• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.115-118
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• 2000

In order to approximate a nonlinear function, modular wavelet networks combining wavelet theory and modular concept based on single layer neural network have been proposed as an alternative to conventional wavelet neural networks and kind of modular network. Modular wavelet networks provide better approximating performance than conventional one. In this paper, we propose an effective method to construct an optimal modualr wavelet network using genetic algorithm. This is verified through experimental results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2102-2104
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• 2001

This paper present the strategy of self-tuning nonlinear PID control using neural network. The nonlinear PID controller consists of a conventional PID controller and a neural network compensator. The neural network is trained by back-propagation algorithm. In this paper we propose modified back-propagation algorithm to improve learning speed. The results of simulation show the usefulness of the proposed scheme.

• 한국멀티미디어학회논문지
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• 제21권3호
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• pp.391-399
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• 2018

In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.

• Journal of Power Electronics
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• 제8권1호
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• pp.23-34
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• 2008

This paper deals with a Direct Torque Control (DTC) of an Interior Permanent Magnet Synchronous Motor (IPMSM) for the Electric Vehicle (EV) propulsion system using a Neural Network (NN). The Conventional DTC with optimized switching lookup table and three level torque controller generates relatively large torque ripples in an electric vehicle motor drive. For reducing the torque ripples, a three level torque controller is hereby replaced by the five level torque controller. Furthermore, the switching lookup table of the five level torque controller based DTC is replaced with a Neural Network. These DTC schemes of an IPMSM drive are simulated using MATLAB/SIMULINK. The simulated results are compared with the conventional DTC and it is found that the ripples in the torque, as well as in the stator current, are reduced drastically.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2000년도 춘계종합학술대회
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• pp.406-409
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• 2000

본 논문에서는 비선형 시스템의 효과적인 제어를 위해 병렬 연결된 신경회로망 제어방식을 제안한다. 제안한 제어방식은 비선형 시스템을 선형부분과 비선형 부분으로 분리하여 각각에 대해 반복최소자승법과 다층회귀신경회로망을 이용하여 플랜트를 직접 제어하는 방식이다. 제안한 제어방식의 유용성을 확인하기 위해 단일 관절 매니퓰레이터에 적용하여 기존의 다층 신경회로망 제어방식과 비교 검토하여 우수성을 확인하였다.

• 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.

• Journal of information and communication convergence engineering
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• 제16권3호
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• pp.160-165
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• 2018

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

• 대한임베디드공학회논문지
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• 제5권2호
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• pp.57-66
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• 2010

In this paper, we propose an enhanced voice recognition algorithm using adaptive MFCC(Mel Frequency Cepstral Coefficients) and neural network. Though it is very important to extract voice data from the raw data to enhance the voice recognition ratio, conventional algorithms are subject to deteriorating voice data when they eliminate noise within special frequency band. Differently from the conventional MFCC, the proposed algorithm imposed bigger weights to some specified frequency regions and unoverlapped filterbank to enhance the recognition ratio without deteriorating voice data. In simulation results, the proposed algorithm shows better performance comparing with MFCC since it is robust to variation of the environment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 B
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• pp.1126-1129
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• 2000

The equivalent transformation of a brushless DC motor into an separately exited DC motor has been possible with the vector control technique. Vector control is an effective technique for controlling variable speed drives of brushless DC motors. Conventional vector controllers, however, suffer from electrical machine parameter variations because these controllers depend on the parameters. This paper presents the vector control of brushless DC motor using a neural network. In the proposed method, a neural network is employed as on-line estimator of the nonlinear dynamic equations of brushless DC motor. The neural network based vector controller has the advantage of robustness against machine parameter variations as compared with conventional vector controller The simulation results using Matlab/Simulink verify the useful of the proposed method.

• Water Engineering Research
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• 제4권2호
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.