• Journal of Korean Society of Steel Construction
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• v.14 no.2
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• pp.339-348
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• 2002

The study developed an automatic design method of steel frames which uses nonlinear analysis. The geometric nonlinearity was considered using stability functions. Likewise, the transverse shear deformation effect in a beam-column was explained. A direct search method was used as an automatic design technique. The unit value of each part was evaluated using LRFD interaction equation. The member with the largest unit value was replaced one by one with an adjacent larger member selected from the database. The weight of the steel frame was considered as an objective function. On the other hand, load-carrying capacities, deflections, inter-story drifts, and ductility requirement were used as constraint functions. Case studies of a two-dimensional and a three-dimensional two-story frames were presented.

• Interaction and multiscale mechanics
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• v.6 no.2
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• pp.237-255
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• 2013

In this paper, a meshfree-enriched finite element method (ME-FEM) is introduced for the large deformation analysis of nonlinear path-dependent problems involving contact. In linear ME-FEM, the element formulation is established by introducing a meshfree convex approximation into the linear triangular element in 2D and linear tetrahedron element in 3D along with an enriched meshfree node. In nonlinear formulation, the area-weighted smoothing scheme for deformation gradient is then developed in conjunction with the meshfree-enriched element interpolation functions to yield a discrete divergence-free property at the integration points, which is essential to enhance the stress calculation in the stage of plastic deformation. A modified variational formulation using the smoothed deformation gradient is developed for path-dependent material analysis. In the industrial metal forming problems, the mortar contact algorithm is implemented in the explicit formulation. Since the meshfree-enriched element shape functions are constructed using the meshfree convex approximation, they pose the desired Kronecker-delta property at the element edge thus requires no special treatments in the enforcement of essential boundary condition as well as the contact conditions. As a result, this approach can be easily incorporated into a conventional displacement-based finite element code. Two elasto-plastic problems are studied and the numerical results indicated that ME-FEM is capable of delivering a volumetric locking-free and pressure oscillation-free solutions for the large deformation problems in metal forming analysis.

• Ocean Systems Engineering
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• v.2 no.3
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• pp.189-203
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• 2012

New experiments focusing on the evolution characteristics of nonlinear wave trains were conducted in a large wave flume. A series of wave trains with added sidebands, varying initial steepness, perturbed amplitudes and frequencies, were physically generated in a long wave flume. The experimental results show that the increasing wave steepness, increases the speed of sidebands growth. To study the frequency and phase modulation, the Morlet wavelet transform is adopted to extract the instantaneous frequency of wave trains and the phase functions of each wave component. From the instantaneous frequency, there are local frequency downshifts, even an effective frequency downshift was not observed. The frequency modulation increases with an increase in amplitude modulation, and abrupt changes of instantaneous frequencies occur at the peak modulation. The wrapped phase functions show that in the early stage of the modulation, the phase of the upper sideband first diverges from that of the carrier waves. However, at the later stage, the discrepancy phase from the carrier wave transformed to the lower sideband. The phase deviations appear in the front of the envelope's peaks. Furthermore, the evolution of the instantaneous frequency exhibits an approximate recurrence-type for the experiment with large imposed sidebands, even when the corresponding recurrence is not observed in the Fourier spectrum.

• Journal of Ship and Ocean Technology
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• v.9 no.4
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• pp.35-46
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• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.4
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• pp.179-189
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• 2021

Conditional spectra (CS) are applied to the seismic fragility assessment of a nuclear power plant (NPP) containment building for comparison with a relevant conventional uniform hazard response spectrum (UHRS). Three different control frequencies are considered in developing conditional spectra. The contribution of diverse magnitudes and epicentral distances is identified from deaggregation for the UHRS at a control frequency and incorporated into the conditional spectra. A total of 30 ground motion records are selected and scaled to simulate the probability distribution of each conditional spectra, respectively. A set of lumped mass stick models for the containment building are built considering nonlinear bending and shear deformation and uncertainty in modeling parameters using the Latin hypercube sampling technique. Incremental dynamic analysis is conducted for different seismic input models in order to estimate seismic fragility functions. The seismic fragility functions and high confidence of low probability of failure (HCLPF) are calculated for different seismic input models and analyzed comparatively.

• Journal of Korean Society of Steel Construction
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• v.15 no.2
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• pp.197-206
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• 2003

The optimal design method in cooperation with a nonlinear elastic analysis method was presented. The proposed nonlinear elastic method overcame the drawback of the conventional LRFD method this approximately accounts for the nonlinear effect caused by using the moment amplification factors of and. The genetic algorithm uses a procedure based on the Darwinian notions of the survival of the fittest, where selection, crossover, and mutation operators are used to look for high performance among the sections of the database. They satisfy constraint functions and give the lightest weight to the structure. The objective function was set to the total weight of the steel structure. The constraint functions were load-carrying capacities, serviceability, and ductility requirement. Case studies for a two-dimensional frame, a three-dimensional frame, and a three-dimensional steel arch bridge were likewise presented.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.51-57
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• 1999

In this paper, we deal with the problem of controlling an unknown nonlinear dynamical system, using wavelet network. Accurate control of the nonlinear systems depends critically on the accuracy and efficiency of the function approximator used to approximate the function. Thus, we use wavelet network which shows high capability of approximating the functions and includes the free-selection of basis functions for the control of the nonlinear system. We find the dilation and translation that are wavelet network parameters by analyzing the time-frequency characteristics of the controller's input to construct an initial adaptive wavelet network controller. Then, weights is adjusted by the adaptive law based on the Lyapunov stability theory. We apply this direct adaptive wavelet network controller to control the inverted pendulum system which is an nonlinear system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.251-268
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• 1996

In this paper, a higher-order feedforward neural network called ridge polynomial network (RPN) which shows good approximation capability for nonlnear continuous functions defined on compact subsets in multi-dimensional Euclidean spaces, is presented. This network provides more efficient and regular structure as compared to ordinary higher-order feedforward networks based on Gabor-Kolmogrov polynomial expansions, while maintating their fast learning property. the ridge polynomial network is a generalization of the pi-sigma network (PSN) and uses a specialform of ridge polynomials. It is shown that any multivariate polynomial can be exactly represented in this form, and thus realized by a RPN. The approximation capability of the RPNs for arbitrary continuous functions is shown by this representation theorem and the classical weierstrass polynomial approximation theorem. The RPN provides a natural mechanism for incremental function approximation based on learning algorithm of the PSN. Simulation results on several applications such as multivariate function approximation and pattern classification assert nonlinear approximation capability of the RPN.

• Journal of the Korean Mathematical Society
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• v.41 no.5
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• pp.821-864
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• 2004

Both parametric and parameter-free necessary and sufficient optimality conditions are established for a class of nondiffer-entiable nonconvex optimal control problems with generalized fractional objective functions, linear dynamics, and nonlinear inequality constraints on both the state and control variables. Based on these optimality results, ten Wolfe-type parametric and parameter-free duality models are formulated and weak, strong, and strict converse duality theorems are proved. These duality results contain, as special cases, similar results for minmax fractional optimal control problems involving square roots of positive semi definite quadratic forms, and for optimal control problems with fractional, discrete max, and conventional objective functions, which are particular cases of the main problem considered in this paper. The duality models presented here contain various extensions of a number of existing duality formulations for convex control problems, and subsume continuous-time generalizations of a great variety of similar dual problems investigated previously in the area of finite-dimensional nonlinear programming.

• 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.