• Composites Research
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• v.20 no.6
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• pp.21-27
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• 2007

Low-velocity impact damage in a composite stiffened panel was detected using built-in piezoelectric active sensor arrays. Using these piezoelectric active sensors, various diagnostic signals were generated to propagate Lamb waves through the structure and the responses were picked up to detect changes in the structure's vibration signature due to the damage. Three algorithms - ADI(Active Damage Interrogation), TD RMS (Time Domain Root Mean Square) and STFT (Short Time Fourier Transform) - were examined to express the features of the signal changes as one damage index. From damage detecting tests, two impact induced delaminations were detected and the location was estimated with the algorithms and diagnostic signals.

• Journal of KSNVE
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• v.7 no.4
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• pp.637-644
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• 1997

In this paper, a method of finite element analysis is presented for efficient calculation of vibration characteristics of not only simply interconnected structure with cyclic symmetry but also multiply interconnected structure with cyclic symmetry by using discrete Fourier trandform by means of a computer with small memory in a short time. Simply interconnected structure means it is composed of substructures which are adjacent themselves in circumferential direction. First, a mathematical model of multiply interconnected structure with cyclic symmetry is defined. The multiply interconnected structure is partitioned into substructures with the same goemetric configuration and constraint eqauations to be satisfied on connecting boundaries are defined. Nodal displacements and forces are transformed into complex forms through discrete Fourier transform and then finite element analysis is performed for just only a representative substructure. In free vibration analysis, natural frequencies of a whole structure can be obtained through a series of calculation for a substructure along the number of nodal diameter. And in forced vibration analysis, forced response of whole structure can be achieved by using inverse discrete Fourier transform of results which come from analysis for a substructure.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.6 s.111
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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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 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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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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.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.2
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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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.8
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• pp.1002-1009
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• 2012

This paper shows the research about radar target recognition using the measured radar signals from MSU(Michgan State University) bistatic radar system. In this research, we first did the bistatic measurements at $30^{\circ}$, $60^{\circ}$, $90^{\circ}$ using F-14, Mig-29, and F-22 scale models. Then, we extract the target feature vectors using time-frequency analysis methods such as STFT(Short Time Fourier Transform) and CWT(Continous Wavelet Transform) and perform the target classification test using MLP(Multi-layerd Perceptron) neural network. The results show that the target classification performance is too much dependent on the bistatic angles and the best performance is obtained at the $60^{\circ}$ bistatic angle.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.895-902
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• 2007

Rotating machinery has two typical faults with clearance, one is partial rub and the other is looseness. Due to these faults, non-linear and non-stationary signals are occurred. Therefore, time-frequency analysis is necessary for exact fault diagnosis of rotating machinery. In this paper newly developed time-frequency analysis method, HHT(Hilbert-Huang Transform) is applied to fault diagnosis and compared with other method of FFT, SFFT and CWT. The results show that HHT can represent better resolution than any other method. Consequently, the faults of rotating machinery are diagnosed efficiently by using HHT.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.81-87
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• 2001

The human auditory system is appropriate for the "constant Q"system. The STFT (Short Time Fourier Transform) is not suitable for the auditory perception model since it has constant bandwidth. In this paper, the CWT (continuous wavelet transform) is employed for the auditory filter model. In the CWT, the frequency resolution can be adjusted for auditory sensation models. The proposed CWT is applied to the modeling of the JNVF. In addition, other signal processing methods such as STFT, VER-FFT and VFR-STFT are discussed. Among these methods, the model of JNVF (Just Noticeable Variation in Frequency) by using the CWT fits in with the JNVF of auditory model although it requires quite a long time.