• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2498-2501
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• 2003

In this paper, we present a wavelet neural network (WNN) approach to the solution of the path tracking problem for mobile robots that possess complexity, nonlinearity and noise. First, we discuss a WNN based control system where the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot. This compact network structure is helpful to determine the number of hidden nodes and the initial value of weights. Then, the data with various noises provided by odometric and external sensors are here fused together by means of an Extended Kalman Filter (EKF) approach for the pose estimation problem of mobile robots. This control process is a dynamic on-line process that uses the wavelet neural network trained via the gradient-descent method with estimates from EKF. Finally, we verify the effectiveness and feasibility of the proposed control system through simulations.

• Journal of Welding and Joining
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• v.21 no.1
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• pp.80-86
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• 2003

This study develops a system for effective prediction of residual stresses by the backpropagation algorithm using the neural network. To achieve this goal, a series of experiments were carried out to and measured the residual stresses using the sectional method. With the experimental results, the optional control algorithms using a neural network could be developed in order to reduce the effect of the external disturbances during GMA welding processes. Then the results obtained from this study were compared between the measured and calculated results, weld guality might be controlled by the neural network based on backpropagation algorithm.. This system can not only help to understand the interaction between the process parameters and residual stress, but also improve the quantity control for welded structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.307-322
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• 1992

For designing the radar integrated data network, we construct the network structure with a spatial hieratchy decomposition scheme. The RIDN can be decomposed into several subent classes, those of which are composed of the several group classes of radar sites, In a group class. The communication nodes of a radar site are modeled by the software modules formulated with the statistical attributes of discrete events. And we get the analysis over the network through the separately constructed infra group level models which were coded with the C language.From the result of the simulation. We could findthe fact that the data integration system;s performance approaches to the theordtically calculated value after being stable. And also we could get the packet processing status of a communication module’s inner processor which is difficult to oberve through the mathematical calculation tin the subnet model of the integrated data network.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.620-623
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a fedforward recall and error back-propagation training. Since the total number of nodes are only eight this system can be easily realized by the general microprocessor. During the normal operation the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. IN addition the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Singal Processor DS1102 Board (TMS320C31) The basic DSP software is used to write C program which is compiled by using ANSI-C style function prototypes.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.3
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• pp.161-166
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• 2011

In this paper, for analysing the major factors giving the effect to the quality of HD level video on the multicasting service, we tried this test experiment on the College school network similar to the real service network environment. We measure the video quality distortion on the multicasting HD level video according to generating and increasing the broadcasting traffic on the situation that apply the QoS technique to the test network and not apply the QoS technique and then find out the threshold value of network factors giving the effect to the video quality distortion on the multicasting HD level video service. This paper can be used for guaranteeing the constant video quality and reducing the video quality distortion on the multicasting HD level video service.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.474-479
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• 2002

In this research, the relationship between physical measurements and subjective evaluations of floor impact noise in apartment building was quantified by applying the neural network analysis due to its complex and nonlinear characteristics. The neural network analysis was undertaken by setting up L-value, inverse A index, Zwicker parameters and ACF/IACF factors, as input data, which came from the measurements at real suites of apartment building having various sound insulations. The subjective responses from the psychoacoustic experiments were extracted as output data. Then, the reliability of the quantitative prediction for the subjective loudness was evaluated.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.296-303
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• 2007

This paper proposes a new width control system composed of an ANWC(Automatic Neural network based Width Control) and a fuzzy-PID controller in hot strip finishing mills which aims at obtaining the desirable width. The ANWC is designed using a neural network based width prediction model to minimize a width variation between the measured width and its target value. Input variables for the neural network model are chosen by using the hypothesis testing. The fuzzy-PlD control system is also designed to obtain the fast looper response and the high width control precision in the finishing mill. It is shown through the field test of the Pohang no. 1 hot strip mill of POSCO that the performance of the width margin is considerably improved by the proposed control schemes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.201-205
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• 1996

This paper suggests the scheme to simultaneously accomplish prediction of fracture initation and geometrical configuration of deformation in metal forming processes using the artificial neural network. A three-layer neural network is used and a back propagation algorithm is adapted to train the network. The Cockcroft-Latham criterion is used to estimate whether fracture occurs during the deformation process. The geometrical configuration and the value of ductile fracture are measured by finite element method. The prediction of network and numerical results of simple upsetting are compared. The proposed scheme has successfully predicted the geometrical configuration and fracture initiation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.351.1-351
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• 2002

In this research, the relationship between physical measurements and subjective evaluations of floor impact noise in apartment building was quantified by applying the neural network analysis due to its complex and nonlinear characteristics. The neural network analysis was undertaken by setting up L-value, inverse A index, Zwicker parameters and ACF/IACF factors, as input data, which came from the measurements at real suites of apartment building having various sound insulations. (omitted)

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.137-143
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• 2017

In this paper, we propose optimal route searching algorithm using ANN(Artificial Neural Network) and implement route searching system. Our proposed scheme shows that the route using artificial neural network is almost same as the route using Dijkstra's algorithm but the time in our propose algorithm is shorter than that of existing Dijkstra's algorithm. Proposed route searching method using artificial neural network has better performance than exiting route searching method because it use several weight value in making different routes. Through simulation, we show that our proposed routing system improves the performance and reduces time to make route irrespective of the number of hidden layers.