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Measuring stiffness of soils in situ
Published in Fusao Oka, Akira Murakami, Ryosuke Uzuoka, Sayuri Kimoto, Computer Methods and Recent Advances in Geomechanics, 2014
Fusao Oka, Akira Murakami, Ryosuke Uzuoka, Sayuri Kimoto
time-frequency domain, the S-transform proposed by Stockwell et al. (1996) and Pinnegar and Mansinha (2003) is used. The S-transform is a time-frequency spectral localization technique which combines elements of wavelet transforms and short-time Fourier transforms. Generally, wavelet analysis decomposes a time series into time and frequency space and it is able to find long-term periodic trends and localized variations of power. More precisely, S-transform represents the evolution of frequency in the time domain. While the Fourier Transform provides an image of the dynamic response as function of the frequency, the S-transform combines the frequency with the time and gives an image of the frequencies and their amplitude that appear during a motion. From Fig. 5(b), it can be remarked that when the regularization method is applied, a high-frequency noise disturbs the seismic signal, which does not exist in the case without regularization (Fig. 5(a)).
A new Stockwell mean square frequency methodology for analysing centrifuge data
Published in Andrew McNamara, Sam Divall, Richard Goodey, Neil Taylor, Sarah Stallebrass, Jignasha Panchal, Physical Modelling in Geotechnics, 2018
The Stockwell spectrum was first presented by Stockwell et al. (1996) as an improved means for studying a time signal in the time-frequency domain. The S transform extends the ideas of the continuous wavelet transform and uses a localized Gaussian window that scales with the time signal frequency. It is also directly related to the commonly used Fourier spectrum, in that the average of the local spectra over time is equal to the Fourier spectrum.
Time–Frequency Processing: Tutorial on Principles and Practice
Published in Antonia Papandreou-Suppappola, Applications in Time-Frequency Signal Processing, 2018
Other TFR software can also be found at Web sites of many TF processing researchers. For example, software for the S-transform [156] can presently be found at ftp://ftp.univnantes.fr/pub/universite/iutstnazaire/tftb/contribs/stockwell/s_transform.html; and for the reassignment TFRs at http://www.aei-potsdam.mpg.de/˜eric/ecm/these/ecm98.html by E. Chassande-Mottin or in the TFT toolbox.
Integrated DWT-DHT Feature Set for ABC Optimized SVM-Based PQ Classifier
Published in Electric Power Components and Systems, 2023
The development of novel techniques for characterizing and recognizing diverse power quality problems has been facilitated by recent developments in signal analysis. The commonly used signal processing techniques are wavelet transform, S-transform, and Hilbert Transform. The primary drawback of the wavelet transform is its deteriorated performance in noisy environments. Another method that is frequently utilized by PQ engineers is Stockwell’s transform, also referred to as the S-transform [40,41]. An extension of the wavelet transform, the S-transform is based on the localizing Gaussian window. In this case, the Gaussian window scales and moves while the modulating sinusoids are fixed with regard to the time axis. However, the drawback of S-transform is that the computation times are longer. The stiff window width, which is inversely proportional to frequency, causes frequency aliasing in ST, which leads to erroneous estimation of harmonics and interharmonics. The Hilbert transform produces distinct, comprehensible, and noise-resistant features [6]. Moreover, when compared to the S-transform, feature extraction takes less time. But, the performance of the Hilbert transform degrades when it faces simultaneous events like sag harmonics and swell harmonics. The event sag harmonics is confused with the event sag and vice versa [42]. But the time of extraction of the Hilbert transform is very less compared to S-transform and is capable of classifying the noisy signals precisely unlike the wavelet transform.
Characteristics of rockburst and early warning of microseismic monitoring at qinling water tunnel
Published in Geomatics, Natural Hazards and Risk, 2022
Tianhui Ma, Daoyuan Lin, Liexian Tang, Limin Li, Chun’an Tang, Kedar Prasad Yadav, Wendong Jin
After picking up the P wave information, it was necessary to identify the microseismic type. The identification of rock microseismic signal type was of great significance for microseismic monitoring. In order to conduct time-frequency analysis of waveform, S-Transform (ST) time-frequency analysis method was used to convert the waveform of time-amplitude into the waveform data of time-frequency-amplitude. ST overcomes the defect that the time width of short-time Fourier transform window was constant and can adaptively adjust the analysis time width according to the change of frequency, which realizes the accurate expression of signal frequency. Unlike wavelet and wavelet packet transform, ST method can provide intuitive time-frequency features without selecting window functions and analysis scales, as shown in Figure 14.
S-Transform Based Kurtosis Analysis for Detection of LG and LL Faults in 14 Bus Microgrid System
Published in IETE Journal of Research, 2022
Sagnik Datta, Aveek Chattopadhyaya, Surajit Chattopadhayay, Arabinda Das
Outgoing current waveforms captured from the aforementioned generator buses are non-stationary in nature. S-Transform is used to analyse the signals as it has two major advantages over the other popular signal processing tools like Short Time Fourier Transform (STFT) and Wavelet Transform (WT). First, the result of S-Transform is not dependent upon the proper choice of window function or mother wavelet. The second advantage is that S-Transform is capable of showing the presence of different harmonics in different frequencies.