• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1769-1776
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• 2000

In this paper a new method is proposed to identify a modulation method in the case of unknown digitally modulated input signals. The proposed identification method is implemented with an artificial neural network which is based on characteristic feature extracted from the instantaneous amplitude the instantaneous phase and the instantaneous frequency of the input signals. The proposed method was simulated with 9 type signals (ASK2, FSK2, FSK4, PSK2, PSK4, PSK8, QAM8, QAM16) in a noisy communication environment. The results show that the artificial neural network can accurately recognize all kinds of patterns.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.625-628
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• 1997

In this paper the new neural network and sliding mode suspension controller is proposed. That neural network is error self-recurrent neural network. For fast on-line learning, this paper use recursive least squares method. A new neural networks converges considerably faster than the backpropagation algorithm and has advantages of being less affected by the poor initial weights and learning rate. The controller for suspension systems is designed according to sliding mode technique based on new proposed neural network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.494-497
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• 2002

In this paper, we proposed a control method of an unknown nonlinear system using a dynamical neural network. The proposed method performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed method, we simulated one-link manipulator. The simulation results showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.19-28
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• 2024

This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.387-394
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• 1996

This paper presents a Neural Network Identification(NNI) method for modeling of highly complicated nonlinear and time varing human system with a pneumatically driven mock circulatory system of Left Ventricular Assist Device(LVAD). This system consists of electronic circuits and pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by the computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, heart rate(HR), systole-diastole rate(SDR), which can vary state of system. Output parameters are preload, afterload which indicate the systemic dynamic characteristics. Consequently, the neural network shows good approximation of nonlinearity, and characteristics of left Ventricular Assist Device. Our results show that the neural network leads to a significant improvement in the modeling of highly nonlinear Left Ventricular Assist Device.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.576-581
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• 2003

Speech signal has the features of speakers. In this paper, we propose the speaker identification system which use the incremental learning based on neural network. Recorded speech signal through the Mic is passed the end detection and is divided voiced signal and unvoiced signal. The extracted 12 order cpestrum are used the input data for neural network. Incremental learning is the learning algorithm that the learned weights are remembered and only the new weights, that is created as adding new speaker, are trained. The architecture of neural network is extended with the number of speakers. So, this system can learn without the restricted number of speakers.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.303-304
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• 1991

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.120-127
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• 1995

A nonlinear empirical state-space model of the Artificial Neural Network(ANN) has been developed. The nonlinear model structure incorporates characteristic, so as to enable identification of the transient response, as well as the steady-state response of a dynamic system. A hybrid feedfoward/feedback neural network, namely a Local Time Delayed Recurrent Multi-layer Perception(RMLP), is the model structure developed in this paper. RMLP is used to identify nonlinear dynamic system in an input/output sense. The feedfoward protion of the network architecture provides with the well-known curve fitting factor, while local recurrent and cross-talk connections provides the dynamics of the system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibit a computationally desirable characteristic; both network sweep involved in the algorithm are performed forward, enhancing its parallel implementation. RMLP state-space and its associate learning algorithm is demonstrated through a simple examples. The simulation results are very encouraging.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.2
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• pp.56-63
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• 1997

This paper is concerned with the design of neural-network based autopilot control system. In this paper, the back-propagation algorithm is introduced and explained in detail. The system identification method based on neural networks for ship motion is developed and its efficacy is verified by using a simple ship maneuvering model. Problems which may arise in a complex maneuvering model are then discussed. The neural-network based system identification method developed in this paper can be used effectively for reconstructing the ship maneuvering moodel which is known to have nonlinearity.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.313-318
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• 1994

It is well known that the neural network can be used as an universal approximater for functions and functionals. But these theoretical results are just an existence theorem and do not lead to decide the suitable network structure. This doubfulness whether a certain network can approximate a given function or not, brings about serious stability problems when it is used to identify a system. To overcome the stability problem, We suggest successive identification and control scheme with supervisory controller which always assures the identification process within a basin of attraction of one stable equilibrium point regardless of fittness of the network.