• Journal of Power System Engineering
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• v.12 no.2
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• pp.62-70
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• 2008

The paper made a description of the method for numerical analysis and modeling of a proportional pressure control valve by bondgraph. The valve is a three port pressure regulator valve, consists of two subsystems; a proportional solenoid and a spool assembly. A purpose of this study is to analysis the dynamic characteristics of the valve using bondgraph method and to verified results that each of parameters has an effect on modeling. It considered the effect which the presence of solenoid, flow coefficient and non-linearity of resistance causes in the valve modeling. In particular, it is analyzed the effect that the solenoid interacted with modeling results and characteristics of the nonlinear resistance through orifice on the supply and discharge side of valve. Thus this paper described method to present nonlinear characteristics by bondgraph modeling method, so that we could know easily result that each parameters has an effect on the modeling.

• Environmental Engineering Research
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• v.14 no.2
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• Computers and Concrete
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• v.33 no.6
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• pp.725-738
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• 2024

This study presents a macro-modeling procedure for nonlinear finite element analysis of reinforced and prestressed concrete panels under blast loading. The analysis procedure treats cracked concrete as an orthotropic material based on a smeared rotating crack model within the context of total-load secant stiffness-based formulation. A direct time integration method compatible with the analysis formulation is adapted to solve the dynamic equation of motion. Considerations are made to account for strain rate effects. The analysis procedure is verified by modeling 14 blast tests from various sources reported in the literature including a blast simulation contest. The analysis results are compared against those obtained from experiments, simplified single-degree-of-freedom (SDOF) methods, and sophisticated hydrocodes. It is demonstrated that the smeared crack macro-modeling approach is a viable alternative analysis procedure that gives more information about the structural behavior than SDOF methods, but does not require detailed micro-modeling and extensive material characterization typically needed with hydrocodes.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.371-374
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• 1999

This paper present the nonlinear control and the Lyapunov analysis of the nonlinear electro-hydraulic system for steering control of UCT. Electro-hydraulic system itself has the high nonlinearities arisen from the nonlinear characteristics of the pressure-fluid flow in valve and friction in cylinder. These nonlinearities are unmodeled terms in the transfer function. This paper presents the system modeling, analysis of stability based on the Lyapunov function and simulation of the nonlinear hydraulic servo system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.227-233
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• 1996

The Inverted Pendulum has been one of most popular nonlinear dynamic systems for the exploration of control techniques. This paper presents a new linear optimal control techniques and nonlinear neural network learning methods. The multiayered neural networks are used to add nonlinear effects on the linear optimal regulator(LQR). The new regulator can compensate nonlinear system uncertainties that are not considered in the LQR design, and can tolerated a wider range of uncertainties than the LQR alone. The new regulator has two neural networks for modeling and control. The neural network for modeling is used to obtain a more accurate model than the given mathematical equations. The neural network for control is used to overcome deficiencies by adding corrections to the linear coefficients of the LQR and by adding nonlinear effects on the LQR. Computer simulations are performed to show the applicability and a more robust regulator than the LQR alone.

• Smart Structures and Systems
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• v.29 no.5
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• pp.693-703
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• 2022

This study investigates an effective approach to stabilize nonlinear systems. To ensure the asymptotic nonlinear stability in nonlinear discrete-time systems, the present study presents controller for an EBA (Evolved Bat Algorithm) NN (fuzzy neural network) in the algorithm. In fuzzy evolved NN modeling, the auxiliary circuit with high frequency LDI (linear differential inclusions) and NN model representation is developed for the nonlinear arbitrary dynamics. An example is utilized to demonstrate the system more robust compared with traditional control systems.

• Structural Engineering and Mechanics
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• v.46 no.2
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• pp.213-229
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• 2013

Although performance based assessment procedures are mainly developed for reinforced concrete and steel buildings, URM (Unreinforced Masonry) buildings occupy significant portion of buildings in earthquake prone areas of the world as well as in IRAN. Variability of material properties, non-engineered nature of the construction and difficulties in structural analysis of masonry walls make analysis of URM buildings challenging. Despite sophisticated finite element models satisfy the modeling requirements, extensive experimental data for definition of material behavior and high computational resources are needed. Recently, nonlinear equivalent frame models which are developed assigning lumped plastic hinges to isotropic and homogenous equivalent frame elements are used for nonlinear modeling of URM buildings. The equivalent frame models are not novel for the analysis of masonry structures, but the actual potentialities have not yet been completely studied, particularly for non-linear applications. In the present paper an effective tool for the non-linear static analysis of 2D masonry walls is presented. The work presented in this study is about performance assessment of unreinforced brick masonry buildings through nonlinear equivalent frame modeling technique. Reliability of the proposed models is tested with a reversed cyclic experiment conducted on a full scale, two-story URM building at the University of Pavia. The pushover curves were found to provide good agreement with the experimental backbone curves. Furthermore, the results of analysis show that EFM (Equivalent Frame Model) with Dolce RO (rigid offset zone) and shell element have good agreement with finite element software and experimental results.

• Advances in Computational Design
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• v.8 no.2
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• pp.97-112
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• 2023

The Finite Element (FE) modeling of Reinforced Concrete (RC) under seismic loading has a sensitive impact in terms of getting good contribution compared to experimental results. Several idealized model types for simulating the nonlinear response have been developed based on the plasticity distribution alone the model. The Continuum Models are the most used category of modeling, to understand the seismic behavior of structural elements in terms of their components, cracking patterns, hysteretic response, and failure mechanisms. However, the material modeling, contact and nonlinear analysis strategy are highly complex due to the joint operation of concrete and steel. This paper presents a numerical simulation of a chosen RC column under monotonic and cyclic loading using the FE Abaqus, to assessthe hysteretic response and failure mechanisms in the RC columns, where the perfect bonding option is used for the contact between concrete and steel. While results of the numerical study under cyclic loading compared to experimental tests might be unsuccessful due to the lack of bond-slip modeling. The monotonic loading shows a good estimation of the envelope response and deformation components. In addition, this work further demonstrates the advantage and efficiency of the damage distributions since the obtained damage distributions fit the expected results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1220-1228
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• 2010

In this paper, we suggest a scheme to develop an efficient discrete nonlinear model based on polynomial structure for a RF power amplifier(PA). We describe a procedure to extract a discrete nonlinear model such as Taylor series or memory polynomial by sampling the input and output signal of RF PA. The performance of the model is analyzed varying the model parameters such as sample rate, nonlinear order, and memory depth. The results show that the relative error of the model is converged if the parameters are larger than specific values. We suggest an efficient modeling scheme considering complexity of the discrete model depending on the values of the model parameters. Modeling efficiency index(MEI) is defined, and it is used to extract optimum values for the model parameters. The suggested scheme is applied to discrete modeling of various RF PAs with various input signals such as WCDMA, WiBro, etc. The suggested scheme can be applied to the efficient design of digital predistorter for the wideband transmitter.