• 대한음성학회지:말소리
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• 제48호
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• pp.93-105
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• 2003

Since telephone channel has bandlimited frequency characteristics, speech signal over the telephone channel shows degraded speech quality. In this paper, we propose an algorithm using neural network to reconstruct wideband speech from its narrowband version. Although single neural network is a good tool for direct mapping, it has difficulty in training for vast and complicated data. To alleviate this problem, we modularize the neural networks based on appropriate clustering of the acoustic space. We also introduce fuzzy computing to compensate for probable misclassification at the cluster boundaries. According to our simulation, the proposed algorithm showed improved performance over the single neural network and conventional codebook mapping method in both objective and subjective evaluations.

• Biotechnology and Bioprocess Engineering:BBE
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• 제11권1호
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• pp.67-71
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• 2006

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

• 전기학회논문지
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• 제56권8호
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• pp.1351-1359
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• 2007

In this paper, we have studied the power quality(PQ) diagnosis system with the two methods for PQ diagnosis. One to Apply a regulation value in compliance with mathematics calculation, and the other Automatic identification using Neural network algorithm. Neural network algorithm is used for an automatic diagnosis of the PQ. The regulation proposed by IEEE 1159 Working group is applied for the precision of the diagnosis. In order to divide accurate segmentation, the algorithm for a computer training used the back propagation out of several neural network algorithms. We have configured the proto-type sample by using Labview and a programmed Neural Networks Algorithm using with C. And arbitrary electric Signal generated by OMICRON Company's CMC 256-6 for an efficiency test.

• Journal of Mechanical Science and Technology
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• 제20권12호
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• pp.2079-2086
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• 2006

A fundamental study for developing a fault diagnosis system of a pump is performed by using neural network. Acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. And the codes of pump malfunctions were selected as units of output layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. Neural network trained by acoustic signals can detect malfunction or diagnose fault of a given machine from the results.

• 대한기계학회논문집A
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• 제26권3호
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• pp.436-444
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• 2002

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is predicted using the artificial neural network. The neural network has trained training examples of stress ratio, normalized eccentricity, off-centered direction and stress error using backpropagation learning process. The prediction results of the error using the trained neural network are good agreement with FE analyzed ones.

• 상하수도학회지
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• 제21권3호
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model

• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.200-209
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• 1999

This paper presents a precise tracking control scheme for the proposed parallel robot using artificial neural network. This control scheme is composed of three feedback controllers and one feedforward controller. Conventional PD controller and artificial neural network are used as feedback and feedforward controller respectively. A backpropagation learning strategy is applied to the training of artificial neural network, and PD controller outputs are used as target outputs. The PD controllers are designed at the robot dynamics based on inter-relationship between active joints and moving platform. Feedback controllers insure the total stability of system, and feedforward controller generates the control signal for trajectory tracking. The precise tracking performance of proposed control scheme is proved by computer simulation.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.620-623
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a fedforward recall and error back-propagation training. Since the total number of nodes are only eight this system can be easily realized by the general microprocessor. During the normal operation the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. IN addition the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Singal Processor DS1102 Board (TMS320C31) The basic DSP software is used to write C program which is compiled by using ANSI-C style function prototypes.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권9호
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• pp.569-575
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.