• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.106-113
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• 2019

In this paper, we propose an algorithm to classify the received active pulse when the active sonar system is operated as a non-cooperative mode. The proposed algorithm uses CNN (Convolutional Neural Networks) which shows good performance in various fields. As an input of CNN, time frequency analysis data which performs STFT (Short Time Fourier Transform) of the received signal is used. The CNN used in this paper consists of two convolution and pulling layers. We designed a database based neural network and a pulse feature based neural network according to the output layer design. To verify the performance of the algorithm, the data of 3110 CW (Continuous Wave) pulses and LFM (Linear Frequency Modulated) pulses received from the actual ocean were processed to construct training data and test data. As a result of simulation, the database based neural network showed 99.9 % accuracy and the feature based neural network showed about 96 % accuracy when allowing 2 pixel error.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.4
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• pp.29-38
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• 2020

In this research, we proposed the active pulse classification algorithm using multi-label convolutional neural networks for active sonar system. The proposed algorithm has the advantage of being able to acquire the information of the active pulse at a time, unlike the existing single label-based algorithm, which has several neural network structures, and also has an advantage of simplifying the learning process. In order to verify the proposed algorithm, the neural network was trained using sea experimental data. As a result of the analysis, it was confirmed that the proposed algorithm converged, and through the analysis of the confusion matrix, it was confirmed that it has excellent active pulse classification performance.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.4
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• pp.27-36
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• 1996

It is well known that the viterbi algorithm proposed as a mthod of decoding convolutional codes is in fact maximum likelihood (ML) and therefore optimal. But, because hardware complexity grows exponentially with the constraint length, there will be severe constraints on the implementation of the viterbi decoders. In this paper, the three-layered backpropagation neural networks are proposed as an alternative in order to get sufficiently useful performance and deal successfully with the problems of the viterbi decoder. This paper shows that the neural convolutional decoder (NCD) can make a decision in the point of ML in decoding and describes simulation results. The cause of the difference between stochastic results and simulation results is discussed, and then thefuture prospect of the NCD is described on the basis of the characteristic of the transfer function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.2109-2118
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• 1996

The neural convolutional decoder(NCD) was proposed as a method of decoding convolutional codes. In this paper, simulation results are presented for coherent BPSK in memoryless AWGN channels and coherent QPSK in the satellite channels. The NCD can learn the nonlinear distortion caused by the charactersitics of the satellite channel including the filtering effects and the nonlinear effects of the travling wave tube amplifier(TWTA). Thus, as compared with the AWGN channel, the performance difference in the satellite channel between the NCD for the systematic code and the Viterbi decoder for the nonsystematic code is reduced.