• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.93-100
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• 2007

In this paper, a novel approach based on wavelet transform is proposed to process the scraped character which is represented on digital image. The basis idea is that the scraped character is described by its textured neighborhood, and it is decomposed into multiresolution features at different levels with its background region. The image is first decomposed into sub bands by applying Daubechies wavelets. Character features are extracted from the low frequency sub-bands by partition, FCM clustering and area-based region process. High frequency ones are activated by applying local energy density over a moving mask. Features are synthesized in order to reconstruct the original image state through inverse wavelet transform Background region is eliminated and character is extracted. The experimental results demonstrate the effectiveness of the proposed method.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.669-684
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• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

• Journal of Intelligence and Information Systems
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• v.4 no.2
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• pp.23-34
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• 1998

Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.27-35
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• 2003

Because of the lack of translation invariance of wavelet basis, traditional wavelet thresholding denoising leads to pseudo-Gibbs phenomena in the vicinity of discontinuities of signal. In this paper, in order to reduce the pseudo-Gibbs phenomena, wavelet coefficients are thresholded and reconstruction algorithm is implemented through minimizing the total variation of denoising signal using subgradient descent algorithm. Most of experiments were performed under the non-real-time and real-time environments. In the case of non-real-time experiments, the algorithm that this paper proposes was found more effective than that of wavelet hard thresholding denoising by 2.794㏈(SNR) based on the signal to noise ratio. And lots of pseudo-Gibbs phenomena was removed visually in the vicinity of discontinuities. In the case of real-time experiments, the number of iteration was restricted to 60 times considering the performance time. It took 0.49 seconds and most of the pseudo-Gibbs phenomena were also removed.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.51-57
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• 1999

In this paper, we deal with the problem of controlling an unknown nonlinear dynamical system, using wavelet network. Accurate control of the nonlinear systems depends critically on the accuracy and efficiency of the function approximator used to approximate the function. Thus, we use wavelet network which shows high capability of approximating the functions and includes the free-selection of basis functions for the control of the nonlinear system. We find the dilation and translation that are wavelet network parameters by analyzing the time-frequency characteristics of the controller's input to construct an initial adaptive wavelet network controller. Then, weights is adjusted by the adaptive law based on the Lyapunov stability theory. We apply this direct adaptive wavelet network controller to control the inverted pendulum system which is an nonlinear system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.439-446
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• 2006

In the present study, we applied wavelet transform to decompose the hydro-meteorological data such as precipitation and temperature into the components with different return periods with a primary objective for extraction of nonlinear dynamical component. For the transform, we used the Daubechies wavelet of order 9 ('db9') as a basis function. Also, we applied the correlation dimension analysis to determine whether or not the detail and approximation components at the respective decomposition stage with the increasing of scale in the wavelet transform reveal the nonlinear dynamical characteristics. In other words, we proposed the combined use of the wavelet transform and the correlation dimension analysis as methodology to extract the nonlinear dynamical component from the hydro-meteorological data. The derived result has shown the method proposed in the present study is suitable for the segregation and extraction of the nonlinear dynamical component which is, in general, difficult to reveal by using the raw data.

• Journal of the Korean Mathematical Society
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• v.41 no.6
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• pp.945-955
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• 2004

In this paper, we establish a number system in $R^n$ which arises from a Haar wavelet basis in connection with decompositions of certain Cuntz algebra representations on $L^2$( $R^n$). Number systems in $R^n$ are also of independent interest [9]. We study radix-representations of $\chi$ $\in$ $R^n$: $\chi$:$\alpha$$_{ι}$ $\alpha$$_{ι-1}$…$\alpha$$_1$$\alpha$$_{0}$ ㆍ$\alpha$$_{-1}$ $\alpha$$_{-2}$ … as $\chi$= $M^{ι}$$\alpha$$_{ι}$ $\alpha$＋…M$\alpha$$_1$＋$\alpha$$_{0}$ ＋ $M^{-1}$ $\alpha$$_{-1}$ ＋ $M^{-2}$ $\alpha$$_{-2}$ ＋… where each $\alpha$$_{k}$ $\in$ D, and D is some specified digit set. Our analysis uses iteration techniques of a number-theoretic flavor. The view-point is a dual one which we term fractals in the large vs. fractals in the small,illustrating the number theory of integral lattice points vs. fractions.s vs. fractions.

• Geophysics and Geophysical Exploration
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• v.17 no.2
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• pp.88-94
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• 2014

Due to mechanical failure or geographical accessibility, the seismic data can be partially missed. In addition, it can be coarsely sampled such as crossline of the marine streamer data. This seismic data that irregular sampled and spatial aliased may cause problems during seismic data processing. Accurate and efficient interpolation method can solve this problem. Futhermore, interpolation can save the acquisition cost and time by reducing the number of shots and receivers. Among various interpolation methods, the Matching Pursuit method can be applied to any sampling type which is regular or irregular. However, in case of using sinusoidal basis function, this method has a limitation in spatial aliasing. Therefore, in this study, we have developed wavelet based Matching Pursuit method that uses wavelet instead of sinusoidal function for the improvement of dealiasing performance. In addition, we have improved interpolation speed by using inner product instead of L-2 norm.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.4
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• pp.179-188
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• 2003

Percentage differential characteristic scheme has been recognized as the principal basis for large transformer protection. Nowadays, relaying signals can contain second harmonic component to a large extent even in a normal state, and second harmonic ratio indicates a tendency of relative reduction because of the advancement of transformer's core material. And then, conventional second harmonic restraint differential relaying exposes some doubt in reliability. It is, therefore, necessary to develop a new algorithm for the effective and accurate discrimination. This paper deals with advanced fuzzy logic based relaying by using flux differential, and a new fault detection criterion logic scheme by using wavelet transform. To comparative analysis of proposed techniques, the paper constructs power system model including power transformer, utilizing the EMTP, and collects data through simulation of various internal faults and magnetizing inrush. The proposed fuzzy relaying and a new fault detection scheme were tested. The former, fuzzy relaying, was proven to be faster and more reliable than the latter.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.178-182
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• 2002

In this paper, we propose an adaptive wavelet method for car interior noise cancellation. For this purpose, we use a node dependent threshold which minimizes the Bayesian risk. We propose a noise estimation method based on spectral entropy using histogram of intensity and a candidate best basis instead of Donoho's best bases. And we modify the hard threshold function. Experimental results show that the proposed algorithm is more efficient, especially to heavy noisy signal than conventional one.