• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.35.1-35
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• 2002

$\textbullet$ Sliding mode control (SMC) with nonlinear interpolation in variable boundary layer (VBL) is proposed $\textbullet$ A sigmoid function is used for nonlinear interpolation in VBL. $\textbullet$ The Parameter of the sigmoid function is tuned by fuzzy controller $\textbullet$ The choice of parameter for the sigmoid function is guided by FC. $\textbullet$ The parameter is continuously updated as boundary layer thickness varies. $\textbullet$ The proposed method hasbetter tracking performance than the conventional linear interpolation $\textbullet$ To demonstrate its performance the proposed control algorithm is applied to a nonlinear system.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.437-444
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have tried to enhance classification accuracy. Previous studies have shown that the classification technique based on wavelet transform is more effective than traditional techniques based on original pixel values, especially in complicated imagery. Various basis functions such as Haar, daubechies, coiflets and symlets are mainly used in 20 image processing based on wavelet transform. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we first computed the wavelet coefficients of satellite image using ten different basis functions, and then classified images. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis functions. The energy parameters of wavelet detail bands and overall accuracy are clearly correlated. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.555-560
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• 2010

One-dimensional approach to two-dimensional warranty data involves modeling us- age as a function of time. Iskandar (1993) suggests a simple linear model for usage. However, simple linear form of intensity function is of limited value to model the situa-tion where the intensity varies over time. In this study Weibull intensity is considered where the scale parameter is expressed in terms of different models. We will nd out how each parameter in the model a ects the warranty cost and which model gives a bigger number of failures within the two-dimensional warranty region.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.6
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• pp.921-928
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• 1990

A multivariable equalizer is designed which compensates transmission distortion for coaxial transmission line. The characteristic transfer function with bilateral parameter domain is approximated using the expansion method of orthonormal function and Gram Schmidt orthogonal function, but the parameter domain of approximated function is limited to unilateral. To solve that problem, Bode type transfer function is used to give bilateral variable characteristic to approximated function. The multivariable equalizer in this paper can be substituted for the current equalizer which consist of a fixed equalizer and a variable equalizer.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.47-64
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• 2020

In this paper, we derive some estimators of the scale parameter of the exponentiated half-logistic distribution based on the multiply Type-I hybrid censoring scheme. We assume that the shape parameter λ is known. We obtain the maximum likelihood estimator of the scale parameter σ. The scale parameter is estimated by approximating the given likelihood function using two different Taylor series expansions since the likelihood equation is not explicitly solved. We also obtain Bayes estimators using prior distribution. To obtain the Bayes estimators, we use the squared error loss function and general entropy loss function (shape parameter q = -0.5, 1.0). We also derive interval estimation such as the asymptotic confidence interval, the credible interval, and the highest posterior density interval. Finally, we compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. The average length of 95% intervals and the corresponding coverage probability are also obtained.

• Proceedings of the KIPE Conference
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• 2001.12a
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• pp.7-10
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• 2001

To obtain a high performance in a direct vector controlled induction machine, it is essential to correct estimation of rotor flux. The accuracy of flux observers for induction machines inherently depends on parameter sensitivity. This paper presents an analysis method for conventional flux observers using Parameter Sensitivity. The Parameter sensitivity is defined as the ratio of the percentage change in the system transfer function to the percentage change of the parameter variation. We define the ratio between real flux and estimated flux as the transfer function, and analyzed a parameter sensitivity of this transfer function.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.147-152
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• 2006

The current type amplifier is the device that is used for an actuator as the motor's torque controller. However, it is too difficult to select the parameter value that has the desired output because the current type amplifier's transfer function is too complex. This study concern about the design of the current type amplifier with the desired output. From the modeled transfer function of the current type amplifier, the optimal parameter values of the transfer function can be selected in order to have the desired output using the Real Coded Genetic Algorithm(RCGA). The real circuit is made with the selected parameter value. The step response of the real circuit is in good agreement with the desired step response.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.2
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• pp.157-160
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• 1999

We show the cumulants of a minimal sufficient statistics in k parameter natural exponential family by parameter function and partial parameter function. We nd the cumulants have some merits of central moments and general cumulants both. The first three cumulants are the central moments themselves and the fourth cumulant has the form related with kurtosis.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.4
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• pp.377-383
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• 2002

To obtain a high performance in a direct vector controlled induction machine, it is essential to obtain the current rotor flux. The accuracy of flux observers for induction machines inherently depends on parameter sensitivity. This paper proposes an analysis method for conventional flux observers using "parameter sensitivity". The "parameter sensitivity" is defined as the ratio of the percentage change in the system transfer function to the percentage change of the parameter variation. We define the ratio between real flux and estimated no as the transfer function, and analyze a parameter sensitivity of this transfer function. Practical verification is presented to conform the capabilities of the proposed analysis method.sed analysis method.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.178-187
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• 1999

In order to identify a transfer function model with noise, penalty function method has been widely used. In this method, estimation process for possible model parameters from low to higher order proceeds the model identification process. In this study, based on linear estimation method, a new approach unifying the estimation and the identification of ARMAX model is proposed. For the parameter estimation of a transfer function model with noise, linear estimation method by noise separation is suggested instead of nonlinear estimation method. The feasibility of the proposed model identification and estimation method is verified through simulations, namely by applying the method to time series model. In the case of time series model with noise, the proposed method successfully identifies the transfer function model with noise without going through model parameter identification process in advance. A new algorithm effectively achieving model identification and parameter estimation in unified frame has been proposed. This approach is different from the conventional method used for identification of ARMAX model which needs separate parameter estimation and model identification processes. The consistency and the accuracy of the proposed method has been verified through simulations.