• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.729-734
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• 2008

Vibroarthrographic(VAG) signals, emitted by human knee joints, are non-stationary and multi-component in nature and time-frequency distributions(TFD) provide powerful means to analyze such signals. The objective of this paper is to classify VAG signals, generated during joint movement, into two groups(normal and patient group) using the characteristic parameters extracted by time-frequency transform, and to evaluate the classification accuracy. Noise within TFD was reduced by singular value decomposition and back-propagation neural network(BPNN) was used for classifying VAG signals. The characteristic parameters consist of the energy parameter, energy spread parameter, frequency parameter, frequency spread parameter by Wigner-Ville distribution and the amplitude of frequency distribution, the mean and the median frequency by fast Fourier transform. Totally 1408 segments(normal 1031, patient 377) were used for training and evaluating BPNN. As a result, the average value of the classification accuracy was 92.3(standard deviation ${\pm}0.9$)%. The proposed method was independent of clinical information, and showed good potential for non-invasive diagnosis and monitoring of joint disorders such as osteoarthritis and chondromalacia patella.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.531-534
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• 2004

In Recent, it became necessary to develope the technology about electrodeless fluorescent lamp according to demand of the electodeless fluorescent lamp system that used higher efficiency and advantage of long-lifetime. Especially, in the electordeless fluorescent lamp which used H-mode, efficiency of lamp is decided from matching parameter of antena and inverter. So it is of the utmost importance to design antena and inverter Therefore, this paper used a transformer principle for efficiency rising of electrodeless fluorescent lamp and interpreted an equivalent circuit, used an impedance analyzer in order to confirm a performance enhancement of lamp along design of antenna, and confirmed parameter characteristic of R, L, C, Z, Q-factor along a change of magnetic flux density. Also, this paper confirmed a luminance characteristic of electordeless lamp along parameter change with measuring optical characteristic along a change of magnetic flux density

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.455-460
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• 2006

This paper presents the convergency property of the Auxiliary Problem Principle when it is applied to large-scale Optimal Power Flow problems with Distributed or Parallel computation features. The key features and factors affecting the convergence ratio and solution stability of APP are also analyzed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.12
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• pp.37-42
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• 2007

Clock grid networks are now common in most high performance microprocessors. This paper presents a new effective modeling and simulation methodology for the clock grid using scattering parameter. It also shows the effect of wire width and grid size on the clock skew of the grid. The interconnection of the clock grid is modeled by RC passive elements. The results show that the error is within 10 % comparing to Hspice simulation results.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.15-17
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of nonlinear IIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate nonlinear IIR filter parameter using the genetic algorithm.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.502-504
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of FIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate FIR filter parameter using the genetic algorithm.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2525-2527
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• 2004

This paper classify using Adaptive Resonance Theory 1(ART1) as a vigilance parameter of pattern clustering algorithm. Inherent characteristics of the model are analyzed. In particular the vigilance parameter $\rho$ and its role in classification of patterns is examined. Our estimates show that the vigilance parameter as designed originally does not necessarily increase the number of categories with its value but can decrease also. This is against the claim of solving the stability-plasticity dilemma. However, we have proposed a modified vigilance parameter setting criterion which takes into account the problem of subset and superset patterns and stably categorizes arbitrarily many input patterns in one list presentation when the vigilance parameter is closer to one. And this paper goal is the input pattern cognition and classification using neural network.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.285-287
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• 1992

This paper considers the design of robust stabilizing controller of a linear time-invariant digital system subject to variations of parameter vector. For a given controller the radius of the largest stability hypersphere in this parameter space is calculated. This radius is a measure of the stability Margin of the closed-loop system. Based on this calculation a design procedure is proposed to robustify a given stabilizing controller. This algorithm iteratively enlarges the stability hypersphere in parameter space and can be used to design a controller to stabilize a plant subject to given ranges of parameter perturbations. These results are illustrated by an example.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1204-1209
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• 1990

This paper offers the theory and method for regression analysis of the regression model with operational parameter variables based on the fundamentals of mathematical statistics. Regression coefficients are usually constants related to the problem of regression analysis. This paper considers that regression coefficients are not constants but the functions of some operational parameter variables. This is a kind of method of two-step fitting regression model. The second part of this paper considers the experimental step numbers as recursive variables, the recursive identification with unknown operational parameter variables, which includes two recursive variables, is deduced. Then the optimization and the recursive identification are combined to obtain the sequential experiment optimum design with operational parameter variables. This paper also offers a fast recursive algorithm for a large number of sequential experiments.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.170-183
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• 2007

This paper presents a delay-dependent approach to robust filtering for linear parameter-varying (LPV) systems with discrete and distributed time-invariant delays in the states and outputs. It is assumed that the state-space matrices affinely depend on parameters that are measurable in real-time. Some new parameter-dependent delay-dependent stability conditions are established in terms of linear matrix inequalities (LMIs) such that the filtering process remains asymptotically stable and satisfies a prescribed $H_{\infty}$ performance level. Using polynomially parameter-dependent quadratic (PPDQ) functions and some Lagrange multiplier matrices, we establish the parameter-independent delay-dependent conditions with high precision under which the desired robust $H_{\infty}$ filters exist and derive the explicit expression of these filters. A numerical example is provided to demonstrate the validity of the proposed design approach.