• Wind and Structures
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• v.25 no.1
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• pp.25-38
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• 2017

This study investigates the dynamic analysis of a transversely isotropic thin plate. The plate is made of hyperelastic John's material and its constitutive law is obtained by taken the Frechect derivative of the highlighted energy function with respect to the geometry of deformation. The three-dimensional equation governing the motion of the plate is expressed in terms of first Piola-Kirchhoff's stress tensor. In the reduction to an equivalent two-dimensional plate equation, the obtained model generalizes the classical plate equation of motion. It is obtained that the plate under consideration exhibits harmonic force within its planes whereas this force varnishes in the classical plate model. The presence of harmonic forces within the planes of the considered plate increases the natural and resonance frequencies of the plate in free and forced vibrations respectively. Further, the parameter characterizing the transversely isotropic structure of the plate is observed to increase the plate flexural rigidity which in turn increases both the natural and resonance frequencies. Finally, this study reinforces the view that non-classical models of problems in elasticity provide ample opportunity to reveal important phenomena which classical models often fail to apprehend.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.303-314
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• 2020

The hydrological characteristics of watersheds and hydraulic systems of urban and river floods are highly nonlinear and contain uncertain variables. Therefore, the predicted time series of rainfall-runoff data in flood analysis is not suitable for existing neural networks. To overcome the challenge of prediction, a NARX (Nonlinear Autoregressive Exogenous Model), which is a kind of recurrent dynamic neural network that maximizes the learning ability of a neural network, was applied to forecast a flood in real-time. At the same time, NARX has the characteristics of a time-delay neural network. In this study, a hydrological model was constructed for the Taehwa river basin, and the NARX time-delay parameter was adjusted 10 to 120 minutes. As a result, we found that precise prediction is possible as the time-delay parameter was increased by confirming that the NSE increased from 0.530 to 0.988 and the RMSE decreased from 379.9 ㎥/s to 16.1 ㎥/s. The machine learning technique with NARX will contribute to the accurate prediction of flow rate with an unexpected extreme flood condition.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1055-1060
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• 2009

In this paper, we design an e-Learning system for the Driver's License Teste studying through the Internet. The proposed system make users to be arrived at the goal for the license in a shorter time by offering learning contents and items according to the item-responses made by the users based on the Item Response Theory. Moreover we design the scheme to give the optimum items and the most necessary content to the user during the learning procedure in the form of concept-based objects. All the items in the problem bank DB maintain their difficulties, discriminations, and guessing parameters as is the case of 3-parameter logistic model. In addition user profile DB stores users' status informations, item responses, and ability parameters. Using these structures and combining agents, we can offer the optimum learning process or dynamic personalized studying structure to the user. We can construct interface agent and content selection and feedback agent with the DB's described above. User can study without any awareness of system operations or personal fitting scheme.

• Journal of Power Electronics
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• v.16 no.2
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• pp.598-609
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• 2016

The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, a backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1008-1027
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• 2014

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.186-192
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• 1999

This paper introduces a study of a method for the forward kinematic analysis, which finds the 6 DOF motions and velocities from the given six cylinder lengths in the Stewart platform. From the viewpoints of kinematics, the solution for the inverse kinematic is easily found by using the vectors of the links which are composed of the joint coordinates in base and plate frames, to act contrary to the serial manipulator, but forward kinematic is difficult because of the nonlinearity and complexity of the Stewart platform dynamic equation with the multi-solutions. Hence we, first in this study, introduce the linear estimator using the Luenberger's observer, and the estimator using the nonlinear measured model for the forward kinematic solutions. But it is difficult to find the parameter of the design for the estimation gain or to select the estimation gain and the constant steady state error exists. So this study suggests the estimator with the estimation gain to be learned by the neural network with the structure of multi-perceptron and the learning method using back propagation and shows the estimation performance using the simulation.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.138-147
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• 1994

Performance management of computer networks is intended to improve a given network performance in order for more efficient information exchange between subsystems of an integrated large-scale system. Improtance of performance management is growing as many function of the large- scale system depend on the quality of communication services provided by the network. The role of performance management is to manipulate the adjustable protocol parameters on line so that the network can adapt itself to a dynamic environment. This can be divided into two subtasks : performance evaluation to find how changes in protocol parameters affect the network performance and decision making to detemine the magnitude and direction of parameter adjustment. This paper is the second part of the two papers focusing on conceptual design, development, and evaluation of performance management for token bus networks. This paper specifically deals with the task of decision making which utilizes the principles of stochastic optimization and learning automata. The developed algorithm can adjuxt four timer settings of a token bus protocol based on the result of performance evaluation. The overall performance management has been evaluated for its efficacy on a network testbed.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.50-59
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• 1998

This paper presents stabilization and adaptive control of flexible single link robot manipulator system by self-recurrent neural networks that is one of the neural networks and is effective in nonlinear control. The architecture of neural networks is a modified model of self-recurrent structure which has a hidden layer. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. When a flexible manipulator is rotated by a motor through the fixed end, transverse vibration may occur. The motor toroque should be controlled in such a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipuators so that it is arresed as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large changes in configuration common to robotic tasks requires dynamic models that describe both the rigid body motions, as well as the flexural vibrations. Therefore, a dynamic models for a flexible single link robot manipulator is derived, and then a comparative analysis was made with linear controller through an simulation and experiment. The results are proesented to illustrate thd advantages and imporved performance of the proposed adaptive control ove the conventional linear controller.

• Smart Structures and Systems
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• v.29 no.4
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• pp.599-616
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• 2022

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.407-410
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• 1996

In this Paper, we propose the feedback method having neural network to control the chaotic signals to periodic signals. This controller has very simple structure, it is immune to small parameter variations, the precise access to system parameters is not required and it is possible to follow ones of its inherent periodic orbits or the desired orbits without error, The controller consist of linear feedback gain and neural network. The learning of neural network is achieved by error-backpropagation algorithm. To prove and analyze the proposed method, we construct a software tool using c-language.