• Structural Engineering and Mechanics
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• v.16 no.1
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• pp.83-99
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• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.189-192
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• 1995

In Visually Evoked Potentials(VEP) or Auditory Evoked Potentials(AEP), the components by the stimulation and the components which are irrelevant to the stimulation(noise or nonstationary spontaneous EEG) are mixed together. So one should average hundreds of EP waves to extract the components by the stimulation only. In this study, we have classified EP's, which are the responses of the different stimulations and different states of subjects. To classify the EP waves, the cross-correlation coefficients and neural network method(error back propagation) are used and compared.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.395-397
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• 1997

We have applied the neural network method for the neural networkmethod for the automatic scoring of the sleep stage. 17 features are extracted from the recorded EEG, EOG and EMG signals. These features are inputed to tile multilayer perceptron model. Neural network was trained with error-back propagation method. Results are compared with manual scoring of the experts, and show the possibility of application of automatic method in sleep stage scoring.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.5
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• pp.482-494
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• 1992

In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.816-824
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• 1991

It is well known that dynamic control is needed for fast and accurate control. Neural networks are ideal for representing the strongly nonlinear relationship in the dynamic equations including complex unmodeled effects. It thus creates many advantages over conventional methods such as simple, fast and accurate control through neural network's inherent learning and massive parallelism. In this paper, dynamic control of the full six degrees of freedom of an industrial robot arm will be presented using neural networks. Moreover, through application to a real robot the usefulness of neurocontrol is demonstrated. The back propagation and feedback-error learning is used to train the neurocontroller. Simulated control of a PUMA 560 arm demonstrates that it moves at high speed with good accuracy and generalizes over untrained trajectories as well as adapt to unforseen load changes and sensor noise.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.07a
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• pp.37-40
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• 2001

This paper presents the modeling methodology of Zinc diffusion process applied for high-speed avalanche photodiode fabrication using neural networks. Three process factors (sealing pressure, amount of Zn$_3$P$_2$ source per volume, and doping concentration of diffused layer) are examined by means of D-optimal design experiment. Then, diffusion rate and doping concentration of Zinc in diffused layer are characterized by a static response model generated by training fred-forward error back-propagation neural networks. It is observed that the process models developed here exhibit good agreement with experimental results.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.769-771
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• 2000

In this paper, pattern recognition scheme for EMG signal using artificial neural network is proposed. For manipulating ability, the movements of human arm are classified into several categories EMG signals of appropriate muscles are collected during arm movement. Patterns of EMG signals of each movement are recognized as follows: 1) The features of each EMG signal are extracted. 2) With these features, the neural network is trained by using feedforward error back-propagation (FFEBP) algorithm. The results show that the arm movements can be classified with EMG signals at high accuracy.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.797-799
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• 1999

In this paper, we design the Multi-FNN(Fuzzy-Neural Networks) using HCM Method. The proposed Multi-FNN uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. Also, We use HCM(Hard C-Means) method of clustering technique for improvement of output performance from pre-processing of input data. The parameters such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. We use the training and testing data set to obtain a balance between the approximation and the generalization of our model. Several numerical examples are used to evaluate the performance of the our model. From the results, we can obtain higher accuracy and feasibility than any other works presented previously.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.737-741
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• 1994

We show an application of a genetic algorithm to, control systems including neural networks. Genetic algorithms are getting more popular nowadays because of their simplicity and robustness. Genetic algorithms are global search techniques for optimization and many other problems. A feed-forward neural network which is widely used in control applications usually learns by error back propagation algorithm(EBP). But, when there exist certain constraints, EBP can not be applied. We apply a modified genetic algorithm to such a case. We show simulation examples of two cart-pole nonlinear systems: single pole and double pole.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.151-155
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• 1996

To automate an excavator the control issues resulting from environmental uncertainties must be solved. In particular the interactions between the excavation tool and the excavation environment are dynamic, unstructured and complex. In addition, operating modes of an excavator depend on working conditions, which makes it difficult to derive the exact mathematical model of excavator. Even after the exact mathematical model is established, it is difficult to design of a controller because the system equations are highly nonlinear and the state variable are coupled. 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. Computer simulation results demonstrate such powerful characteristics of the proposed controller as adaptation to changing environment, robustness to disturbance and performance improvement with the on-line learning in the position control of excavator attachment.