• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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• pp.198-207
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• 2012

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCA-based method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated.

• Smart Structures and Systems
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• v.9 no.4
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• pp.373-392
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• 2012

Tracking control of systems with variable stiffness hysteresis using a gain-scheduled (GS) controller is developed in this paper. Variable stiffness hysteretic system is represented as quasi linear parameter dependent system with known bounds on parameters. Assuming that the parameters can be measured or estimated in real-time, a GS controller that ensures the performance and the stability of the closed-loop system over the entire range of parameter variation is designed. The proposed method is implemented on a spring-mass system which consists of a semi-active independently variable stiffness (SAIVS) device that exhibits hysteresis and precisely controllable stiffness change in real-time. The SAIVS system with variable stiffness hysteresis is represented as quasi linear parameter varying (LPV) system with two parameters: linear time-varying stiffness (parameter with slow variation rate) and stiffness of the friction-hysteresis (parameter with high variation rate). The proposed LPV-GS controller can accommodate both slow and fast varying parameter, which was not possible with the controllers proposed in the prior studies. Effectiveness of the proposed controller is demonstrated by comparing the results with a fixed robust $\mathcal{H}_{\infty}$ controller that assumes the parameter variation as an uncertainty. Superior performance of the LPV-GS over the robust $\mathcal{H}_{\infty}$ controller is demonstrated for varying stiffness hysteresis of SAIVS device and for different ranges of tracking displacements. The LPV-GS controller is capable of adapting to any parameter changes whereas the $\mathcal{H}_{\infty}$ controller is effective only when the system parameters are in the vicinity of the nominal plant parameters for which the controller is designed. The robust $\mathcal{H}_{\infty}$ controller becomes unstable under large parameter variations but the LPV-GS will ensure stability and guarantee the desired closed-loop performance.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.561-576
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• 2007

Five plotting positions are applied to the computation of probability weighted moments (PWM) on the parameters of the generalized logistic distribution. Over a range of parameter values with some finite sample sizes, the effects of five plotting positions are investigated via Monte Carlo simulation studies. Our simulation results indicate that the Landwehr plotting position frequently tends to document smaller biases than others in the location and scale parameter estimations. On the other hand, the Weibull plotting position often tends to cause larger biases than others. The plotting position (i - 0.35)/n seems to report smaller root mean square errors (RMSE) than other plotting positions in the negative shape parameter estimation under small samples. In comparison to the maximum likelihood (ML) method under the small sample, the PWM do not seem to be better than the ML estimators in the location and scale parameter estimations documenting larger RMSE. However, the PWM outperform the ML estimators in the shape parameter estimation when its magnitude is near zero. Sensitivity of right tail quantile estimation regarding five plotting positions is also examined, but superiority or inferiority of any plotting position is not observed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.2
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• pp.69-75
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• 2002

AVR parameter tuning for voltage control of power system generators has generally been performed with the analytic methods and the simulation methods, which mostly depend on off-line linear mathematical models of excitation control system. However, due to the nonlinear nature of excitation control system, excitation control system performance of the tuned Parameters using the above conventional tuning methods may not be appropriate for some operating conditions. This paper presents an AVR parameter tuning method using actual on-line data of the excitation control system with the parameter optimization technique. As this method utilizes on-line operating data of the target excitation control system not the mathematical model of the system, it can overcome the limitation of model uncertainty Problems in conventional method, and it can tune the AVR parameter set which gives desired performance at the operating conditions. For the verification of proposed tuning method, two case studies with scaled excitation systems and the real-time power system simulator are presented.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.5
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• pp.144-149
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• 2000

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.226-234
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• 2003

Put Many researcher have made much progress in studying an estimate for fracture probability of brittle materials. However, studies of the fracture probability for the elastic-plasticity have not been made yet. An estimate method for fracture probability which is grafted onto 2-parameter criterion and statistical probability analysis is not only introduced in this study, but also applied to the simple 2dimensional model and carbon steel piping to evaluate the effect of random variable.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.6 s.237
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• pp.795-803
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• 2005

This paper studies the observability of calibration system with a measurement operator. The calibration system needs a simple digital indicator to measure the mobile table movements with respect to the MC coordinate. However, it yields the concern about the poor parameter observability due to measuring only a part of the movements. We uses the QR-decomposition to find the optimal calibration configurations maximizing the linear independence of rows of an observation matrix. The number of identifiable parameter is examined by the rank of the observation matrix, which represents the parameter observability. The method is applied to a 6-axis MC with parallel tilting table and the calibration results are presented. These results verify that all necessary kinematic parameters are observable and the calibration system has robustness to the noise using optimal calibration configurations.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.40-45
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• 1999

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc.In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA (parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.423-428
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• 1998

This paper studies the regional identifiability of spatially-varying parameters in distributed parameter systems of hyperbolic type. Let Ω be a bounded domain of R$^n$and let Ωo be a subregion of the closed domain Ω. The distributed parameter systems having unknown parameters defined on Ω are described by the second order evolution equations in the filbert space L$^2$(Ω) and the observations are made on the subregion Ωo ⊂ Ω. The regional identifiability is formulated as the uniqueness of parameters by the identity of solutions on the subregion. Several regional identifiability results of the spatially-varying parameters of hyperbolic distributed parameter systems are established by means of the Riesz spectral representations.

• Journal of the Korean Mathematical Society
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• v.58 no.3
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• pp.723-740
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• 2021

Design and maintenance of large span roof structures require an analysis of their static and dynamic behavior depending on the physical parameters defining the structures. Therefore, it is highly desirable to estimate the parameters from observations of the system. In this paper we study the parameter estimation problem for damped shallow arches. We discuss both symmetric and non-symmetric shapes and loads, and provide theoretical and numerical studies of the model behavior. Our study of the behavior of such structures shows that it is greatly affected by the existence of critical parameters. A small change in such parameters causes a significant change in the model behavior. The presence of the critical parameters makes it challenging to obtain good estimation. We overcome this difficulty by presenting the Parameter Estimation Algorithm that identifies the unknown parameters sequentially. It is shown numerically that the algorithm achieves a successful parameter estimation for models defined by arbitrary parameters, including the critical ones.