum likelihood estimators are not robust to contamination. Basuet al.(1998) proposed a robust method called the minimum density power divergence estimation. In this paper, we investigate data-driven selection of the tuning parameter $\alpha$ in the minimum density power divergence estimation. A criterion is proposed and its performance is studied through the simulation. The simulation includes three cases of estimation problem.

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.3
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• pp.289-295
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• 2013

It is important to know exact values of Interior Permanent Magnet Synchronous Motors(IPMSM)' parameters such as stator resistance and inductance in order to have their high performance. This paper proposes a novel motor parameter(stator resistance, d&q axis inductance) estimation algorithm for IPMSM. The proposed estimation method utilizes back-EMF equations and model reference adaptive system(MRAS). The algorithm using back-EMF estimates d and q axis inductances in the constant torque region, and the stator resistance is estimated by using MRAS with the estimated inductance regardless of speed regions. The validity of the proposed algorithm is verified by simulations and experiments.

• Journal of Power Electronics
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• v.11 no.6
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• pp.864-869
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• 2011

This paper presents a sensorless control and parameter estimation strategies for a Transverse Flux Rotating Motor (TFRM). The proposed sensorless control method is based on a Model Reference Adaptive System (MRAS) to estimate the stator flux. Parameter estimation theory is also applied into the sensorless control method to estimate motor parameters, such as inductances. The effectiveness of the proposed methods is verified by some simulations and experiments.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.6
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• pp.696-703
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• 2003

A photovoltaic power generation system is an infinite and clean energy system. Recently. because of the realization of high efficiency and low cost PV modules, the studies on the PV system have extensively increased. In this paper. we present an online scheme for parameter estimation of solar cell, based on the model adjustment technique and a real-coded genetic algorithm(RCGA). The ideal diode model and the diode model with series and shunt resistors are used to estimate their parameters, Simulation works using field data in the form of a V-I characteristic curve are carried out to demonstrate the effectiveness of the proposed method.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.78-79
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• 1998

This paper describes a real time algorithm for blood flow estimation of LDF(laser Doppler flowmeter). Many algorithms for blood flow estimation are using power spectral density of Doppler signal by blood flow. In these research, the fast Fourier transformation is used to estimate power spectral density. This is a block processing procedure rather than real time processing. The algorithm in this paper used parametric spectral estimation. This has real time capability by estimation of AR(autoregressive) parameters sample by sample, and has smoothing power spectrum. Also, the frequency resolution is not limited by number of samples used to estimate AR parameter. Another advantage of this algorithm is that AR model enhance SNR.

• ETRI Journal
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• v.37 no.1
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• pp.157-164
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• 2015

As GPU power consumption in smartphones increases with more advanced graphic performance, it becomes essential to estimate GPU power consumption accurately. The conventional GPU power model assumes, simply, that a GPU consumes constant power when turned on; however, this is no longer true for recent smartphone GPUs. In this paper, we propose an accurate GPU power model for smartphones, considering newly adopted dynamic voltage and frequency scaling. For the proposed GPU power model, our evaluation results show that the error rate for system power estimation is as low as 2.9%, on average, and 4.6% in the worst case.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1264-1272
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• 2016

The accurate estimation of leak rate through cracks is crucial in applying the leak before break (LBB) concept to pipeline design in nuclear power plants. Because of its importance, several programs were developed based on the several proposed flow models, and used in nuclear power industries. As the flow models were developed for a homogeneous pipe material, however, some difficulties were encountered in estimating leak rates for bimaterial pipes. In this paper, a flow model is proposed to estimate leak rate in bimaterial pipes based on the modified Henry-Fauske flow model. In the new flow model, different crack morphology parameters can be considered in two parts of a flow path. In addition, based on the proposed flow model, a program was developed to estimate leak rate for a crack with linearly varying cross-sectional area. Using the program, leak rates were calculated for through-thickness cracks with constant or linearly varying cross-sectional areas in a bimaterial pipe. The leak rate results were then compared and discussed in comparison with the results for a homogeneous pipe. The effects of the crack morphology parameters and the variation in cross-sectional area on the leak rate were examined and discussed.

• Nuclear Engineering and Technology
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• v.52 no.4
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• pp.846-855
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• 2020

A loose part monitoring system is used to detect unexpected loose parts in a reactor coolant system in a nuclear power plant. It is still necessary to develop a new methodology for the localization and mass estimation of loose parts owing to the high estimation error of conventional methods. In addition, model-based diagnostics recently emphasized the importance of a model describing the behavior of a mechanical system or component. The purpose of this study is to propose a new localization and mass-estimation method based on finite element analysis (FEA) and optimization technique. First, an FEA model to simulate the propagation behavior of the bending wave generated by a metal sphere impact is validated by performing an impact test and a corresponding FEA and optimization for a downsized steam-generator structure. Second, a novel methodology based on FEA and optimization technique was proposed to estimate the impact location and mass of a loose part at the same time. The usefulness of the methodology was then validated through a series of FEAs and some blind tests. A new feature vector, the cross-correlation function, was also proposed to predict the impact location and mass of a loose part, and its usefulness was then validated. It is expected that the proposed methodology can be utilized in model-based diagnostics for the estimation of impact parameters such as the mass, velocity, and impact location of a loose part. In addition, the FEA-based model can be used to optimize the sensor position to improve the collected data quality in the site of nuclear power plants.

• Journal of Power Electronics
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• v.12 no.1
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• pp.40-48
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• 2012

This paper proposes the State-of-charge (SOC) estimator of a LiPB Battery using the Extended Kalman Filter (EKF). EKF can work properly only with an accurate model. Therefore, the high accuracy electrical battery model for EKF state is discussed in this paper, which is focused on high-capacity LiPB batteries. The battery model is extracted from a single cell of LiPB 40Ah, 3.7V. The dynamic behavior of single cell battery is modeled using a bulk capacitance, two series RC networks, and a series resistance. The bulk capacitance voltage represents the Open Circuit Voltage (OCV) of battery and other components represent the transient response of battery voltage. The experimental results show the strong relationship between OCV and SOC without any dependency on the current rates. Therefore, EKF is proposed to work by estimating OCV, and then is converted it to SOC. EKF is tested with the experimental data. To increase the estimation accuracy, EKF is improved with a single dominant varying parameter of bulk capacitance which follows the SOC value. Full region of SOC test is done to verify the effectiveness of EKF algorithm. The test results show the error of estimation can be reduced up to max 5%SOC.