• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.75-106
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• 2008

In this paper, we derive necessary optimality conditions for a continuous programming problem in which both objective and constraint functions contain support functions and is, therefore, nondifferentiable. It is shown that under generalized invexity of functionals, Karush-Kuhn-Tucker type optimality conditions for the continuous programming problem are also sufficient. Using these optimality conditions, we construct dual problems of both Wolfe and Mond-Weir types and validate appropriate duality theorems under invexity and generalized invexity. A mixed type dual is also proposed and duality results are validated under generalized invexity. A special case which often occurs in mathematical programming is that in which the support function is the square root of a positive semidefinite quadratic form. Further, it is also pointed out that our results can be considered as dynamic generalizations of those of (static) nonlinear programming with support functions recently incorporated in the literature.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.7 no.2
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• pp.3-10
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• 1997

Correlation immune functions can be used not only as filter functions or nonlinear combiners in stream ciphers but also as a primitive logic in block cipher. In this paper, we suggest a construction method of correlation immune functions. Using this method, we find lower and upper bound of the cardinality of the correlation immune functions. This result improves Mitchell's result and Yang-Guo's result.

• Nonlinear Functional Analysis and Applications
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• v.28 no.2
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• pp.589-599
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• 2023

The object of this study is to introduce a new subclass of univalent analytic functions on the open unit disk. This subclass is created by utilizing univalent analytic functions with negative coefficients. We first explore the specific properties that functions in this subclass must possess before examining their coefficient characterization. By applying this approach, we observe several fascinating features, including coefficient approximations, growth and distortion theorems, extreme points and a demonstration of the radius of starlikeness and convexity for functions belonging to this subclass.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.28-34
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• 2003

In this paper, the problem of designing an adaptive observer for a class of nonlinear systems with linear unknown parameters is studied. The nonlinear system to be considered consists of two blocks, only one of which has measurable states. Assuming the minimum-phase property of the error dynamics obtained after a change of coordinates and imposing some conditions on the functions multiplied by unknown parameters, an adaptive observer is constructed using an existing observer design method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.2
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• pp.193-199
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• 1988

The forced vibrations of nondissipative nonlinear two-degree-of-freedom system, subjected to periodic forcing functions, are investigated by use of the method of slowly changing phase and amplitude. The first order differential equations are derived for nonrationally solutions and the coupled nonlinear algebraic equations for stationary solutions. Through investigating the response curves of the system, which are obtained numerically by using Newton-Raphson method, it is found that the resonances can occur at more than the number of degree-of-freedom of the system depending on the relation between the nonlinear spring parameters, which has no counterpart in linear systems.

• Structural Engineering and Mechanics
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• v.43 no.6
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• pp.795-821
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• 2012

The size and topology of geometrically nonlinear dome structures are optimized thereby minimizing both its entire weight & joint (node) displacements and maximizing load-carrying capacity. Design constraints are implemented from provisions of American Petroleum Institute specification (API RP2A-LRFD). In accordance with the proposed design constraints, the member responses computed by use of arc-length technique as a nonlinear structural analysis method are checked at each load increment. Thus, a penalization process utilized for inclusion of unfeasible designations to genetic search is correspondingly neglected. In order to solve this complex design optimization problem with multiple objective functions, Non-dominated Sorting Genetic Algorithm II (NSGA II) approach is employed as a multi-objective optimization tool. Furthermore, the flexibility of proposed optimization is enhanced thereby integrating an automatic dome generating tool. Thus, it is possible to generate three distinct sphere-shaped dome configurations with varying topologies. It is demonstrated that the inclusion of brace (diagonal) members into the geometrical configuration of dome structure provides a weight-saving dome designation with higher load-carrying capacity. The proposed optimization approach is recommended for the design optimization of geometrically nonlinear dome structures.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.11
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• pp.920-929
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• 2005

This paper presents controller design method for nonlinear radar gimbal system with parameter uncertainty and time delay. In order to consider nonlinearity of gimbal bearing frictional torque, we firstly represent fuzzy model for the nonlinear gimbal system, which is achieved by fuzzy combination of linear models through nonlinear fuzzy membership functions. And secondly we propose a delay dependent fuzzy $H_\infty$ controller design method for the delayed fuzzy model with parameter uncertainty and design radar gimbal controller. The designed controller stabilize gimbal system and guarantee $H_\infty$ performance. A computer simulation is given to illustrate stabilized control performances of the designed controller.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.768-776
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• 2011

This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.12
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• pp.1916-1924
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• 1990

In this paper attenuation effect on the measurement and the tomography of nonlinear parameter is discussed. We perform computer simulation with the method using harmonic components and the method using secondary wave components, and then estimate attenuation effect through the results and compare two measurement techniques. According to simulation result the attenuation effect is more intensive as large n and \ulcorner, and the degree of the attenuation effect is represented as error functions. In the aspect of measuremnet techniques, the method using secondary wave components is more insensitive to attenuation effect than the method using harmonic compnents. We obtain the same result in the nonlinear tomography, and show that the attenuation compensive filter is required because the whole tomogram is affected by frequency dependent attenuation(or nonlinear attenuation)

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.