• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.3
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• pp.231-238
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• 2020

This paper investigates the soundness of porcelain insulators associated with the acoustic emission (AE) technique. The AE technique is a popular non-destructive method that measures and analyzes the burst energy that occurs mainly when a crack occurs in a high-frequency region. Typical AE methods require continuous monitoring with frequent sensor calibration. However, in this study, the AE technique excites a porcelain insulator using only an impact hammer, and it applies a high-pass filter to the signal frequency range measured only in the AE sensor by comparing the AE and the acceleration sensors. Next, the extracted time-domain signal is analyzed for the damage assessment. In normal signals, the duration is about 2ms, the area of the envelope is about 1,000, and the number of counts is about 20. In the damage signal, the duration exceeds 5ms, the area of the envelope is about 2,000, and the number of counts exceeds 40. In addition, various characteristics in the time and frequency domain for normal and damage cases are analyzed using the short-time Fourier transform (STFT). Based on the results of the STFT analysis, the maximum energy of a normal specimen is less than 0.02, while in the case of the damage specimen, it exceeds 0.02. The extracted high-frequency components can present dynamic behavior of crack regions and eigenmodes of the isolated insulator parts, but the presence, size, and distribution of cracks can be predicted indirectly. In this regard, the characteristics of the surface crack region were derived in this study.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.31-37
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• 1998

This paper proposes a feature extraction method using wavelet transform for speech recognition. Speech recognition system generally carries out the recognition task based on speech features which are usually obtained via time-frequency representations such as Short-Time Fourier Transform (STFT) and Linear Predictive Coding(LPC). In some respects these methods may not be suitable for representing highly complex speech characteristics. They map the speech features with same may not frequency resolutions at all frequencies. Wavelet transform overcomes some of these limitations. Wavelet transform captures signal with fine time resolutions at high frequencies and fine frequency resolutions at low frequencies, which may present a significant advantage when analyzing highly localized speech events. Based on this motivation, this paper investigates the effectiveness of wavelet transform for feature extraction of wavelet transform for feature extraction focused on enhancing speech recognition. The proposed method is implemented using Sampled Continuous Wavelet Transform (SCWT) and its performance is tested on a speaker-independent isolated word recognizer that discerns 50 Korean words. In particular, the effect of mother wavelet employed and number of voices per octave on the performance of proposed method is investigated. Also the influence on the size of mother wavelet on the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is compared with the most prevalent conventional method, MFCC (Mel0frequency Cepstral Coefficient). The experiments show that the recognition performance of the proposed method is better than that of MFCC. But the improvement is marginal while, due to the dimensionality increase, the computational loads of proposed method is substantially greater than that of MFCC.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.9
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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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.99-106
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• 2005

Instantaneous Frequency(IF) of Doppler signals is used to get the information of relative velocity and miss distance between a missile and the corresponding target. Though Short-Time Fourier Transform(STFT) is mainly used to estimate IF, it has many errors in wide band signals where frequency changes sharply. Because it has a fixed window in time and frequency axes. This paper deals with IF estimation of Doppler signal using a Continuous Wavelet Transform(CWT) which has adaptive window in time and frequency axes. The proposed method is able to estimate IF regardless of frequency changes because it has a narrow window in high frequency band and a wide window in low frequency band. The experimental results demonstrate that the proposed method outperforms STFT in estimating IF.

• Journal of Korea Multimedia Society
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• v.20 no.10
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• pp.1646-1654
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• 2017

Pupil size variation can not be controlled intentionally by the user and includes various features such as the blinking frequency and the duration of a blink, so it is suitable for understanding the user's emotional state. In addition, an ocular feature based emotion classification method should be studied for virtual and augmented reality, which is expected to be applied to various fields. In this paper, we propose a novel emotion classification based on CNN with pupil size variation signals which include not only various ocular feature information but also time information. As a result, compared to previous studies using the same database, the proposed method showed improved results of 5.99% and 12.98% respectively from arousal and valence emotion classification.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.469-473
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• 2010

In this study, combustion tests using liquefying fuels with fast regression rate were performed. The chamber pressure oscillation was analyzed and hazards of combustion instabilities were examined. In case of Liquefying fuel with fast regression rate, the amplitude of chamber pressure oscillation was increased compared to the polymeric fuels. However, the critical combustion instability can hardly occur in liquefying fuel. This is because the rapid change of inner chamber diameter limits the amplification of chamber pressure oscillation. The chamber pressure oscillation due to the large increase of fuel production and the vortex shedding in pre-chamber violently occurs during combustion using single-port axial injector.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.7
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• pp.848-851
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• 2012

As many radar systems are simultaneously operated with overlapping frequency bands, interference between systems inevitably occurs. Because interference can degrade radar performance, suppression of interference is a critical issue in radar systems. In this letter, a new interference detection and suppression method using a short-time Fourier transform and an adaptive notch filter is proposed. An experiment is carried out to validate the proposed method and the results demonstrate that the proposed method is suitable for application in real FMCW radars.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.217-223
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• 2023

In this paper, we propose a new model for the conditional generation of music, considering key and rhythm, fundamental elements of music. MIDI sheet music is converted into a WAV format, which is then transformed into a Mel Spectrogram using the Short-Time Fourier Transform (STFT). Using this information, key and rhythm details are classified by passing through two Convolutional Neural Networks (CNNs), and this information is again fed into the Music Transformer. The key and rhythm details are combined by differentially multiplying the weights and the embedding vectors of the MIDI events. Several experiments are conducted, including a process for determining the optimal weights. This research represents a new effort to integrate essential elements into music generation and explains the detailed structure and operating principles of the model, verifying its effects and potentials through experiments. In this study, the accuracy for rhythm classification reached 94.7%, the accuracy for key classification reached 92.1%, and the Negative Likelihood based on the weights of the embedding vector resulted in 3.01.

• Speech Sciences
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• v.7 no.1
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• pp.17-29
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• 2000

The STC(Sinusoidal Transform Coding) is a vocoding technique that uses a sinusoidal speech model to obtain high- quality speech at low data rate. It models and synthesizes the speech signal with fundamental frequency and its harmonic elements in frequency domain. To reduce the data rate, it is necessary to represent the sinusoidal amplitudes and phases with as small number of peaks as possible while maintaining the speech quality. As a basic research to develop a low-rate speech coding algorithm using the sinusoidal model, in this paper, we investigate the speech quality depending on the number of sinusoids. By varying the number of spectral peaks from 5 to 40 speech signals are reconstructed, and then their qualities are evaluated using spectral envelope distortion measure and MOS(Mean Opinion Score). Two approaches are used to obtain the spectral peaks: one is a conventional STFT (Short-Time Fourier Transform), and the other is a multiresolutional analysis method.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.10a
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• pp.158-162
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• 2003

The PVDF(polyvinylidene fluoride) film sensor as one of smart sensors has good characteristics to detect the impact damages of composite structures. The capabilities of the PVDF film sensor for evaluating impact behaviors and damages of Gr/Ep laminates subjected to low-velocity impact were examined. From sensor signals, the specific wave-forms implying the damage were detected. The wavelet transform(WT) and Short Time Fourier Transform(STFT) were used to decompose the piezoelectric sensor signals in this study. The impact behaviors of Gr/Ep laminates were simulated and the impact forces were reconstructed using the sensor signals. Finally, the impact damages were predicted by finite element analysis with the reconstructed forces. For experimental verification, a series of low-velocity impact tests from low energy to damage-induced energy were carried-out. The extent of damage in each case was examined by means of ultrasonic C-scan and the measured damage areas were agreed well with the predicted areas by the F.E.A.