• Structural Engineering and Mechanics
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• v.44 no.2
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• pp.219-238
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• 2012

Objective of this paper is to compare linear buckling analysis formulations, available in commercial finite element programs. Modern steel design codes, including Eurocode 3, make abundant use of linear buckling loads for calculation of slenderness, and of linear buckling modes, used as shapes of imperfections for nonlinear analyses. Experience has shown that the buckling mode shapes and the magnitude of buckling loads may differ, sometimes significantly, from one algorithm to another. Thus, three characteristic examples have been used in order to assess the linear buckling formulations available in the finite element programs ADINA and ABAQUS. Useful conclusions are drawn for selecting the appropriate algorithm and the proper reference load in order to obtain either the classical linear buckling load or a good approximation of the actual geometrically nonlinear buckling load.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.827-829
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• 1999

The identification of nonlinear system using Extended GMDH(EGMDH) is studied in this paper. The proposed EGMDH algorithm is based on GMDH(Group Method of Data handling) method and its structure is similar to Neural Networks. The each node of EGMDH structure utilizes several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. As the operating condition changes, the parameters of EGMDH will also change, so the proposed scheme by means of the EGMDH method is capable of adapting rapidly to the changing environment. The simulation result shows that the simple nonlinear process can be modeled reasonably well by the proposed method which are simple but efficient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.807-821
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• 1996

In this paper, we proposed a nonlinear hierarchical mtion estimation method. Generally, the conventional hierarchical motion estimation methods have been proposed for fast convergence and detection of large motions. But they have a common drawback that large error in motion estimation is propapated across motion discontinuities. This artifiact is due to the constriaint of motion continuity and the linear interpolation of motion vectors between hierarchical levels. In this paper, we propose an effective hierarchical motion estimation mechod that is robust to motion discontinuities. The proposed algorithm is based on the decomposition of the functional domain for optimizing the intra-level motion estimation functional. Also, we propose an inter-level nonlinear motion estimation equation rather than using the conventional linearprojection scheme of motion field. computer simulations with several test sequences show tht the proposed algorithm performs better than several conventional methods.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.215-221
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• 2003

The education system in this paper is used to demonstrate and educate the effects of electromagnetic induction. Placing an aluminum ring over the core and switching on AC source causes the ring to jump in the air due to induced currents in the ring producing a magnetic field opposed to that produced in the core. To control the position of the ring by only the current, it is to require nonlinear control algorithm and control board that is composed of photo sensors, decode circuit, computer communication, and power electronics circuit. This paper provides the development for education system in detail and the effects of dynamic neural networks for nonlinear control with on line is studied.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.431-435
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• 2001

The active structural control has emerged as structural safety of structures against natural loadings such as earthquake and wind loadings. Of many control algorithms, Sliding-Mode Control (SMC) can design both linear controller and nonlinear controller. The robustness against parameter variations as well as excitation uncertainties that is imparted to the SMC due to its nonlinear control action, could make SMC an attractive control algorithm when dealing with structures where the external excitation constitutes the main uncertainty in the system. This paper demonstrates experimentally the efficacy of the SMC algorithm based on the active mass driver system in reducing the response of seismically excited buildings. The SMC control strategy is verified with the experimental study on the one-story building model equipped with the active mass driver.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1212-1220
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• 2015

This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.

• Smart Structures and Systems
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• v.19 no.4
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• pp.403-413
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• 2017

The present article encompasses a nonlinear finite element (FE) and genetic algorithm (GA) based optimal vibration energy harvesting from nonprismatic piezo-laminated cantilever beams. Three cases of cross section profiles (such as linear, parabolic and cubic) are modelled to analyse the geometric nonlinear effects on the output responses such as displacement, voltage, and power. The simultaneous effects of taper ratios (such as breadth and height taper) on the output power are also studied. The FE based nonlinear dynamic equation of motion has been solved by an implicit integration method (i.e., Newmark method in conjunction with the Newton-Raphson method). Besides this, a real coded GA based constrained optimization scheme has also been proposed to determine the best set of design variables for optimal harvesting of power within the safe limits of beam stress and PZT breakdown voltage.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.7
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• pp.959-965
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• 2002

It is intended to develop an algorithm for dynamic simulation of a fast-acting solenoid valve. The coupled equations of electric, magnetic, and mechanical systems should be solved simultaneously in a transient nonlinear manner. The transient nonlinear electromagnetic field is analyzed by the Finite Element Method (FEM), which is coupled with nonlinear electronic circuitry. The dynamic movement of the solenoid valve is analyzed at every time step from the force balance acting on the plunger, which includes the electromagnetic force calculated from the Finite Element analysis as well as the elastic force by a spring and the hydrodynamic pressure force along the flow passage. Dynamic responses of the solenoid valves predicted by this algorithm agree well with the experimental results including bouncing effects.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.65-71
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• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.50-55
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• 1998

The GMDH(Group Method of Data Handling) is a useful data analysis technique for identification of nonlinear complex systems. Therefore, in this paper the application method of GMDH algorithm for modeling nonlinear dynamic systems is proposed. The identification of dynamic systems by using GMDH consists of applying a set of input/output data and computing the necessary coefficient set dynamically. Also, in this paper, by reducing sequentially the criterion which can adopt or reject the data, a method to prevent excessive computation that is a disadvantage of GMDH is proposed.