• The Journal of the Korea institute of electronic communication sciences
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• 제19권1호
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• pp.113-118
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• 2024

In this paper, we propose an optimal wavelet in a system for canceling background noise of acoustic signals. This system performed Discrete Wavelet Transform(DWT) instead of the existing Short Time Fourier Transform(STFT) and then improved noise cancellation performance through a deep learning process. DWT functions as a multi-resolution band-pass filter and obtains transformation parameters by time-shifting the parent wavelet at each level and using several wavelets whose sizes are scaled. Here, the noise cancellation performance of several wavelets was tested to select the most suitable mother wavelet for analyzing the speech. In this study, to verify the performance of the noise cancellation system for various wavelets, a simulation program using Tensorflow and Keras libraries was created and simulation experiments were performed for the four most commonly used wavelets. As a result of the experiment, the case of using Haar or Daubechies wavelets showed the best noise cancellation performance, and the mean square error(MSE) was significantly improved compared to the case of using other wavelets.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• 제17권1호
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• pp.101-106
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• 2004

The phenomenon of PD(Partial Discharge) is accompanied by electromagnetic wave which can be detected by UHF(Ultra High Frequency) antenna. The signals obtaining from UHF antenna are very high rapid pulse and have wide band frequency responses. The distribution of PRPD(Phase Resolved Partial Discharge) which consisted of those pulse train can show distinct characteristics of PD sources. But it is not sufficient to discriminate among PD sources. This paper suggests that the stochastic parameters formed by preprocessing of STFT(Short Time Fourier Transform) are good tools for differentiate from PD sources. The stochastic parameters are CC(Cross Correlation) mean value, CC standard deviation, CC skewness, CC kurtosis.

• Journal of the Korean Society for Nondestructive Testing
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• 제24권2호
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• pp.142-150
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• 2004

A preliminary study of the behavior of ultrasonic guided wave mode in a pipe using a comb transducer for maintenance inspection of power plant facilities has been verified experimentally. The mode identification has been carried out in a pipe using the time-frequency analysis methods such as the wavelet transform(WT) and the short time Fourier transform (STFT), compared with theoretically calculated group velocity dispersion curves for longitudinal and flexural modes. The results are in good agreement with analytical predictions and show the effectiveness of using the time-frequency analysis method to identify the individual modes. It was found out that the longitudinal mode(0,1) is less affected by mode conversion compared with the other modes. Therefore, L(0,1) is selected as an optimal mode for the evaluation of the surface defect in a pipe.

• Proceedings of the KSR Conference
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• 한국철도학회 2008년도 추계학술대회 논문집
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• pp.263-268
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• 2008

The measurement of magnetic field is performed AC magnetic field emission density in driver cab and saloon's compartment of rolling stock. In order to measure magnetic-field emission, a three-axial magnetic-field sensor is used and connected to data process system. The AC magnetic field is checked and analysis through BNC output, DAQ cad and notebook PC. The spectral analysis is performed by short time Fourier transform(STFT) for time-domain emission signal.

• Journal of Electrical Engineering and Technology
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• 제10권6호
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• pp.2442-2447
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• 2015

Because an electric railway vehicle is a large scale moving load, it can cause various kinds of power quality problems in the railroad power system. The power quality impacts are considered as the strong instantaneous stresses to the related power systems and can cause an accelerating aging and a malfunction of the power supplying components. Therefore, it is necessary to detect the small and intermittent symptoms mixed in the voltage waveform. However, they cannot be detected by the triggering functions of the existing power analyzers installed in the railway systems. This paper will examine the drawback of some fast detection tools and propose an advanced detecting and analyzing method based on a combined TEO and STFT algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• 제16권6호
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• pp.627-633
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• 2006

The tire is considered as one of the important noise sources having an influence on vehicle's performance. The Pattern noise of a tire is the transmission sound of airborne noise. On smooth asphalt road, Pattern noise is amplified with the velocity. In recent, the study on the reduction of Pattern noise is energetically processed. Pattern noise is strongly related with pitch sequence. To reduce the pattern noise, tire's designer has to randomize the sequence of pitch. The FFT is a traditional method to evaluate the level of the randomization of the pitch sequence, but gives no information on time-varying, instantaneous frequency. In the study, we found that Time-Frequency transform is a useful method to non-stationary signal such as tire noise.

• Proceedings of the KIEE Conference
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• 대한전기학회 2004년도 하계학술대회 논문집 C
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• pp.1784-1786
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• 2004

PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency. STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33${\times}$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13${\times}$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• 제8권1호
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• pp.135-141
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• 1999

Recently, advance signal analysis which is called "Time-Frequency Analysis" has been developed. Wavelet and Wigner Distribution are used to the method. Wavelet transform(WT) is applied to time-frequency analysis of waveforms obtained by an ultrasonic pulse-echo technique. The Gabor function is adopted as the analyzing wavelet. Wavelet analysis method is an attractive technique for evolution of material characterization evoluation. In this paper, the feasibility of suppression of ultrasonic background noise signal using WT has been presented. These results suggest that ultrasonic background noise ginal can be suppressed and enhanced even for SNR of 20.8 dB. This property of the WT is extremely useful for the detecting flaw echos embedded in background noise.und noise.

• The Transactions of the Korean Institute of Electrical Engineers A
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• 제49권9호
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• pp.425-432
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• 2000

Wavelet transform is a new method fro electric power quality analysis. Several types of mother wavelets are compared using voltage sag data. Investigations on the use of some mother wavelets, namely Daubechies, Symlets, Coiflets, Biorthogonal, are carried out. On the basis of extensive investigations, optimal mother wavelets for the detection of voltage sag are chosen. The recommended mother wavelet is 'Daubechies 4(db4)' wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, can be used most properly in disturbance phenomena which occurs rapidly for a short time. This paper presents a discrete wavelet transform approach for determining the beginning time and end time of voltage sags. The technique is based on utilising the maximum value of d1(at scale 1) coefficients in multiresolution analysis(MRA) based on the discrete wavelet transform. The procedure is fully described, and the results are compared with other methods for determining voltage sag duration, such as the RMS voltage and STFT(Short-Time Fourier Transform) methods. As a result, the voltage sag detection using wavelet transform appears to be a reliable method for detecting and measuring voltage sags in power quality disturbance analysis.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• 제21권12호
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• pp.1460-1466
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• 2010

In this paper, we analyzed the performance of radar target classification using the monostatic and bistatic radar cross section(RCS) for four different wire targets. Short time Fourier transform(STFT) and continuous wavelet transform (CWT) were used for feature extraction from the monostatic RCS and the bistatic RCS of each target, and a multi-layered perceptron(MLP) neural network was used as a classifier. Results show that CWT yields better performance than STFT for both the monostatic RCS and the bistatic RCS. And, when STFT was used, the performance of the bistatic RCS was slightly better than that of the monostatic RCS. However, when CWT was used, the performance of the monostatic RCS was slightly better than that of the bistatic RCS. Resultingly, it is proven that bistatic RCS is a good cadndidate for application to radar target classification in combination with a monostatic RCS.