• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2521-2525
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• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.178-186
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• 2003

Recently, many researchers have been interested in biometric systems such as fingerprint, handwriting, key-stroke patterns and human iris. From the viewpoint of reliability and robustness, iris recognition is the most attractive biometric system. Moreover, the iris recognition system is a comfortable biometric system, since the video image of an eye can be taken at a distance. In this paper, we discuss human iris recognition, which is based on accurate iris localization, robust feature extraction, and Neural Network classification. The iris region is accurately localized in the eye image using a multiresolution active snake model. For the feature representation, the localized iris image is decomposed using wavelet transform based on dyadic Haar wavelet. Experimental results show the usefulness of wavelet transform in comparison to conventional Gabor transform. In addition, we present a new method for setting initial weight vectors in competitive learning. The proposed initialization method yields better accuracy than the conventional method.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2034-2036
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• 2002

In this paper, we propose the modeling of a chaotic nonlinear system using wavelet neural networks. In our modeling, we used the parameter adjusting method as the training method of a wavelet neural network. The difference between the actual output of a nonlinear chaotic system and that of a wavelet neural network adjusts the parameters of a wavelet neural network using the gradient-descent method. To verify the efficiency of this paper, we perform the simulation using Duffing system, which is a representative continuous time chaotic nonlinear system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.169-172
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• 1999

Recently, the wavelet transform has been a new and powerful tool for signal processing. It is more suitable specially for the feature extraction and detection of non-stationary signals than traditional methods such as, the Fourier Transform(FT), the Fast Fourier Transform(FFT) and the Least Square Method etc. because of the characteristic of the multi-scale analysis and time-frequency domain localization. The wavelet transform has been developed for the analysis of PD pulse signal to raise in the progress of insulation degradation. In this paper, the wavelet transform was applied to one foundational method for feature extraction. For the obtain experimental data, a computer-aided partial discharge measurement system with a single acoustic sensor was used. If we are applying to the neural network method the accumulated data through the extracted feature, it is expected that we can detect the PD pulse signal in the insulation materials on the on-line.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.249-256
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• 2003

To investigate the safety and stability of the concrete lining, numerous studies have been conducted over the years and several methods have been developed. Most signal processing method of NDT techniques has based on the Fourier analysis. However, the application of Fourier analysis to analyze recorded signal shows results only in frequency domain, it is not enough to analyze transient waves precisely. In this study, a new NDT technique .using the wavelet theory was employed for the analysis of non-stationary wave propagation induced by mechanical impact in the concrete lining. The wavelet transform of transient signals provides a method for mapping the frequency spectrum as a function of time. To verify the availability of wavelet transform as a time- frequency analysis tool, model experiments have been conducted on the concrete lining model. From this study, it was found that the contour map by Wavelet transform provides more distinct results than the power spectrum by Fourier transform and it was concluded that Wavelet transform was an effective tool for the experimental analysis of dispersive waves in concrete structures.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.298-300
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• 2004

In the decoding process of interframe wavelet coding, the inverse wavelet transform requires huge computational complexity. However, the decoder may need to be used in various devices such as PDAs, notebooks, PCs or set-top Boxes. Therefore, the decoder's complexity should be adapted to the processor's computational power. A decoder designed in accordance with the processor's computational power would provide optimal services for such devices. So, it is natural that the complexity scalability and the low complexity codec are also listed in the requirements for scalable video coding. In this contribution, we develop a method of controlling and lowering the complexity of the spatial wavelet transform while sustaining almost the same coding efficiency as the conventional spatial wavelet transform. In addition, the proposed method may alleviate the ringing effect for certain video data.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.2
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• pp.126-135
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• 2011

In this study, numerical analysis was carried out by using the finite element method to construct the first mode shape of damaged stiffened plates, and the damage locations were detected with two-dimensional discrete wavelet analysis. In the experimental analysis, four different damaged stiffened structures were observed. Firstly, each damaged structure was hit with a shaker, and then accelerometers were used to measure the vibration responses. Secondly, the first mode shape of each structure was obtained by using the wavelet packet, and the location of cracks were also determined by two-dimensional discrete wavelet analysis. The results of the numerical analysis and experimental investigation reveal that the proposed method is applicable to detect single crack or multi-cracks of a stiffened structure. The experimental results also show that fewer measurement points are required with the proposed technique in comparison to those presented in previous studies.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.133-140
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• 2006

Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.

• Journal of KSNVE
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• v.10 no.5
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• pp.769-774
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• 2000

The purpose of the work is to present successful applications of a modified wavelet shrinkage method for the accurate and fast estimation of a transfer function. Although the experimental process of determining a transfer function introduces not only Gaussian but also non-Gaussian noises, most existing estimation methods are based only on a Gaussian noise model. To overcome this limitation, we propose to employ a modified wavelet shrinkage method in which L1 -based median filtering and L2 -based wavelet shrinkage are applied repeatedly. The underlying theory behind this approach is briefly explained and the superior performance of this modified wavelet shrinkage technique is demonstrated by a numerical example.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.3
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• pp.121-129
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• 2001

In this paper, a new efficient feature extraction method based on Daubechies discrete wavelet transform is presented. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of aging(the early period, the middle period, the last period)