• Smart Structures and Systems
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• v.33 no.3
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• pp.201-215
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• 2024

Spline chirplet transform and local maximum synchrosqueezing are introduced to present a novel structural instantaneous frequency (IF) identification method named local maximum synchrosqueezing spline chirplet transform (LMSSSCT). Namely spline chirplet transform (SCT), a transform is firstly introduced based on classic chirplet transform and spline interpolated kernel function. Applying SCT in association with local maximum synchrosqueezing, the LMSSSCT is then proposed. The index of accuracy and Rényi entropy show that LMSSSCT outperforms the other time-frequency analysis (TFA) methods in processing analytical signals, especially in the presence of noise. Numerical examples of a Duffing nonlinear system with single degree of freedom and a two-layer shear frame structure with time-varying stiffness are used to verify the effectiveness of structural IF identification. Moreover, a nonlinear supported beam structure test is conducted and the LMSSSCT is utilized for structural IF identification. Numerical simulation and experimental results demonstrate that the presented LMSSSCT can effectively identify the IFs of nonlinear structures and time-varying structures with good accuracy and stability.

• Smart Structures and Systems
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• v.33 no.6
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• pp.385-398
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• 2024

To improve the accuracy of time-frequency analysis (TFA) and instantaneous frequency (IF) extraction of structural dynamic response signals, this paper improves the spline-kernelled chirplet transform, and a new form of modified spline-kernelled chirplet transform (MSCT) based on revised Gaussian window function and energy concentration principle is put forward. The effectiveness of the proposed method is verified by numerical examples of single-component signal, multicomponent signal, single-degree-of-freedom Duffing nonlinear system and two-layer shear frame structure model. Then, a time-varying cable test is designed to collect the acceleration response signals under linear changing tension, and the IF extraction of these signals is performed by using MSCT, which further verifies the effectiveness and accuracy of this method. Through numerical simulation and experimental verification, it is proved that the proposed method can effectively extract the IF of nonlinear structure and time-varying structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11C
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• pp.1060-1067
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• 2005

In this paper, we generalize the shift-invariance properties of linear time-frequency distributions to the fractional Fourier domains that interpolate between the time and frequency domains. Magnitude-wise shift invariance in arbitrary fractional Fourier domains distinguishes the short-time Fourier transform (STFT) among all linear time-frequency distributions and simplifies the interpretation of the resultant distribution. We prove that the STFT is the only linear distribution that satisfies the magnitude-wise shift-invariance property in the fractional Fourier domains.

• Economic and Environmental Geology
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• v.28 no.5
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• pp.527-533
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• 1995

A disadvantage of Fourier analysis is that frequency information can only be extracted for the complete duration of a signal f(t). Since the Fourier transform integral extends over all time, from $-{\infty}$ to $+{\infty}$), the information it provides arises from an average over the whole length of the signal. If there is a local oscillation representing a particular feature, this will contribute to the calculated Fourier transform $F({\omega})$, but its location on the time axis will be lost There is no way of knowing whether the value of $F({\omega})$ at a particular ${\omega}$ derives from frequencies present throughout the life of f(t) or during just one or a few selected periods. This disadvantage is overcome in wavelet analysis which provides an alternative way of breaking a signal down into its constituent parts. The main advantage of the wavelet transform over the conventional Fourier transform is that it can not only provide the combined temporal and spectral features of the signal, but can also localize the target information in the time-frequency domain simultaneously. The wavelet transform distinguishes itself from Short Time Fourier Transform for time-frequency analysis in that it has a zoom-in and zoom-out capability.

• Interaction and multiscale mechanics
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• v.2 no.1
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• pp.91-107
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• 2009

In order to accurately estimate the seismic behavior of buildings, it is important to consider both nonlinear characteristics of the buildings and the frequency dependency of the soil impedance. Therefore, transform methods of the soil impedance in the frequency domain to the impulse response in the time domain are needed because the nonlinear analysis can not be carried out in the frequency domain. The author has proposed practical transform methods. In this paper, seismic response analyses considering frequency dependent soil impedance in the time domain are shown. First, the formulation of the proposed transform methods is described. Then, the linear and nonlinear earthquake response analyses of a building on 2-layered soil were carried out using the transformed impulse responses. Through these analyses, the validity and efficiency of the methods were confirmed.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.249-256
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• 2003

To investigate the safety and stability of the concrete lining, numerous studies have been conducted over the years and several methods have been developed. Most signal processing method of NDT techniques has based on the Fourier analysis. However, the application of Fourier analysis to analyze recorded signal shows results only in frequency domain, it is not enough to analyze transient waves precisely. In this study, a new NDT technique .using the wavelet theory was employed for the analysis of non-stationary wave propagation induced by mechanical impact in the concrete lining. The wavelet transform of transient signals provides a method for mapping the frequency spectrum as a function of time. To verify the availability of wavelet transform as a time- frequency analysis tool, model experiments have been conducted on the concrete lining model. From this study, it was found that the contour map by Wavelet transform provides more distinct results than the power spectrum by Fourier transform and it was concluded that Wavelet transform was an effective tool for the experimental analysis of dispersive waves in concrete structures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.83-95
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• 1999

Wavelet transform has localization for time or frequency. It is useful to analyze a nonstationary signal. Basic function on wavelet transform is generated dilating and translating the original wavelet(mother wavelet). In this paper, time-frequency analysis method using linear combination wavelet transform is proposed. And it is applied to diagnostic monitoring system using the proposed linear combination wavelet transform. The stationary and nonstationary signal is used linear chirp signal, fan noise signal, a sinusoid signal from revolution body, electronic signal. Transform applied to signal analysis use fast Fourier transform (FFT), Daubechies, Haar and proposed linear combination method. The result of time-frequency analysis using linear combination wavelet transform is suited for portraying nonstationary time signal as well as stationary signal. Also the diagnostic monitoring system carry out the effective the signal analysis.

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

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.8-15
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• 2016

As a generalized form of the conventional Fourier transform, fractional Fourier transform (FrFT) can analyze a signal at intermediate domain between time and frequency domains with a transform order ${\alpha}$. Especially, FrFT has a number of advantages in the analysis of LFM (Linear Frequency Modulation) signals due to its robustness to noise. In this paper, we have proposed a new method to detect and estimate the distance of the target from the FrFT spectrum of the received echo signal. Experimental results have validated the proposed method, and shown that reliable target distance could be estimated in noise and reverberation environments.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.219-223
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• 2002

Voltage and current signals during impulse tests on transformer are treated as non-stationary signals. A new method incorporating signal-processing method such as Wavelets and courier transform is proposed for failure identification. It is now possible to distinguish failure during impulse tests. The method is experimentally validated on a transformer winding. The wavelet transforms enables the detection of the time of occurrence of switching or failure events. After establishing the time of occurrence, the original waveform is split into two or more sections. The wavelet transform has ability to analysis the failure signal on time domain as well as frequency domain. Therefore, the wavelet transform is superior than courier transform to analysis the failure signal. In this paper, the fact was proved by real data which was achieved.