• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.1
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• pp.74-79
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• 2003

In this paper, the PID controller is constructed with a neural network and wavelet function. And the wavelet neural PID controller is adapted by choosing the values of the dilation and translation parameter of the wavelet function. Weights are adjusted by the inverse propagation algolithm. Applying this method to the position control system, its usefulness is verified from the results of experiment.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.17-20
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• 2001

In this paper, we design a nonlinear adaptive controller using wavelet neural network. The method proposed in this paper performs for a nonlinear system with unknown parameters, identification with using a wavelet neural network, and then a nonlinear adaptive controller is designed with those identified informations. The advantage of the proposed control method is simple to design a controller for unknown nonlinear systems, because we use the identified informations and design parameters are positioned within a negative real part of s-plane. The simulation results showed the effectiveness of proposed controller design method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.4
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• pp.22-27
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• 2000

In this paper, new neural network called dynamic multidimensional wavelet neural network (DMWNN) is proposed. The resulting network from wavelet theory provides a unique and efficient representation of the given function. Also the proposed DMWNN have ability to store information for later use. Therefore it can represent dynamic mapping and decreases the dimension of the inputs needed for network. This feature of DMWNN can compensate for the weakness of diagonal recurrent neural network(DRNN) and feedforward wavelet neural network(FWNN). The efficacy of this type of network is demonstrated through experimental results.

• Journal of Power Electronics
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• v.6 no.4
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• pp.307-314
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• 2006

This paper presents a wavelet and neural network based technology for the monitoring and classification of various types of power quality (PQ) disturbances. Simultaneous and automatic detection and classification of PQ transients, is recommended, however these processes have not been thoroughly investigated so far. In this paper, the hardware and software of a power quality data acquisition system (PQDAS) is described. In this system, an auto-classifying system combines the properties of the wavelet transform with the advantages of a neural network. Additionally, to improve recognition rate, extraction technology is considered.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.1
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• pp.65-73
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• 2007

Pattern classification of single and multiple discharge sources was applied using a wavelet image signal method in which a feature extraction was applied using a hidden sub-image. A feature extracting method that used vertical and horizontal images using an MSD method was applied to an averaging process for the scale of pulses for the phase. A feature extracting process for the preprocessing of the input of a neural network was performed using an inverse transformation of the horizontal, vertical, and diagonal sub-images. A back propagation algorithm in a neural network was used to classify defective signals. An algorithm for wavelet image processing was developed. In addition, the defective signal was classified using the extracted value that was quantified for the input of a neural network.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.780-785
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• 2004

In this paper, we proposed a ventricular fibrillation detection algorithm based on wavelet transform and artificial neural network. we selected RR intervals, the 6th and 7th wavelet coefficients(D6, D7) as features for classifying ventricular fibrillation. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference and fuzzy-neural network. MIT-BIH Arrhythmia database, Creighton University Ventricular Tachyarrhythmia database and MIH-BIH Malignant Ventricular Arrhythmia database were used as test and learning data. Among the algorithms, the proposed algorithm showed that the classification rate of normal and abnormal beat was sensitivity(%) of 96.10 and predictive positive value(%) of 99.07, and that of ventricular fibrillation was sensitivity(%) of 99.45. Finally. the proposed algorithm showed good performance compared to two other methods.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.459-460
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• 2001

In this paper, we analyze algorithms for diagnosing of VEP(visual evoked potential) signal. We used wavelet transform for the preprocessing of VEP signal data and back propagation neural network for the pattern recognition. We used several wavelets to study their effects and efficiency in the preprocessing of VEP. The diagnosis system led to good results. We obtained the noise reduced and compressed signal with the wavelet transform of the training VEP signal. So it is possible to train the neural network faster and exact diagnosis processing is possible in the neural network. From the experimental results, we know that the discrimination ability of the neural network is changed by the type of basis vector and the proposed system is good to the diagnosis of VEP.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.138-140
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• 2006

A new diagnosis model was constructed by combining atomic force microscopy (AFM), wavelet, and neural network. Plasma faults were characterized by filtering AFM-measured etch surface roughness with wavelet. The presented technique was evaluated with the data collected during the etching of silicon oxynitride thin film. A total of 17 etch experiments were conducted. Applying wavelet to AFM, surface roughness was detailed into vertical, horizon%at, and diagonal components. For each component, neural network recognition models were constructed and evaluated. Comparisons revealed that the vertical component-based model yielded about 30% improvement in the recognition accuracy over others. The presented technique was evaluated with the data collected during the etching of silicon oxynitride thin film. A total of 17 etch experiments were conducted

• International Journal of Control, Automation, and Systems
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• v.6 no.4
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• pp.607-612
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• 2008

This paper proposes a robot visual servo approach based on image appearance and a wavelet function neural network. The inputs of the wavelet neural network are changes of image features or the elements of image appearance vector, and the outputs are changes of robot joint angles. Image appearance vector is calculated by using eigen subspace transform algorithm. The proposed approach does not need a priori knowledge of the robot kinematics, hand-eye geometry and camera models. The experiment results on a real robot system show that the proposed method is practical and simple.

• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.