• Earthquakes and Structures
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• v.22 no.2
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• pp.147-155
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• 2022

Regarding the importance of seismic pounding, the available standards and guidelines specify minimum separation distance between adjacent buildings. However, the rules in this field are generally based on some simple assumptions, and the level of confidence is uncertain. This is attributed to the fact that the relative response of adjacent structures is strongly dependent on the frequency content of the applied records and the Eigen frequencies of the adjacent structures as well. Therefore, this research aims at investigating the separation distance of the buildings through a probabilistic-based algorithm. In order to empower the algorithm, the record-to-record uncertainties, are considered by probabilistic approaches; besides, a wide extent of material nonlinear behaviors can be introduced into the structural model by the implementation of the hysteresis Bouc-Wen model. The algorithm is then simplified by the application of the linearization concept and using the response acceleration spectrum. By implementing the proposed algorithm, the separation distance in a specific probability level can be evaluated without the essential need of performing time-consuming dynamic analyses. Accuracy of the proposed method is evaluated using nonlinear dynamic analyses of adjacent structures.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.71-76
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• 2023

This paper studied a method of attenuating nonlinear noise using a Wiener filter of a neural network structure in an acoustic noise attenuator. This system improves nonlinear noise attenuation performance with a deep learning algorithm using a neural network Wiener filter instead of using a conventional adaptive filter. A voice is estimated from a single input voice signal containing nonlinear noise using a 128-neuron, 8-neuron hidden layer and an error back propagation algorithm. In this study, a simulation program using the Keras library was written and a simulation was performed to verify the attenuation performance for nonlinear noise. As a result of the simulation, it can be seen that the noise attenuation performance of this system is significantly improved when the FNN filter is used instead of the Wiener filter even when nonlinear noise is included. This is because the complex structure of the FNN filter expresses any type of nonlinear characteristics well.

• Journal of computational fluids engineering
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• v.11 no.4 s.35
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• pp.90-100
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• 2006

A digital wave tank (DWT) simulation technique has been developed by authors to investigate the interactions of fully nonlinear waves with 3D marine structures. A finite-difference/volume method and a modified marker-and-cell (MAC) algorithm have been used, which are based on the Navier-Stokes (NS) and continuity equations. The fully nonlinear kinematic free-surface condition is implemented by the marker-density function (MDF) technique or the Level-Set (LS) technique developed for one or two fluid layers. In this paper, some applications for various engineering problems with free-surface are introduced and discussed. It includes numerical simulation of marine environments by simulation equipments, fully nonlinear wave motions around offshore structures, nonlinear ship waves, ship motions in waves and marine flow simulation with free-surface. From the presented simulations, it seems that the developed DWT simulation technique can handle various engineering problems with free-surface and reliably predict hydrodynamic features due to the fully-nonlinear wave motions interacting with such marine structures.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.254-262
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• 2008

In this paper, we propose an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for full dynamics of nonlinear flight systems. It is assumed that the model uncertainties such as structured and unstrutured uncertainties, and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate the model uncertainties of nonlinear flight systems, and an adaptive DSC technique is extended for the disturbance attenuation of nonlinear flight systems. All weights of SRWNNs are trained on-line by the smooth projection algorithm. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance nom external disturbances can be obtained. Finally, we present the simulation results for a nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1153-1159
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• 2007

This paper presents the hardware implementation of nonlinear PID controllers for the ROBOKER humanoid robot arms. To design the nonlinear PID controller on an FPGA chip, nonlinear functions as well as the conventional PID control algorithm have to be implemented by the hardware description language. Therefore, nonlinear functions such as trigonometric or exponential functions are designed on an FPGA chip. Simulation studies of the position control of humanoid arms are conducted and results are compared. Superior performances by the nonlinear PID controllers are confirmed when disturbances are present. Experiments of humanoid robot arm control tasks are conducted to confirm the performance of our hardware design and the simulation results.

• Journal of applied mathematics & informatics
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• v.2 no.1
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• pp.41-58
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• 1995

When F(χ)=0 is a system of nonlinear equations, we have established the parametrizied Homotopy algorithm for solving F(χ)=0 and some theorems for algorithm have been obtained.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.3
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• pp.559-564
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• 2010

The GMDH(Group Method of Data Handling) algorithm can be used to predict the complex nonlinear systems. The traditional GMDH algorithm produces the prdicted output of the system model in the output layer through the input layer and the intermediate layers as the prescribed process. The outputs of each layer are produced only by the outputs of the former layer. However, in the traditional GMDH algorithm, though the optimal structure of each layer is derived, the overall structure may not be derived optimally. To overcome this problem, GMDH prediction model which has the overall optimal structure is constructed by feeding back the error between the predicted output and the real output. This can make the prediction more precise. The capability improvement of the proposed algorithm compared to the traditional algorithm is verified through computer simulation.

• Journal of Astronomy and Space Sciences
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• v.35 no.4
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• pp.263-277
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• 2018

This paper presents a vision-based relative pose estimation algorithm and its validation through both numerical and hardware experiments. The algorithm and the hardware system were simultaneously designed considering actual experimental conditions. Two estimation techniques were utilized to estimate relative pose; one was a nonlinear least square method for initial estimation, and the other was an extended Kalman Filter for subsequent on-line estimation. A measurement model of the vision sensor and equations of motion including nonlinear perturbations were utilized in the estimation process. Numerical simulations were performed and analyzed for both the autonomous docking and formation flying scenarios. A configuration of LED-based beacons was designed to avoid measurement singularity, and its structural information was implemented in the estimation algorithm. The proposed algorithm was verified again in the experimental environment by using the Autonomous Spacecraft Test Environment for Rendezvous In proXimity (ASTERIX) facility. Additionally, a laser distance meter was added to the estimation algorithm to improve the relative position estimation accuracy. Throughout this study, the performance required for autonomous docking could be presented by confirming the change in estimation accuracy with respect to the level of measurement error. In addition, hardware experiments confirmed the effectiveness of the suggested algorithm and its applicability to actual tasks in the real world.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.3
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• pp.373-384
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• 2000

To assess the safety of nonlinear steel frame structures subjected to short duration dynamic loadings, especially seismic loading, a nonlinear time domain reliability analysis procedure is proposed in the context of the stochastic finite element concept. In the proposed algorithm, the finite element formulation is combined with concepts of the response surface method, the first order reliability method, and the iterative linear interpolation scheme. This leads to the stochastic finite element concept. Actual earthquake loading time-histories are used to excite structures, enabling a realistic representation of the loading conditions. The assumed stress-based finite element formulation is used to increase its efficiency. The algorithm also has the potential to evaluate the risk associated with any linear or nonlinear structure that can be represented by a finite element algorithm subjected to seismic loading or any short duration dynamic loading. The algorithm is explained with help of an example and verified using the Monte Carlo simulation technique.