• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.933-946
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• 2013

The quantitative analysis of evapotranspiration (ET) is a key component in hydrological studies and the establishment of water resources planning. Generally, the quantitative analysis of ET is performed by the estimation method of potential or reference ET based on meteorological factors such as air temperature, wind speed, etc. Hargreaves equation is one of empirical methods for reference ET using air temperature data. In this study, in order to estimate more exact reference ET considering climatological characteristics in Korea, parameter regionalization of Hargreaves equation is carried out. Firstly, modified Hargreaves equation is presented after the analysis of the relationship between solar radiation and temperature. Secondly, parameter ($K_{ET}$) optimization of Hargreaves equation is performed using Penman-Monteith method and modified equation at 71 weather stations. Lastly, the equation for calculating $K_{ET}$ using temperature data is proposed and verified. As a result, reference ET from original Hargreaves equation is overestimated or underestimated compared with Penman-Monteith method. But modified equation in this study is more accurate in the climatic conditions of Korea. In addition, the applicability of the equation between $K_{ET}$ and temperature is confirmed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.102-111
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• 2000

Methodology of volumetric error identification and compensation 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 geo-metric 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. A volumetric error compensation system based on IBM/PC is linked with a FANUC CNC controller to compensate for the identified volumetric error in machining workspace.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.835-844
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• 2016

We develop the testing procedures about the homogeneity of the shape parameters of several inverse Gaussian distributions in our paper. We propose default Bayesian testing procedures for the shape parameters under the reference priors. The Bayes factor based on the proper priors gives the successful results for Bayesian hypothesis testing. For the case of the lack of information, the noninformative priors such as Jereys' prior or the reference prior can be used. Jereys' prior or the reference prior involves the undefined constants in the computation of the Bayes factors. Therefore under the reference priors, we develop the Bayesian testing procedures with the intrinsic Bayes factors and the fractional Bayes factor. Simulation study for the performance of the developed testing procedures is given, and an example for illustration is given.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1239-1243
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• 2004

A novel voltage-current reference circuit for stable voltage-current applications is Proposed. Circuits for a positive and for a negative voltage-current reference are presented and are designed with commercial CMOS technology. The voltage-current reference that is stable over ambient temperature variations is an important component of most data acquisition systems. These results are verified by the HSPICE simulation $0.8{\mu}m$ parameter. As the result, the temperature dependency of output voltage and output current each is $0.57mV/^{\circ}C$, $0.11{\mu}A/^{\circ}C$ and the power dissipation is 1.8 mV on 5V supply voltage.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.92-99
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• 1997

A model reference adaptive control(MRAC) theory is very useful for controlling a plant of which the parameters are unknown or vary during operation usint only input-output signal of plant. In this study, 2' nd order discreter time MRAC controller is designed for an electrohydraulic position control system which is represented with nonlinear mathematical model and the least square method is adopted for the para-meter adjustment law. This control algorthm is applied to the position control of electrohydraulic servosystem through computer simulation and the effect of the change of load, sampling time upon the performance following reference model and upon the performance of estimating plant parameters are examined.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.124-134
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• 2011

A new dead time compensation scheme that can exactly estimate the dead time and inverter nonlinearity under parameter variations is proposed for a PWM inverter-fed PMSM drive. The proposed scheme uses the fact that the sixth harmonic component in total disturbance estimated under the presence of various uncertainties is mainly caused by the dead time and inverter nonlinearity. The total disturbance due to the parameter variations as well as the dead time and inverter nonlinearity is estimated by the adaptive scheme. The sixth harmonic component is extracted from this total disturbance through harmonic analysis. The obtained sixth harmonic is processed by the PI controller to estimate the disturbance caused by the dead time and inverter nonlinearity in the stationary reference frame. The effectiveness of the proposed scheme is verified. Without requiring an additional hardware, the proposed scheme can effectively compensate the dead time and inverter nonlinearity even under the parameter variations.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.207-209
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.