• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.459-464
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• 2003

This paper proposes the use of a wavelet based image transform algorithm in human iris recognition method and the effectiveness of this technique will be determined in preprocessing of extracting Iris image from the user´s eye obtained by imaging device such as CCD Camera or due to torsional rotation of the eye, and it also resolves the problem caused by invariant under translations and dilations due to tilt of the head. This technique values through the proposed translation-invariant wavelet transform algorithm rather than the conventional wavelet transform method. Therefore we extracted the best-matching iris feature values and compared the stored feature codes with the incoming data to identify the user. As result of our experimentation, this technique demonstrate the significant advantage over verification when it compares with other general types of wavelet algorithm in the measure of FAR & FRR.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.417-424
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• 2002

This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.640-645
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• 2004

This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

• Tribology and Lubricants
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• v.24 no.6
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• pp.320-325
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• 2008

The recent industrial application requires technical methods to get the cutting fluid droplet surfaces in particular from the viewpoint of topography and micro texture. To characterize the surface topography of droplet, the combination of the confocal laser scanning microscope (CLSM) and wavelet filtering is well suited for obtaining the droplet geometry encountered in tribological research. This technique indicates a better agreement in obtaining an appropriate droplet surface obtained by the CLSM over a detail range of surface accuracy (resolution: $2{\mu}m$). And the results allow an excellent accuracy in a measurement of a droplet surface. The combination of extended focal depth measurement configured and multi-scale wavelet filtering has proven that it can construct a droplet surface in a successive and accurate way. A multi-scale approach of wavelet filtering was developed based on the decomposition and reconstruction of droplet surface by 2D wavelet transform using db9 (a mother wavelet of daubechies). Also this technique can be extended to characterize the quantification of droplet properties and other field in a wide range of scales. Finally this method is verified to be a better droplet surface modeling in a micro scale arising in a mist machining.

• Transactions on Electrical and Electronic Materials
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• v.18 no.4
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• pp.221-224
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• 2017

This paper dealt with the extraction of series arc signals based on wavelet transform in order to improve the accuracy of arc detection in indoor wiring systems. Three types of arc sources including a cord-cord, a terminal-cord, and an outlet-plug were fabricated to simulate typical arc defects. An arc generator fabricated according to UL 1699 was used to generate arcs. The optimal mother wavelet was selected as bior1.5 by calculating the correlation coefficients between the detected single current pulse and the wavelet. The detected arc current signals were then decomposed into eight levels using the discrete wavelet transform that implements the multi-resolution analysis method. By analyzing the decomposed components, the detail components D6, D7, and D8 were associated with arc signals, which were used for signal reconstruction. From the result, it was verified that the proposed method can be used for the extraction of the series arc signal from the AC mains, which is expected to be applied to further analysis of arc signals in indoor wiring systems.

• The Journal of the Acoustical Society of Korea
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• v.20 no.5
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• pp.21-27
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• 2001

Impulsive sound and vibration signals in gear system are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in condition monitoring of gear system. The traditional continuous wavelet transform has been used for detection of impulsive signals. However, it is often difficult for the continuous wavelet transform to identify spikes at high frequency and meshing frequencies at low frequency simultaneously since the continuous wavelet transform is to apply the linear scaling (a-dilation) to the mother wavelet. In this paper, the spike wavelet transform is developed to extract these impulsive sound and vibration signals. Since the spike wavelet transform is to apply the non-linear scaling, it has better time resolution at high frequency and frequency resolution at low frequency than that of the continuous wavelet transform respectively. The spike wavelet transform can be, therefore, used to detect fault position clearly without the loss of information for the damage of a gear system. The spike wavelet transform is successfully is applied to detection of the gear fault with tip breakage.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2209-2211
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• 2004

Unlike the wavelet neural network, since a mother wavelet layer of the self-recurrent wavelet neural network (SRWNN) is composed of self-feedback neurons, it has the ability to store past information of the wavelet. Therefore we propose the prediction method for the nonlinear chaotic time series model using a SRWNN. The SRWNN model is learned for the modeling of a function such that the inputs arc known values of the time series and the output is the value in the future. The parameters of the network are tuned to minimize the difference between the nonlinear mapping of the chaotic time series and the output of SRWNN using the gradient-descent method for the adaptive backpropagation algorithm. Through the computer simulations, we demonstrate the feasibility and the effectiveness of our method for the prediction of the logistic map and the Mackey-Glass delay-differential equation as a nonlinear chaotic time series.

• Smart Structures and Systems
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• v.18 no.5
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.317-325
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• 2008

The electrocardiogram (ECG) measurement system consists of I/O interface to input the ECG signals from two electrodes, FPGA (Field programmable gate arrays) module to process the signal conditioning, and real time module to control the system. The algorithms based on wavelet transform were developed to remove the noise of the ECG signals and to determine the QRS-waves. Triangular wave tests were conducted to determine the optimal factors of the wavelet filter by analyzing the SNRs (signal to noise ratios) and RMSEs (root mean square errors). The hybrid rule, soft method, and symlets of order 5 were selected as thresholding rule, thresholding method, and mother wavelet, respectively. The developed wavelet filter showed good performance to remove the noise of the triangular waves with 10.98 dB of SNR and 0.140 mV of RMSE. The ECG signals from a total of 6 subjects were measured at different measuring postures such as lying, sitting, and standing. The durations of QRS-waves, the amplitudes of R-waves, the intervals of RR-waves were analyzed by using the finite impulse response (FIR) filter and the developed wavelet filter. The wavelet filter showed good performance to determine the features of QRS-waves, but the FIR filter had some problems to detect the peaks of Q and S waves. The measuring postures affected accuracy and precision of the ECG signals. The noises of the ECG signals were increased due to the movement of the subject during measurement. The results showed that the wavelet filter was a useful tool to remove the noise of the ECG signals and to determine the features of the QRS-waves.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2369-2381
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• 1999

In this paper, we propose an iterative algorithm for reducing the blocking artifact in block transform-coded images by using a wavelet transform. In the proposed method, an image is considered as a set of one-dimensional horizontal and vertical signals and one-dimensional wavelet transform is utilized in which the mother wavelet is the first order derivative of a Gaussian like function. The blocking artifact is reduced by removing the blocking component, that causes the variance at the block boundary position in the first scale wavelet domain to be abnormally higher than those at the other positions, using a minimum mean square error (MMSE) filter in the wavelet domain. This filter minimizes the MSE between the ideal blocking component-free signal and the restored signal in the neighborhood of block boundaries in the wavelet domain. It also uses local variance in the wavelet domain for pixel adaptive processing. The filtering and the projection onto a convex set of quantization constraint are iteratively performed in alternating fashion. Experimental results show that the proposed method yields not only a PSNR improvement of about 0.56-1.07 dB, but also subjective quality nearly free of the blocking artifact and edge blur.