• Smart Structures and Systems
• /
• v.21 no.2
• /
• pp.195-205
• /
• 2018

Research on Lamb wave-based damage identification in plate-like structures depends on precise knowledge of dispersive wave velocity. However, boundary reflections with the same frequency of interest and greater amplitude contaminate direct waves and thus compromise measurement of Lamb wave dispersion in different materials. In this study, non-reflecting boundaries were proposed in both numerical and experimental cases to facilitate time-frequency characterization of Lamb wave dispersion. First, the Lamb wave equations in isotropic and laminated materials were analytically solved. Second, the non-reflecting boundaries were used as a series of frames with gradually increased damping coefficients in finite element models to absorb waves at boundaries while avoiding wave reflections due to abrupt property changes of each frame. Third, damping clay was sealed at plate edges to reduce the boundary reflection in experimental test. Finally, the direct waves were subjected to the slant-stack and short-time Fourier transformations to calculate the dispersion curves of phase and group velocities, respectively. Both the numerical and experimental results suggest that the boundary reflections are effectively alleviated, and the dispersion curves generated by the time-frequency analysis are consistent with the analytical solutions, demonstrating that the combination of non-reflecting boundary and time-frequency analysis is a feasible and reliable scheme for characterizing Lamb wave dispersion in plate-like structures.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.1
• /
• pp.115-126
• /
• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.26 no.7
• /
• pp.656-665
• /
• 2015

In this paper, we improved the clarity and quality of the radar imaging by applying motion compensation for time-frequency transform in B-ISAR imaging. The proposed motion compensation algorithm using UWB radar is verified. B-ISAR algorithm procedure and time-frequency transform for improved motion compensation are provided for theoretical ground. The image was created by a UWB Radar B-ISAR imaging algorithm method. Also, creating a B-ISAR imaging algorithm for motion compensation of time-frequency transformation method was used. The B-ISAR Imaging algorithm is implemented using STFT(Short-Time Fourier Transform), GWT(Gabor Wavelet Transform), and WVD(Wigner-Ville Distribution) approaches. The performance of STFT is compared with the GWT and WVD algorithms. It is found that the WVD image shows more clarity and decreased spread phenomenon than other methods.

• Structural Engineering and Mechanics
• /
• v.66 no.2
• /
• pp.185-193
• /
• 2018

In this study, low- and high-frequency structure behaviors were identified and a systematic analysis procedure was proposed using noisy GPS data from a 165-m-high tower in ${\dot{I}}stanbul$, Turkey. The raw GPS data contained long- and short-periodic position changes and noisy signals at different frequencies. To extract the significant results from this complex dataset, the general structure and components of the GPS signal were modeled and analyzed in the time and frequency domains. Uncontrolled jumps and deviations involving the signal in the time domain were pre-filtered. Then, the signal was converted to the frequency domain after applying low- and high-pass filters, and the frequency and periodic component values were calculated. The spectrum of the tower motion obtained from the filtered GPS data had dominant peaks at a low frequency of $1.15572{\times}10-4Hz$ and a high frequency of 0.16624 Hz, consistent with two equivalent GPS datasets. Then, the signal was reconstructed using inverse Fourier transform with the dominant low frequency values to obtain filtered and interpretable clean signals. With the proposed sequence, processing of noisy data collected from the GPS receivers mounted very close to the structure is effective in revealing the basic behaviors and features of buildings.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.113-118
• /
• 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.

• Sleep Medicine and Psychophysiology
• /
• v.19 no.1
• /
• pp.11-17
• /
• 2012

Objective: Among various methods developed to quantitatively explore electroencephalograms (EEG), we focused on a wavelet method that was known to yield robust results under nonstationary conditions. The aim of this study was thus to introduce the wavelet method and demonstrate its potential use in clinical sleep studies. Method: This study involved artificial EEG specifically designed to validate the wavelet method. The method was performed to obtain time-dependent spectral power and phase angles of the signal. Synchrony of multichannel EEG was analyzed by an order parameter of the instantaneous phase. The standard methods, such as Fourier transformation and coherence, were also performed and compared with the wavelet method. The method was further validated with clinical EEG and ERP samples available as pilot studies at academic sleep centers. Result: The time-frequency plot and phase synchrony level obtained by the wavelet method clearly showed dynamic changes in the EEG waveforms artificially fabricated. When applied to clinical samples, the method successfully detected changes in spectral power across the sleep onset period and identified differences between the target and background ERP. Conclusion: Our results suggest that the wavelet method could be an alternative and/or complementary tool to the conventional Fourier method in quantifying and identifying EEG and ERP biomarkers robustly, especially when the signals were nonstationary in a short time scale (1-100 seconds).

• Smart Structures and Systems
• /
• v.13 no.2
• /
• pp.319-341
• /
• 2014

A family of smart tuned mass dampers (STMDs) with variable frequency and damping properties is analyzed under harmonic excitations and ground motions. Two types of STMDs are studied: one is realized by a semi-active independently variable stiffness (SAIVS) device and the other is realized by a pendulum with an adjustable length. Based on the feedback signal, the angle of the SAIVS device or the length of the pendulum is adjusted by using a servomotor such that the frequency of the STMD matches the dominant excitation frequency in real-time. Closed-form solutions are derived for the two types of STMDs under harmonic excitations and ground motions. Results indicate that a small damping ratio (zero damping is the best theoretically) and an appropriate mass ratio can produce significant reduction when compared to the case with no tuned mass damper. Experiments are conducted to verify the theoretical result of the smart pendulum TMD (SPTMD). Frequency tuning of the SPTMD is implemented through tracking and analyzing the signal of the excitation using a short time Fourier transformation (STFT) based control algorithm. It is found that the theoretical model can predict the structural responses well. Both the SAIVS STMD and the SPTMD can significantly attenuate the structural responses and outperform the conventional passive TMDs.

• Corrosion Science and Technology
• /
• v.4 no.3
• /
• pp.108-113
• /
• 2005

One of the recent safety issues in the pressurized heavy water reactor (PHWR) is the cracking of the feeder pipe. Because of the limited accessibility to the cracked region and a high dose of radiation exposure, it is difficult to inspect all the pipes with the conventional ultrasonic method. In order to solve this problem, a long-range guided wave technique has been developed. A computer program to calculate the dispersion curves in the pipe was developed and the dispersion curves for the feeder pipes in PHWR plants were determined. Several longitudinal and/or flexural modes were selected from the review of the dispersion curves and an actual experiment has been carried out with the specific alignment of the piezoelectric ultrasonic transducers. They were confirmed as L(0,1)) and/or flexural modes(F(m,2)) by the short time Fourier transformation(STFT) and were sensitive to the circumferential cracks, but not to the axial cracks in the pipe. An electromagnetic acoustic transducers(EMAT) was designed and fabricated for the generation and reception of the torsional guided wave. The axial cracks were detected by a torsional mode(T(0,1)) generated by the EMAT.

• Publications of The Korean Astronomical Society
• /
• v.14 no.2
• /
• pp.103-112
• /
• 1999

We have investigated the solar activity variation with period shorter than 1000 days, through Fourier transformation of solar cycle 21 and 22 data. And real time predictions of the flare maximum intensity have been made by multilinear regression method to allow the use of multivariate vectors of sunspot groups or active region characteristics. In addition, we have examined the evolution of magnetic field and current density in active regions at times before and after flare occurrence, to check short term variability of solar activity. According to our results of calculation, solar activity changes with periods of 27.1, 28.0, 52.1, 156.3, 333.3 days for solar cycle 21 and of 26.5, 27.1, 28.9, 54.1, 154, 176.7, 384.6 days for solar cycle 22. Periodic components of about 27, 28, 53, 155 days are found simultaneously at all of two solar cycles. Finally, from our intensive analysis of solar activity data for three different terms of $1977\~1982,\; 1975\~1998,\;and\;1978\~1982$, we find out that our predictions coincide with observations at hit rate of $76\%,\;63\%$, 59 respectively.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.12 no.3
• /
• pp.247-255
• /
• 2010

Bonding strength of shotcrete is a significant influential factor which plays the role of collapse prevention of tunnel crown and of debonding prevention of shotcrete induced by the blasting vibration. Thus, the evaluation of the shotcrete bonding state is one of the core components for shotcrete quality control. In this study, the peak particle velocities induced by blasting were measured on the shotcrete in a tunnel construction site and its effect on the bonding state of shotcrete is investigated. Drilling and blasting technique was used for the excavation of intersection tunnel connecting the main tunnel with the service tunnel. Blast-induced vibrations were monitored at some points of the main tunnel and the service tunnel. The shotcrete bonding state was evaluated by using impact-echo test coupled with the time-frequency domain analysis which is called short-time Fourier transformation. Analysis results of blast-induced vibrations and the time-frequency domain impact-echo signals showed that the blasting condition applied to the excavation of intersection tunnel hardly affects on the tunnel shotcrete bonding state. The general blasting practice in Korea was evaluated to have a minor negative impact on shotcrete quality.