• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1442-1447
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• 2018

Since the indoor location recognition system using RFID is a method for predicting the indoor position, an error occurs due to the surrounding environment such as an obstacle. In this paper, we plan to reduce errors using back propagation neural networks. The neural network adjusts and trains the connection values between the layers to reduce the error between the actual position of the object with the reader and the expected position of the object through the experiment. In this paper, we propose a method that uses the median method and the radiation method as input to the neural network. Among the two methods, we want to find out which method is more effective in recognizing the actual position in an environment with obstacles and reduce the error. Consequently, the method using the median has less error, and we confirmed that the more the number of data, the smaller the error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.585-594
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• 2000

The exact path following of an autonomous mobile robot in a factory and an unreliable environment has many disadvantages in case of a classical control algorithm. In this paper, a neural network control approach based on an error back propagation algorithm is proposed for controlling a mobile robot to follow a line installed on the road. Since not only the three recognized informations from three sensors attached on a mobile robot but also the ten detailed informations in non recognition area are learned with input patterns, a mobile robot moves smoothly an installed line in spite of non perception space. The mobile robot has an effect of error minimization with a short time till a destination. To test an effectiveness of the proposed controller, the two motor velocity changes which is affected from a moving angle change of a mobile robot are simulated with computer.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.679-684
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• 1989

In this paper, we describe the PTP notion of robot manipulators by neural networks. The PTP motion requires the inverse kinematic redline and the joint trajectory generation algorithm. We use the multi-layered Perceptron neural networks and the Error Back Propagation(EBP) learning rule for inverse kinematic problems. Varying the number of hidden layers and the neurons of each hidden layer, we investigate the performance of the neural networks. Increasing the number of learning sweeps, we also discuss the performance of the neural networks. We propose a method for solving the inverse kinematic problems by adding the error compensation neural networks(ECNN). And, we implement the neural networks proposed by Grossberg et al. for automatic trajectory generation and discuss the problems in detail. Applying the neural networks to the current trajectory generation problems, we can refute the computation time for trajectory generation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.352-359
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• 2002

A neuro-fuzzy controller has some problems that he difficulty of tuning up the membership function and fuzzy rules, long time of inferencing and defuzzifying compare to PID. Also, the fuzzy controller's own defect as a PD controller has. In this study, it is proposed two methods to solve these problems. The first method is that inner fuzzy rules are tuned up automatically by the back propagation learning according to error patterns. And the second method is a new type defuzzification method that shorten the calculation time of an inferencing and a defuzzifying. In this study, it is designed the new type neuro-fuzzy controller that improves the fast response and the stability of a system by using the proposed methods. And, the designed controller is named EPLNFC(Error pattern Learning Neuro-Fuzzy Controller). To evaluate the fast response and the stability of EPLNFC designed in this study, EPLNFC is applied to a speed control of a DC motor and AC motor.

• Journal of Welding and Joining
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• v.17 no.5
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• pp.98-106
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• 1999

In GMAW, the spatters are generated according to the variation of the arc. Of the arc is stable, Few spatters are generated. But if unstable, too many spatters are generated. So, this means the spatters are dependent on the arc state. The aim of this study is to accurately estimate the arc state. To do this, the generated spatters were captured under the some welding conditions, and the waveforms of the arc voltage and welding current were collected. From the collected signals, the waveform factors and their standard deviations were extracted. Using these factors as input parameters of multi-layer artificial neural network, the learning for the weight of the generated spatters is performed and the estimation results to the real spatter are assessed. Obtained results are as follow: the linear correlation coefficient between the estimated result and the real spatters was 0.9986. And although the average convergence error was set 0.002, the estimated error to the real spatter was within 0.1 gr/min at each welding condition. In the estimation for the weight generated spatters, the result with multi-layer neural network was far better than with multiple regression analysis. Especially, even though under the welding condition which the arc state is unstable (the spatter is generated much more), very excellent estimation performance was shown.

• Journal of the Korea Society of Computer and Information
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• v.7 no.2
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• pp.95-100
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• 2002

By feedback channel, the decoder reports the addresses of corrupted macroblocks induced by transmission errors back to the encoder With these negative acknowledgements, the encoder can make the next frame having propagated errors by using forward dependency based on GOBs and MBs of the frame happening transmission errors. The encoder can precisely calculate and track the propagated errors by examining the backward motion dependency for each of four comer pixels in the current encoding frame until before-mentioned the next frame. The error-propagation effects can be terminated completely by INTRA refreshing the affected macroblocks. Such a fast algorithm further reduce the computation and memory requirements. The advantages of the low computation complexity and the low memory requirement are Particularly suitable for real-time implementation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.12
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• pp.2418-2425
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• 1994

Although the error backpropagation(EBP) algorithm based on the gradient descent method is a widely-used learning algorithm of neural networks, learning sometimes takes a long time to acquire accuracy. This paper develops a novel learning method to alleviate the problems of EBP algorithm such as local minima, slow speed, and size of structure and thus to improve performance by adopting other new networks. Gaussian Potential Function networks(GPFN), in parallel with multilayer neural networks. Empirical simulations show the efficacy of the proposed algorithm in function approximation, which enables us to train networks faster with the better generalization capabilities.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2949-2952
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• 2000

A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by back-propagation and each weights are updated by RLS(Recursive Least Square). Consequently. this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.705-709
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• 1995

The objective of this study is to design a multi-layer neural network which controls the position of excavator's attachment. In this paper, a dynamic controller has been developed based on an error back-propagation(BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it was used as a commanded feedforward input generator. A PD feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the excavator as well as the PD feedback error. By using the BP network as a feedforward controller, no a priori knowledge on system dynamics is need. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbancen and performance improvement with the on-line learning in the position control of excavator attachment.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.