• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.345-354
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• 2010

The Gamma Distribution is widely used in Engineering and Industrial applications. Estimation of parameters is revisited in the two-parameter Gamma distribution. The parameters are estimated by minimizing the likelihood ratios. A comparative study between the method of moments, the maximum likelihood method, the method of product spacings, and minimization of three different likelihood ratios is performed using simulation. For the scale parameter, the maximum likelihood estimate performs better and for the shape parameter, the product spacings estimate performs better. Among the three likelihood ratio statistics considered, the Anderson-Darling statistic has inferior performance compared to the Cramer-von-Misses statistic and the Kolmogorov-Smirnov statistic.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2305-2307
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• 2000

In this paper we propose a Neuro-Fuzzy modeling method using mGA for complex nonlinear system. mGA has more effective and adaptive structure than sGA with respect to using the changeable-length string. This paper suggest a new coding method for applying the model's input and output data to the number of optimul rules of fuzzy models and the structure and parameter identifications of membership function simultaneously. The proposed method realize optimal fuzzy inference system using the learning ability of Neural network. For fine-tune of the identified parameter by mGA, back-propagation algorithm used for optimulize the parameter of fuzzy set. The proposed fuzzy modeling method is applied to a nonlinear system to prove the superiority of the proposed approach through compare with ANFIS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.63-71
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• 2010

The existing reliability evaluation models which have already developed by the corporations are so various because of using Maximum Likelihood Method. The existing models are very complicated owing to using system designing methods. Therefore, it is very difficult to utilize the existing models in business fields of many corporations. The purposes of this paper are as follows: The first purpose is to study the simple estimated Parameter to be easily utilized in the business fields of the corporations. The second purpose is to testify the simplification of the developed Parameter of estimated method by comparing the developed reliability evaluation model with the existing reliability evaluation models which are used in the business fields of the corporations.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2252-2254
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• 2001

This paper proposes a algebraic parameter determination of MRAC (Model Reference Adaptive Control) controller using block Pulse functions and block Pulse function's differential operation. Generally, adaption is performed by solving differential equations which describe adaptive low for updating controller parameter. The proposes algorithm transforms differential equations into algebraic equation, which can be solved much more easily in a recursive manner. We believe that proposes methods are very attractive and proper for parameter estimation of MRAC controller on account of its simplicity and computational convergence.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.811-827
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• 2010

This paper deals with a density estimation method in binary choice models that can be regarded as a statistical inverse problem. We use an orthogonal basis to estimate density function and consider the choice of an appropriate truncation parameter to reflect the model complexity and the prediction accuracy. We propose a data-dependent rule to choose the truncation parameter in the context of binary choice models. A numerical simulation is provided to illustrate the performance of the proposed method.

• Journal of Power System Engineering
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• v.10 no.4
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• pp.159-164
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• 2006

In recent, the marine engine of a large size is being realized a lower speed, longer stroke and a small number of cylinders for the energy saving. Consequently the variation of rotational torque became larger than former days because of the longer delay-time in fuel oil injection process and an increased output per cylinder. It was necessary that algorithms have enough robustness to suppress the variation of the delay-time and the parameter perturbation. This paper shows the structure of hybrid F-NFC against the delay-time and the perturbation of engine parameter as modeling uncertainties, and the design of the robust speed controller by hybrid F-NFC for the engine. And, The Parameter values of linear equation are determined by RC-GA for F-NFS. The hybrid F-NFC is combined the F-NFC and PID controller for filling up each.

• Korean Journal of Optics and Photonics
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• v.2 no.4
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• pp.219-226
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• 1991

The phase and the amplitude of the photoacoustic signal were measured as a function of chopping frequency for several kinds of widely used thin metallic plates (stainless steel 304, brass, aluminum and copper) attached to plexiglass backing. The experimental data have been analyzed systematically by parameter estimation technique based on the two-layer model developed from Rosencwaig-Gersho (R-G) theory. Using this analysis, the values of thermal diffusivity and thermal effusivity of the materials have been determined.

• Journal of Drive and Control
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• v.15 no.4
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• pp.67-73
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• 2018

The paper deals with an approach to time domain simulation for closed end at the downstream of pipe, hydraulic lines terminating into a tank and series lines with change of cross sectional area. Time domain simulation of a fluid power systems containing hydraulic lines is very complex and difficult if the transfer functions consist of hyperbolic Bessel functions which is the case for the distributed parameter dissipative model. In this paper, the magnitudes and phases of the complex transfer functions of hydraulic lines are calculated, and the MATLAB Toolbox is used to formulate a rational polynomial approximation for these transfer functions in the frequency domain. The approximated transfer functions are accurate over a designated frequency range, and used to analyze the time domain response. This approach is usefully to simulate fluid power systems with hydraulic lines without to approximate the frequency dependent viscous friction.

• Advances in nano research
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• v.7 no.2
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• pp.99-108
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• 2019

Higher-order theories are very important to investigate the mechanical properties and behaviors of nanoscale structures. In this study, a free vibration behavior of SiNW resting on elastic foundation is investigated via Eringen's nonlocal elasticity theory. Silicon Nanowire (SiNW) is modeled as simply supported both ends and clamped-free Euler-Bernoulli beam. Pasternak two-parameter elastic foundation model is used as foundation. Finite element formulation is obtained nonlocal Euler-Bernoulli beam theory. First, shape function of the Euler-Bernoulli beam is gained and then Galerkin weighted residual method is applied to the governing equations to obtain the stiffness and mass matrices including the foundation parameters and small scale parameter. Frequency values of SiNW is examined according to foundation and small scale parameters and the results are given by tables and graphs. The effects of small scale parameter, boundary conditions, foundation parameters on frequencies are investigated.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.146-158
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• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.