• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.228-234
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• 2000

The aim of the work is to present a new method of estimating the existence of a mass unbalance and mass unbalance under a crack on a rotating shaft. This is an advanced new method for the detection of a mass unbalance and a new way to estimate the position of it under crack influence. As the first step, the shaft is physically modelled with a finite element method and the dynamic mathematical model is derived by using the Hamilton principle; thus, the system is represented by various subsystems. The equation of motion of the shaft with a mass unbalance and a crack are established by adapting the local mass unbalance and the stiffness change. this is a reference system for the given system. Based on a model for transient behavior induced from vabrations measured at the bearings, an elementary Estimator is designed to detect mass unblance on the shaft. Using the Estimator, a bank of the Estimator is established to estimate the estimate the position of the mass unbalance and arranged at a certain location on the shaft. The informations for the given system are the measurements of bearing displacements and velocity.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.811-817
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• 2011

We shall introduce a general probability mass function which includes several discrete probability mass functions. Especially, when the random variable X is Poisson, binomial, and negative binomial random variables as some special cases of the introduced distribution, the maximum likelihood estimator (MLE) and the uniformly minimum variance unbiased estimator (UMVUE) of the probability P(X ${\leq}$ t) are considered. And the efficiencies of the MLE and the UMVUE of the reliability ar compared each other.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1103-1108
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• 2007

This paper describes a robust model-based roll state estimator for application to the detection of impending vehicle rollover. The roll state estimator is based on a 2-D bicycle model and a roll model to estimate the maneuver-induced vehicle roll motion. The measurement signals are lateral acceleration, yaw rate, steering angle, and vehicle speed. Vehicle mass is adapted to obtain robust performance of the estimator. Computer simulation is conducted to evaluate the proposed roll state estimator by using a validated vehicle simulator. It is shown that the roll state estimator shows robust performance without exact vehicle mass information.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.286-293
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• 1999

A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should dbjust the time constant of the adopted filter according to the natural resonance frequency detemined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.13-19
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• 2002

In nonparametric entropy estimation, both mass and mean-preserving maximum entropy distribution (Theil, 1980) and the underlying distribution of the sample entropy (Vasicek, 1976), the most widely used entropy estimator, consist of nb mass-preserving densities based on disjoint Intervals of the simple averages of two adjacent order statistics. In this paper, we notice that those nonparametric density functions do not actually keep the mass-preserving constraint, and propose a modified sample entropy by considering the generalized 0-statistics (Kaigh and Driscoll, 1987) in averaging two adjacent order statistics. We consider the proposed estimator in a goodness of fit test for normality and compare its performance with that of the sample entropy.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.14-20
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• 2020

This paper presents a vehicle mass and road grade estimator for developing an automated bus. To consider the dynamic characteristics of a bus varying with the number of passengers, the longitudinal controller needs the estimation of the vehicle's mass and road grade in real-time and utilizes the information to adjust the control gains. Discrete Kalman filter is applied to estimate the time-varying road grade, and the recursive least squares algorithm is adopted to account for the constant mass estimation. After being implemented in MATLAB/Simulink, the estimators are evaluated with the dynamic model and experimental data of the target bus. The proposed estimators will be applied to complement the algorithm of the longitudinal controller and proceed with algorithm verification.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.428-436
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• 2005

In this paper, we introduce an adaptive control method to improve the position accuracy and reduce nonlinear friction effects for the linear motion servo system with the nonlinear friction. The considered system plant included not only the variation of the mass of mover but also the friction change by the normal force. We also designed an adaptive controller with the mass estimator and the compensator by observing the variation of normal force. The effectiveness and system performances for the proposed control method in this paper show to improve than other control methods through numerical simulations.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.420-422
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• 1998

In this paper, we propose a position-speed control of servo system with a state estimator. And also we utilized two mass modelling in order to deals with real system accurately. The overall control system consists of two parts: the position-speed controller and state estimator. The Kalman filter applied as state - feedback controller is an optimal state estimator applied to a dynamic system that involves random perturbations and gives a linear,unbiased and minimun error variance recursive algorithm to estimate the unknown state optimally. Therefore we consider the error problem about the servo system modelling, the measurement noise at low-speed ranges a stochastic system, and implement a optimal state observer. Performance of the proposed state estimator are demonstrated by computer simulations.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.199-204
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• 2006

In this paper, we propose an adaptive control algorithm to improve the position accuracy and reduce the nonlinear friction effects for linear motion servo system. Especially, the considered system includes not only the variation of the mass of the mover but also the friction change by the normal force. To adapt to these problems, we designed the controller with the mass estimator and the compensator by observing the variation of normal force. Finally, the numerical simulation results are presented in order to show the effectiveness of the proposed method to improve the position accuracy compared to other control methods.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.43.2-43.2
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• 2015

The massive limit of black holes (BHs) is observed as present day ten billion solar masses. We search for observational signatures of BHs that become extremely massive (EMBHs, 1-10 billion solar masses). I will report on the evolution of active galactic nuclei (AGNs) through the growth of BH mass and their dust emission strength. First, we measured 26 EMBH masses of quasars at 1