• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• Journal of Mechanical Science and Technology
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• v.20 no.8
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• pp.1125-1138
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• 2006

The optimal design of the squeeze film damper (SFD) for rotor system has been studied in previous researches. However, these researches have not been considering jumping or nonlinear phenomena of a rotor system with SFD. This paper represents an optimization technique for linear and nonlinear response of a simple rotor system with SFDs by using a hybrid GA-SA algorithm which combined enhanced genetic algorithm (GA) with simulated annealing algorithm (SA). The damper design parameters are the radius, length and radial clearance of the damper. The objective function is to minimize the transmitted load between SFD and foundation at the operating and critical speeds of the rotor system with SFD which has linear and nonlinear unbalance responses. The numerical results show that the transmitted load of the SFD is greatly reduced in linear and nonlinear responses for the rotor system.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.217-221
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• 2007

This paper deals with the robust nonlinear controller design using output feedback for a Chua circuit which is one of the well-known nonlinear models. First, an exosystem for reference signal tracking is defined, and error dynamic equations are derived from the differentiation of the output tracking error equation. The normal sliding surface is modified using the integral type servo compensator. The parameters in the equations of the modified sliding surface and servo compensator are determined by using the Hurwitz condition of stability. Especially the error signals can't be obtained directly from the output because all parameters are assumed unknown. So instead, a high gain observer is designed. From this estimated error signals, a stabilizing controller is designed. Simulation is done for demonstrating the effectiveness of the suggested algorithm.

• Journal of applied mathematics & informatics
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• v.2 no.1
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• pp.3-10
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• 1995

Nonlinear hyperbolic differenctial equations have been a subject of intense research in the field of gas dynamics due to many engineering problems associated with high-speed airplanes missiles materials processing etc. Recently phenomena known from gas dy-namics are found to occur also in the microeletronic devices such as MOSFET. Here a few interesting mathematical problems are presented along with future areas of research.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.203-217
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• 1994

In this paper we develop an exponentialization procedure for the modelling of open FMS networks with general processing time at each station and limited buffer size. By imposing a reasonable assumption on the solution set, the nonlinear equation system for the exponentialization procedure is formulated as a variational inequality problem and the solution existence is examined. The efficient algorithm for the nonlinear equation system is developed using linearized Jacobi approximation method.

• Journal of the Korean Graphic Arts Communication Society
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• v.14 no.2
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• pp.1-20
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• 1996

we describes a method for realizing the color conversion from the tristimulus (X, Y, Z) values to the print ink signals (C, M, Y) by using neural networks. The realized nonlinear color conversion system consists of two hidden layers those have seventeen nodes. We determined the C, M, Y values of the input control signals to compensate the printer nonlinearity of real systems. Experimental results showed that the described method is useful and valid to realized the nonlinear color conversion.

• Proceedings of the Optical Society of Korea Conference
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• 1991.07a
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• pp.5-8
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• 1991

New nonliner organic materials have been developed for all-optical signal processing. The organic materials possess many interesting features for this purpose. Unlike inorganic molecules the delocalized $\pi$-electron distribution and intramolecular charge transfer mevhanism allows certain organic molecules to respond highly anharmonically to an external field. In the present paper the origins of nonlinear phenomena, advantages of orgnic materials and structures of organic devices will be discussed.

• Journal of IKEEE
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• v.11 no.4
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• pp.213-218
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• 2007

We consider a web transport system. The objective of this paper is to design the output feedback controller such that the controller can track a desired tension and processing speed on web transport system. We propose the new design method using observer and feedback linearization technique. The proposed method use a nonlinear feedback to transform to linear system and high gain observer to estimate the state value. We show that the proposed controller can achieve the control object using only output. We show a performance of controller via the simulation.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.89-96
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• 2015

In this paper, time-frequency analysis algorithms, empirical mode decomposition and local mean decomposition, are reviewed and their applications to nonlinear and nonstationary real-world data are discussed. In addition, their generic extensions to complex domain are addressed for the analysis of multichannel data. Simulations of these algorithms on synthetic data illustrate the fundamental structure of the algorithms and how they are designed for the analysis of nonlinear and nonstationary data. Applications of the complex version of the algorithms to the synthetic data also demonstrate the benefit of the algorithms for the accurate frequency decomposition of multichannel data.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.455-468
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• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.