• Journal of KSNVE
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• v.8 no.6
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• pp.1023-1029
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• 1998

When one attempts to construct a hologram. one finds that there are many sources of measurement errors. These errors are even amplified if one predicts the pressures close to the sources. The pressure estimation errors depend on the following parameters: the measurement spacing on the hologram plane. the prediction spacing on the prediction plane. and the distance between the hologram and the prediction plane. This raper analyzes quantitatively the errors when these are distributed irregularly on the hologram plane The sensor mismatch and inaccurate measurement location. position mismatch. are mainly addressed. In these cases. one can assume that the measurement is a sample of many measurement events. The bias and random error are derived theoretically. Then the relationship between the random error amplification ratio and the parameters mentioned above is examined quantitatively in terms of energy.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.522-525
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• 2005

Light detection and ranging (LIDAR) is one of the effective technologies for monitoring forest inventory, and importance of forestry is increasing because of its function as the sink of green house gases (GHG). This study aims at development of a methodology for better and more accurate estimation of physical parameters of individual trees by removing sudden drops of LIDAR data within a crown. Our study area is located in Aomori prefecture, the northern part of Honshu Island, with the dominant species of Japanese cedar. The results show practicality of our method in the usage of LIDAR data in the field of forest inventory.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1194-1197
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• 2004

High temporal-resolution and accurate measurement of skin impedance locus provides useful data for the identification of the physiological/psychological changes and also the identification of acupuncture point. An impedance spectroscopy method using digitally constructed current waveform consisting of many frequency components (multiples of 1Hz) was reported3. The time resolution of the method depends on the lowest frequency used in the waveform construction, and therefore, the measurement would be faster if the lowest frequency is the higher. However, it was not clear that how many and how low frequencies must be used for the estimation of the skin impedance parameters from which the impedance locus can be drawn. This study shows the relationship between the estimation error of the impedance parameters and the frequency coverage of the spectroscopy. The results of this study are expected to serve as the reference of the frequency selection in the impedance spectroscopy.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1852-1860
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• 2000

Distinct from the Markov random field or pseudo 2D HMM models for image analysis, this paper proposes a new model of 2D hidden Markov mesh chain(HMMM) model which subsumes the definitions of and the assumptions underlying the conventional HMM. The proposed model is a new theoretical realization of 2D HMM with the causality of top-down and left-right progression and the complete lattice constraint. These two conditions enable an efficient mesh decoding for model estimation and a recursive maximum likelihood estimation of model parameters. Those algorithms are developed in theoretical perspective and, in particular, the training algorithm, it is proved, attains the optimal set of parameters.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.73-81
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• 2003

In case of hollow cylinder extrusion using porthole die, the effects of extrusion parameters-temperature, the speed of extrusion, the shape of the die and mandrel-on metal flow in porthole die extrusion of aluminum have been investigated. However, there have been few studies about condenser tube extruded by porthole die. Original metal flow of condenser tube by porthole die extrusion is similar to hollow cylinder extrusion but the estimation of metal flow for extrusion parameters is different. For example, variation of chamber length in hollow extrusion only affects the welding pressure, however, the welding chamber length in condenser tube extrusion influences to the welding pressure as well as the deflection of mandrel. This study was designed to evaluate metal flow, welding pressure, extrusion load, tendency of mandrel deflection according to angular variation in the bottom of chamber in porthole die. Estimation was carried out using finite element method in as non-steady state. Analytical results can provide useful information the optimal design of porthole die.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.244-249
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• 1998

The present paper improves the direct identification scheme based upon the equation error formulation with incomplete modal data. First, an indirect estimation technique is considered for estimating unmeasured elements of latent vectors by the combined use of a model and measured incomplete eigen vectors. It is used for estimating the other elements of eigen vectors, which are essential for identification but not available. Next an index is introduced here to indicate the quality of estimation with respect to the mode and the measured positions. A sensitivity formula for eigenvalues with respect to the unknown joint coefficient is also derived to select the modes appropriate for identification. An identification strategy is suggested to meet with practical problems with the help of the index and sensitivity formula. The index and the sensitivity are proved to be useful for selecting measurement positions and modes appropriate for identification A comprehensive simulation study is performed to test the proposed method.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.6
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• pp.207-213
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• 1999

In this study, the possibility of estimation of friction coefficient between tire and road surface using running car data are checked. To get necessary data, such as tire and car velocities and braking force, a test car is driven with certain magnitude of decelerations from pre-set initial velocities to stop . The data are used to estimate friction coefficient with property chosen parameters , e.g,, driving stiffness, pressure distribution functions, etc. Experimental results show that running data car be used with properly chosen parameters to estimate friction coefficient.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.199-202
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• 1996

In this paper, we used the maximum likelihood method for 2-point aerodynamic model to determine the parameters of the ChangGong-91. Since the estimation from the flight test of real aircraft is the most reliable, we performed the flight test of ChangGong-91 to get the parameters such as velocity, height, 3 axis acceleration, 3 axis angular rate, pitch angle, angle of attack, temperature and so on. We recorded the flight test data in S-VHS tapes and stored them to personal computer using A/D(analog to digital) converter. Flight test was done in stall motion, and the acquired data was be processed with parameter identification method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.527-530
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• 1996

The Kalman filter has been used as a self-localization method for the mobile robot. To satisfy the assumptions inherent in the Kalman filter, we should calibrate the sensors of the robot before use of them. However, it is generally hard to find exact sensor parameters, and the parameters may change during the robot task as the environment varies. Thus we need to perform on-line sensor calibration, by which we can obtain more credible location of the mobile robot. In this paper, we present an on-line sensor calibration scheme which estimates the unknown sensor bias and the current position of the robot. To this end, first we find out the calibration errors of the sensor from redundant sensory data using the parity vector and recursive minimum variance estimation. Then we calculate the current position of the robot by weighted least square estimation without internal encoder data. The performance of the proposed method is evaluated through computer simulation.