• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.2
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• pp.63-71
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• 2005

In the field of nuclear reactor safety study, common cause failures (CCFs) became significant contributors to system failure probability and core damage frequency in most Probabilistic risk assessments. However, it is hard to estimate the reliability of such a system, because of the dependency of components caused by CCFs. In order to analyze the system, we propose an analytic method that can find the parameters with lack of raw data. This study adopts the shock model in which the failure probability increases as the shock is cumulated. We use two-step Expectation and Maximization (EM) algorithm to find the unknown parameters. In order to verify the analysis result, we perform the simulation under same environment. This approach might be helpful to build the defensive strategy for the CCFs.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.389-391
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• 1994

This paper proposes an approach to control the current amplitude and phase simultaneously. To do this, variable structure control and adaptive parameter estimation arc applied to the current control of a single-phase PWM inverter with unknown R-L series load. The load parameters, R and L, are estimated by using the recursive least square method and these parameters are used to adjust the feedback gains of control input. The inverter system is modelled in a 2nd-order system by treating load current variation caused by inductive component as a disturbance. Simulation and experiment based on the 2nd -order model are done and the results show good dynamic response and low THD.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.8
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• pp.1206-1212
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• 2001

The kinematic analysis of Stewart platform manipulator(SPM) is carried out in order to reduce the calculation time for its forward kinematic solution when the iterative numerical method is employed. The kinematic equations for three substructures of the 6-3 SPM are newly derived by introducing Denavit-Hartenberg link parameters and using kinematic constraints associated with the SPM and substructure kinematics. It is shown that the forward kinematics can be easily solved from three nonlinear equations with three unknown variables only, leading to a great reduction in calculation time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1114-1119
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• 1995

This paper presents the development of estimation model and control method based on the new computer vision. This proposed control method is accomplished using a sequential estimation scheme that permits placement of the rigid body in each of the two-dimensional image planes of monitoring cameras. Estimation model with six parameters is developed based on a model that generalizes known 4-axis scara robot kinematics to accommodate unknown relative camera position and orientation, etc. Based on the estimated parameters,depending on each camers the joint angle of robot is estimated by the iteration method. The method is tested experimentally in two ways, the estimation model test and a three-dimensional rigid body placement task. Three results show that control scheme used is precise and robust. This feature can open the door to a range of application of multi-axis robot such as assembly and welding.

• 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.

• Structural Monitoring and Maintenance
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• v.7 no.3
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• pp.195-213
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• 2020

In this paper an extension to the method for the identification of mechanical parameters of nonlinear systems proposed in Breccolotti and Materazzi (2007) for MDoF systems is presented. It can be used for damage identification purposes when damage modifies the linear characteristics of the investigated structure. It is based on the following two main features: the solution of the Fokker-Planck equation that describes the response probabilistic properties of the system when it is excited by external Gaussian loads; and a model updating technique that minimizes the differences between the response of the actual system and that of a parametric system used to identify the unknown parameters. Numerical analysis, that simulate virtual experimental tests, are used in the paper to show the capabilities of the method and to analyse the conditions required for its application.

• Structural Engineering and Mechanics
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• v.68 no.1
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• pp.39-51
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• 2018

In this study, a frequency domain model updating method is presented using power spectral density (PSD) data. It uses the sensitivity of PSD function with respect to the unknown structural parameters through a decomposed form of transfer function. The stiffness parameters are captured with high accuracy through solving the sensitivity equations utilizing the least square approach. Using numerically noise polluted data, the model updating results of a truss model prove robustness of the method against measurement and mass modelling errors. Results prove the capabilities of the method for parameter estimation using highly noise polluted data of low ranges of excitation frequency.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.738-741
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• 2004

The overall performance of AC servo system is greatly affected by the uncertainties of unpredictable mechanical parameter variations and external load disturbances. Therefore, to compensate this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an online identification method of mechanical parameters/load disturbances for AC servo system using Support Vector Regression (SVR). The proposed methodology advocates analytic parameter regression directly from the training data, rather than adaptive controller and observer approaches commonly used in motion control applications. The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with large measurement noise.

• The Korean Journal of Applied Statistics
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• v.21 no.5
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• pp.755-763
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• 2008

This paper approaches the problem of option pricing in an incomplete market, where the underlying asset price process follows a compound Poisson model. We assume that the price process follows a compound Poisson model under an equivalent martingale measure and it converges weakly to the Black-Scholes model. First, we express the option price as the expectation of the discounted payoff and expand it at the Black-Scholes price to obtain a pricing formula with three unknown parameters. Then we estimate those parameters using the market option data. This method can use the option data on the same stock with different expiration dates and different strike prices.

• 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.