• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.162-167
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• 2012

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.

• 융합신호처리학회논문지
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• 제3권2호
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• pp.64-70
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• 2002

This paper proposes a new construction method of neural networks. The construction method consists of two fundmental ideas, which are a parallel selection-style evaluation and rules evolution. A new collision avoidance algorithm using genetic and neural network is proposed to avoid moving obstacles such as mobile robots. The input parameters of this algorithm is position of moving obstacles and target. Output is a regenerated direction of mobile robot. This algorithm is very simple and so, it is available to application of real time process. The pattern of collision avoidance is learned through test execution.

• 융합신호처리학회논문지
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• 제3권1호
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• pp.80-86
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• 2002

아날로그 합산과 선형방정식을 풀 수 있는 신경망구조가 제안되었다. 계산에너지함수에 근거하여 가중치를 구하는 Hopfield 신경망모델을 사용하였다. 아날로그 합산과 선형방정식은 각각 Hopfiled의 A/D컨버터와 선형프로그래밍회로망을 이용하여 설계되었다. 시뮬레이션은 Pspice 프로그램을 이용하였으며, 그 결과들은 대부분 전체극소점으로 수렴함을 보였다.

• 융합신호처리학회논문지
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• 제2권1호
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• pp.88-94
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• 2001

In the position control system, the output of a controller is generally used as the input of a plant but the undesired noise is included in the output of a controller. Therefore, there is a need to use a precompensator for rejecting the undesired noise. In this paper, the expanded PID controller with a precompensator is constructed. The precompensator and PID controller are designed by a neural network with two-hidden layer and these coefficients are changed automatically to be a desired response of system when the response characteristic is changed under a condition.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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• pp.201-204
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• 2000

This paper describes a study on the time-varying control of parameters in learning of the neural network. Elman recurrent neural network (RNN) is used to implement the control of parameters. The parameters of learning and momentum rates In the error backpropagation algorithm ate updated at every iteration using fuzzy rules based on performance index. In addition, the gain and slope of the neuron's activation function are also considered time-varying parameters. These function parameters are updated using the gradient descent algorithm. Simulation results show that the auto-tuned learning algorithm results in faster convergence and lower system error than regular backpropagation in the system identification.

• 융합신호처리학회논문지
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• 제16권4호
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• pp.134-138
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• 2015

본 논문은 심장질환을 비침습적 방법으로 빠르고 쉽게 진단할 수 있도록 심음을 이용하는 방법에 대한 가능성을 찾는 것이다. 일반적으로 심음의 분류를 위하여 심음을 분리한 후에 특징파라미터를 추출하는 과정을 거치지 않고, 심음 분리에 사용되는 Shannon 엔트로피로 정규화하여 신경회로망의 입력으로 사용하였다. 심장질환에 따른 심잡음 분류를 위하여 Scaled conjugate gradient 역전파 알고리즘을 이용하여 신경회로망 분류기를 구현하였다. 정상 심음과 심장 질환의 경우 5가지를 포함하여 6종류의 심잡음에 대하여 분류가 가능함을 확인하였다.

• 융합신호처리학회논문지
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• 제14권1호
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• pp.57-61
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• 2013

본 논문에서는 하이브리드 유전 알고리즘을 이용한 morphological 뉴럴 네트워크 (MNN)의 최적화 방법을 제안하였다. MNN은 max-plus 연산을 기반으로 하고 있으므로 경사 학습법에 의한 파라미터 학습이 매우 어렵다. 이러한 문제를 해결하기 위해 하이브리드 유전 알고리즘을 이용하여 MNN의 파라미터들을 학습하였다. 제안된 방법의 유용성을 보이기 위해 SIDBA(standard image database) 표준영상에서 추출된 테스트 영상을 이용한 영상 압축/복원 실험을 수행하였고, 그 결과 제안된 방법에 의한 복원 영상이 합-곱 연산에 기반한 기존의 뉴럴 네트워크에 의한 복원영상보다 우수함을 알 수 있었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.179-182
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• 2002

In this paper, this system makes use of the analog sensor and converts the feature of fish analog signal when sensor is operating with CPU(80C196KC). Then, After signal processing, this feature Is classified a special feature and a outline of fish by using the neural network, one of the artificial intelligence scheme. This neural network classifies fish pattern of very simple and short calculation. This has linear activation function and the error backpropagation is used as a learning algorithm. And the neural network is learned in off-line process. Because an adaptation period of neural network is too long time when random initial weights are used, off-line learning Is induced to decrease the Progress time We confirmed this method has better performance than somewhat outdated machines.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.166-169
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• 1995

An intelligent system which is an integration of multi-functional instrumentation (MFI) and a neural network is discussed. According to some experiments of temperature and wind velocity it is clear that this system can learn the data structure of two parameters above. So it makes good performances for estimations of non-sample data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.3029-3045
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• 2017

The main processes of a cognitive radio system include spectrum sensing, spectrum decision, spectrum sharing, and spectrum conversion. Experimental results show that these stages introduce a time delay that affects the spectrum sensing accuracy, reducing its efficiency. To reduce the time delay, the frequency spectrum prediction was proposed to alleviate the burden on the spectrum sensing. In this paper, the deep recurrent neural network (DRNN) was proposed to predict the spectrum of multiple time slots, since the existing methods only predict the spectrum of one time slot. The continuous state of a channel is divided into a many time slots, forming a time series of the channel state. Since there are more hidden layers in the DRNN than in the RNN, the DRNN has fading memory in its bottom layer as well as in the past input. In addition, the extended Kalman filter was used to train the DRNN, which overcomes the problem of slow convergence and the vanishing gradient of the gradient descent method. The spectrum prediction based on the DRNN was verified with a WiFi signal, and the error of the prediction was analyzed. The simulation results proved that the multiple slot spectrum prediction improved the spectrum efficiency and reduced the energy consumption of spectrum sensing.