• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.209-216
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• 2009

The accurate estimation of software development cost is important to a successful development in software engineering. This paper presents a software cost estimation method using a support vector machine. Support vector machine is one of the efficient techniques for classification, and it is the classification method of input data based on Maximum-Margin Hyperplane. But SVM has the problem of the selection of optimal parameters, because it is dependent on user's parameters. This paper selects optimized SVM parameters using advanced method, and estimates software development cost. The proposed approach outperform some recent results reported in the literature.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.12-15
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• 1996

In this paper, a new shape control law is derived as a result of introducing the parametric curve representation. This control alw is based on the estimation of the curve parameters corresponding to the target joint positions and the target tip position. Estimating target curve parameters makes it possible to find, easily, a simple shape control law by the Lyapunov design method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1305-1313
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• 1995

In this paper, we propose a robust estimation of camera parameters from image sequence for high quality video composition. We first establish correspondence of feature points between consecutive image fields. After the establishment, we formulate a nonlinear least-square data fitting problem. When the image sequence contains moving objects, and/or when the correspondence establishment is not successful for some feature points, we get bad observations, outliers. They should be properly eliminated for a good estimation. Thus, we propose an iterative algorithm for rejecting the outliers and fitting the camera parameters alternatively. We show the validity of the proposed method using computer generated data sets and real image sequeces.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1435-1451
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• 2011

In this paper the estimation of the parameters as well as survival and hazard functions are presented for the two-parameter Pareto distribution by using Bayesian and non-Bayesian approaches under upper record values. Maximum likelihood estimation (MLE) and interval estimation are derived for the parameters. Bayes estimators of reliability performances are obtained under symmetric (Squared error) and asymmetric (Linex and general entropy (GE)) losses, when two parameters have discrete and continuous priors, respectively. Finally, two numerical examples with real data set and simulated data, are presented to illustrate the proposed method. An algorithm is introduced to generate records data, then a simulation study is performed and different estimates results are compared.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.149-158
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• 2009

It is important to derive reliable parameter values in the measurement-based load model development of electric power systems. However parameter estimation tasks, in practice, often face the parameter identifiability issue; whether or not the model parameters can be estimated with a given input-output data set in reliable manner. This paper introduces concepts and practical definitions of the local identifiability of model parameters. A posteriori local identifiability is defined in the sense of nonlinear least squares. As numerical examples, local identifiability of third-order induction motor (IM) model and a Z-induction motor (Z-IM) model is studied. It is shown that parameter ill-conditioning can significantly affect on reliable parameter estimation task. Numerical studies show that local identifiability can be quite sensitive to input data and a given local solution. Finally, several countermeasures are proposed to overcome ill-conditioning problem in measurement-based load modeling.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.526-526
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• 2000

The present has a double objective. The first one is to compare and estimate sixteen continuous-time methods through the identificatiun of a system consisted with an RLC electrical circuit. These sixteen methods are classified into three groups that are the linear filters, the modulating functions and the integral methods. The second objective is to estimate directly the physical parameters of the RLC circuit, without resorting to a discrete-time model. The system is consisted of a coil with inductance L and resistance H, and of a capacitor with capacitance C. Having written the physical equations which describe the behavior of the system, the transfer function in where the initial conditions appear is given. These initial conditions should be taken into account during the parameter estimation phase, because they are inevitable within the framework of real signals. A physical interpretation of the identified models is tempted by the direct estimation of the physical parameters L and C. In conclusion, a classification of the studied methods is proposed.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.1-9
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• 2003

In this article, we consider a Bayesian estimation method for the geometric mean of $textsc{k}$ exponential parameters, Using the Tibshirani's orthogonal parameterization, we suggest an invariant prior distribution of the $textsc{k}$ parameters. It is seen that the prior, probability matching prior, is better than the uniform prior in the sense of correct frequentist coverage probability of the posterior quantile. Then a weighted Monte Carlo method is developed to approximate the posterior distribution of the mean. The method is easily implemented and provides posterior mean and HPD(Highest Posterior Density) interval for the geometric mean. A simulation study is given to illustrates the efficiency of the method.

• Geotechnical Engineering
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• v.8 no.4
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• pp.41-50
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• 1992

In general, the conceptual lumped-parameter groundwater flow model to predict the groundwater fluctuations in hillside slopes has unknown model parameters to be estimated from the known input -output data. The purpose of this study is to estimate the optimal model parameters of the groundwater flow model developed by authors. The Mazilnum A Posteriori( MAP) estimation method is utilized for this purpose and it is applied to a site which shows the typical landslide in Korea. The result of application shows tllat the 반AP estimation method can estimate the unknown parameters properly well. The groundwater model developed along with estimation technique applied in this paper will be used for assessing risk of landslides.

• Journal of Magnetics
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• v.19 no.2
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• pp.174-180
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• 2014

In this paper, a new and simple method is proposed to quickly estimate the induced electric field in the human child exposed to a 100 kHz-10 MHz magnetic field, for the sake of electromagnetic field (EMF) safety assessment. The quasi-static finite-difference time-domain (FDTD) method is used to calculate the induced electric fields in high resolution 3D human child models with various body size parameters, in order to derive the correction factor for the estimation equation. The calculations are repeated for various frequencies and incident angles of the magnetic field. Based on these calculation results, a new and simple estimation equation for the 99th percentile value of the body electric field is derived that depends on the body size parameters, and the incident magnetic field. The estimation errors were equal to or less than 5.1%, for all cases considered.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2545-2547
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• 2000

In this paper, a hybrid fuzzy controller using genetic algorithm based on parameter estimation mode to obtain optimal control parameter is presented. First, The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PID's output in steady state by a fuzzy variable, namely, membership function of weighting coefficient. Second, genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller utilizing the conventional methods for finding PID parameters and estimation mode of scaling factor. The algorithms estimates automatically the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules according to the rate of change and limitation condition of control input. Computer simulations are conducted to evaluate the performance of proposed hybrid fuzzy controller. ITAE, overshoot and rising time are used as a performance index of controller.