• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.1
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• pp.20-26
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• 2011

A weakly nonlinear seakeeping methodology for predicting motions and loads is presented in this paper. This methodology assumes linear radiation and diffraction forces, calculated in the frequency domain, and fully nonlinear Froude-Krylov and hydrostatic forces, evaluated in the time domain. The particular approach employed here allows to overcome numerical problems connected to the determination of the impulse response functions. The procedure is divided into three consecutive steps: evaluation of dynamic sinkage and trim in calm water that can significantly influence the final results, a linear seakeeping analysis in the frequency domain and a weakly nonlinear simulation. The first two steps are performed employing a three-dimensional Rankine panel method. Nonlinear Froude-Krylov and hydrostatic forces are computed in the time domain by pressure integration on the actual wetted surface at each time step. Although nonlinear forces are evaluated into the time domain, the equations of motion are solved in the frequency domain iteratively passing from the frequency to the time domain until convergence. The containership S175 is employed as a test case for evaluating the capability of this methodology to correctly predict the nonlinear behavior related to wave induced motions and loads in head seas; numerical results are compared with experimental data provided in literature.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.468-474
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• 2005

Brain-machine interface (BMI) based on neuronal spike trains is regarded as one of the most promising means to restore basic body functions of severely paralyzed patients. The spike train decoding algorithm, which extracts underlying information of neuronal signals, is essential for the BMI. Previous studies report that a linear filter is effective for this purpose and there is no noteworthy gain from the use of nonlinear mapping algorithms, in spite of the fact that neuronal encoding process is obviously nonlinear. We designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR), and show that the nonlinear algorithms are superior in general. The MLP often showed unsatisfactory performance especially when it is carelessly trained. The nonlinear SVR showed the highest performance. This may be due to the superiority of the SVR in training and generalization. The advantage of using nonlinear algorithms were more profound for the cases when there are false-positive/negative errors in spike trains.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.374-383
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• 2004

This paper presents a neural network adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV) using adaptive backstepping method. In general, the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori because of variations of these coefficients with different operating conditions. In this paper, the smooth unknown dynamics of a vehicle is approximated by a neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to be unbounded, although they still satisfy certain growth conditions characterized by 'bounding functions' composed of known functions multiplied by unknown constants. Under certain relaxed assumptions pertaining to the control gain functions, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.

• Industrial Engineering and Management Systems
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• v.9 no.1
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• pp.10-19
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• 2010

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

• Journal of applied mathematics & informatics
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• v.32 no.1_2
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• pp.211-225
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• 2014

A new class of smoothing functions is introduced in this paper, which includes some important smoothing complementarity functions as its special cases. Based on this new smoothing function, we proposed a smoothing Newton method. Our algorithm needs only to solve one linear system of equations. Without requiring the nonemptyness and boundedness of the solution set, the proposed algorithm is proved to be globally convergent. Numerical results indicate that the smoothing Newton method based on the new proposed class of smoothing functions with ${\theta}{\in}(0,1)$ seems to have better numerical performance than those based on some other important smoothing functions, which also demonstrate that our algorithm is promising.

• 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.