• The Journal of the Acoustical Society of Korea
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• v.16 no.3E
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• pp.62-68
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• 1997

In this paper, a modified adaptive sign (MAS) algorithm based on a mixed norm error criterion is proposed. The mixed norm error criterion of be minimized is constructed as a combined convex function of the mean-absolute error and the mean-absolute error to the third power. A convergence analysis of the MAS algorithm is also presented. Under a set of mild assumptions, a set of nonlinear evolution equations that characterizes the statistical mean and mean-squared behavior of the algorithm is derived. Computed simulations are carried out to verify the validity of our derivations.

• Nonlinear Functional Analysis and Applications
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• v.27 no.4
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• pp.701-707
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• 2022

In this article, we put forward a new type of variational inclusion problem known as resolvent inclusion. An algorithm is given for approximating its solution. The convergence of the algorithm is explained with the help of an example and plots using Matlab.

• Kyungpook Mathematical Journal
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• v.55 no.4
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• pp.933-951
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• 2015

In this paper, we consider the problem of convergence of an iterative algorithm for a general system of variational inequalities, a nonexpansive mapping and an ${\eta}$-strictly pseudo-contractive mapping. Strong convergence theorems are established in the framework of real Banach spaces.

• Journal of applied mathematics & informatics
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• v.7 no.1
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• pp.215-222
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• 2000

A multigrid algorithm is developed for solving the one- dimensional initial boundary value problem. The numerical solutions of linear and nonlinear Burgers; equation for various initial conditions are studied. The stability conditions are derived by Von -Neumann analysis . Numerical results are presented.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1988.10a
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• pp.262-265
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• 1988

• Structural Engineering and Mechanics
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• v.68 no.5
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• pp.563-573
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• 2018

Temperature-induced responses, such as strains and displacements, are related to the boundary conditions. Therefore, it is required to determine the boundary conditions to establish a reliable bridge model for temperature-induced responses analysis. Particularly, bridge bearings usually present nonlinear behavior with an increase in load, and the nonlinear boundary conditions cause significant effect on temperature-induced responses. In this paper, the bridge nonlinear boundary conditions were simulated as bilinear translational or rotational springs, and the boundary parameters of the bilinear springs were identified based on the measured temperature-induced responses. First of all, the temperature-induced responses of a simply support beam with nonlinear translational and rotational springs subjected to various temperature loads were analyzed. The simulated temperature-induced strains and displacements were assumed as measured data. To identify the nonlinear translational and rotational boundary parameters of the bridge, the objective function based on the temperature-induced responses is then created, and the nonlinear boundary parameters were further identified by using the nonlinear least squares optimization algorithm. Then, a beam structure with nonlinear translational and rotational springs was simulated as a numerical example, and the nonlinear boundary parameters were identified based on the proposed method. The numerical results show that the proposed method can effectively identify the parameters of the nonlinear boundary conditions. Finally, the boundary parameters of a real arch bridge were identified based on the measured strain data and the proposed method. Since the bearings of the real bridge do not perform nonlinear behavior, only the linear boundary parameters of the bridge model were identified. Based on the bridge model and the identified boundary conditions, the temperature-induced strains were recalculated to compare with the measured strain data. The recalculated temperature-induced strains are in a good agreement with the real measured data.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.628-633
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• 1994

A new Riemannian geometric model for the controlled plant is proposed by imbedding the control vector space in the state space, so as to reduce the dimension of the model. This geometric model is derived by replacing the orthogonal straight coordinate axes on the state space of a linear system with the curvilinear coordinate axes. Therefore the integral manifold of the geometric model becomes homeomorphic to that of fictitious linear system. For the lower dimensional Riemannian geometric model, a nonlinear optimal regulator with a quadratic form performance index which contains the Riemannian metric tensor is designed. Since the integral manifold of the nonlinear regulator is determined to be homeomorphic to that of the linear regulator, it is expected that the basic properties of the linear regulator such as feedback structure, stability and robustness are to be reflected in those of the nonlinear regulator. To apply the above regulator theory to a real nonlinear plant, it is discussed how to distort the curvilinear coordinate axes on which a nonlinear plant behaves as a linear system. Consequently, a partial differential equation with respect to the homeomorphism is derived. Finally, the computational algorithm for the nonlinear optimal regulator is discussed and a numerical example is shown.

• Coupled systems mechanics
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• v.3 no.3
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• pp.267-289
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• 2014

A proper physical modeling of infilled building frame-foundation beam-soil mass interaction system is needed to predict more realistic and accurate structural behavior under static vertical loading. This is achieved via finite element method considering the superstructure, foundation and soil mass as a single integral compatible structural unit. The physical modelling is achieved via use of finite element method, which requires the use of variety of isoparametric elements with different degrees of freedom. The unbounded domain of the soil mass has been discretized with coupled finite-infinite elements to achieve computational economy. The nonlinearity of soil mass plays an important role in the redistribution of forces in the superstructure. The nonlinear behaviour of the soil mass is modeled using hyperbolic model. The incremental-iterative nonlinear solution algorithm has been adopted for carrying out the nonlinear elastic interaction analysis of a two-bay two-storey infilled building frame. The frame and the infill have been considered to behave in linear elastic manner, whereas the subsoil in nonlinear elastic manner. In this paper, the computational methodology adopted for nonlinear soil-structure interaction analysis of infilled frame-foundation-soil system has been presented.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.3
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• pp.129-138
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• 2002

The purpose of this study is to propose the nonlinear analysis method which combines the nonlinear incremental method with the layered method to solve the problems due to the geometric and the material nonlinearities. As numerical analysis models, the reinforced concrete simple beam and the steel arch frame are used to verify the algorithm of the proposed nonlinear method. The results are gotten from the computation procedures. According to the results of this study, the fracture pattern of the beam according to the ratio of tensile steel and the strength of the concrete and the steel can be estimated by the proposed method. Therefore, the load-deflection curve of structure can be, exactly, depicted by the proposed method. Also, the rupture load, the site and the depth of crack of the beam can analytically be checked by the proposed method. In this respect, the proposed method contributes for the solving the stability problem of the actual structure.

• 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.