Analysis and Simulation of Meteorological Wind Fields Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1228-1236 ◽  
Author(s):  
Fo Rong Jin ◽  
Wei Rong Wang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
Inertial Subrange ◽  
Target Velocity ◽  
Wind Fields ◽  
Transform Method ◽  
Time Frequency ◽  
Velocity Spectra ◽  
Energy Relations

In this work, we examined the non Gauss distribution characteristic and evolution law of the wavelet coefficient of a gust using wavelet transform; according to the time-frequency characteristic, the wavelet transform coefficients and the energy relations of the target velocity spectra are derived; the wavelet coefficient is generated using the cascade model reflecting the turbulent intermittent; the unsteady gust artificial generation method is established based on inverse wavelet transform; and the arbitrary unsteady fluctuation law can be generated by regulating the coefficient of low frequency. The results show that: the natural gust is in good agreement with Karman wind speed spectrum, meets the turbulence-5 / 3 law in the inertial subrange, and exhibits the nature of intermittence and local self-similarity; the artificial wind sequence based on the inverse wavelet transform method shows similar turbulence statistics with natural gust, with which, the effectiveness of the method is confirmed.

Aeroacoustic analysis of slat tones

2021 ◽  
Vol 263 (1) ◽  
pp. 5650-5663
Author(s):  
Hasan Kamliya Jawahar ◽  
Syamir Alihan Showkat Ali ◽  
Mahdi Azarpeyvand
Keyword(s):  
Wavelet Transform ◽  
Surface Pressure ◽  
Wavelet Coefficient ◽  
Pressure Fluctuations ◽  
Low Frequency ◽  
Stream Velocity ◽  
High Lift ◽  
Unsteady Surface Pressure ◽  
Slat Noise ◽  
Generation Mechanisms

Experimental measurements were carried out to assess the aeroacoustic characteristics of a 30P30N high-lift device, with particular attention to slat tonal noise. Three different types of slat modifications, namely slat cove filler, serrated slat cusp, and slat finlets have been experimentally examined. The results are presented for an angle of attack of α = 18 at a free-stream velocity of U = 30 m/s, which corresponds to a chord-based Reynolds number of Re = 7 x 10. The unsteady surface pressure near the slat region and far-field noise were made simultaneously to gain a deeper understanding of the slat noise generation mechanisms. The nature of the low-frequency broadband hump and the slat tones were investigated using higher-order statistical approaches for the baseline 30P30N and modified slat configurations. Continuous wavelet transform of the unsteady surface pressure fluctuations along with secondary wavelet transform of the broadband hump and tones were carried out to analyze the intermittent events induced by the tone generating resonant mechanisms. Stochastic analysis of the wavelet coefficient modulus of the surface pressure fluctuations was also carried out to demonstrate the inherent differences of different tonal frequencies. An understanding into the nature of the noise generated from the slat will help design the new generation of quite high-lift devices.

scholarly journals Acoustic Emission Wavelet Analysis of Filament Wound Composite Tube Fatigue Damage Process

2015 ◽  
Vol 9 (1) ◽  
pp. 214-219 ◽  
Author(s):  
Su Hua ◽  
Chang Cheng
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Fatigue Damage ◽  
Frequency Band ◽  
Low Frequency ◽  
High Signal ◽  
Wave Form ◽  
Radial Compression ◽  
Time Frequency ◽  
Emission Signal

This paper performed a radial compression fatigue test on glass fiber winding composite tubes, collected acoustic emission signals at different fatigue damages stages, used time frequency analysis techniques for modern wavelet transform, and analyzed the wave form and frequency characteristics of fatigue damaged acoustic emission signals. Three main frequency bands of acoustic emission signal had been identified: 80-160 kHz (low frequency band), 160-300 kHz (middle frequency band), and over 300kHz (high frequency band), corresponding to the three basic damage modes: the fragmentation of matrix resin, the layered damage of fiber and matrix, and the fracture of cellosilk respectively. The usage of wavelet transform enabled the separation of fatigue damaged acoustic emission signals from interference wave, and the access to characteristics of high signal-noise-ratio fatigue damage.

COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

2007 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Clinical Tool ◽  
Prognostic Information ◽  
Transform Method ◽  
Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

Method of Footprint Image Stitching Based on Multiscale SIFT Feature Matching

2013 ◽  
Vol 303-306 ◽  
pp. 1056-1059
Author(s):  
Sen Wang ◽  
Yin Hui Zhang ◽  
Zhong Hai Shi ◽  
Zi Fen He
Keyword(s):  
Wavelet Transform ◽  
Feature Matching ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
Original Method ◽  
Inverse Transformation ◽  
Image Stitching ◽  
Image Splicing ◽  
Sift Feature ◽  
Matching Pair

The image stitching method is widely used into the suspect's footprint information extraction. In order to improve the image detail and the matching precision, the Footprint map image stitching method which is based on the wavelet transform and the SIFT feature matching is put forward. The wavelet transform in this method is perform based on the pretreatment of image, move the low frequency wavelet coefficient to zero, adjusting thresholds of the high frequency wavelet coefficient and inverse transformation, then, use the SIFT to extract and match the key-points of the processed images. For the error matching pair of coarse match, you can use the RANSAC to filter them out. This article demonstrates its advantage through to the original image splicing comparisons. The experimental results show that the method display more clear detail and the precision of matching than the original method.

scholarly journals Application of Short Time Fourier Transform and Wavelet Transform for Sound Source Localization Using Single Moving Microphone in Machine Condition Monitoring

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Meifal Rusli
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Sound Source ◽  
Low Frequency ◽  
Peak Position ◽  
Source Position ◽  
Short Time Fourier Transform ◽  
Sound Sources ◽  
Time Frequency ◽  
Short Time

<p class="TTPParagraphothers"><em>The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed.</em> The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). <em>As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map</em><em>. </em>Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.</p>

scholarly journals Signal Analysis On Soil Stress From Vibrating Compaction Based On Wavelet Transform

2014 ◽  
Vol 60 (2) ◽  
pp. 257-268
Author(s):  
Zhang Qing-Zhe ◽  
Yan Bing ◽  
Dai Jing-Liang ◽  
Yang Bao-Gui
Keyword(s):  
Wavelet Transform ◽  
Low Frequency ◽  
Frequency Component ◽  
Start Time ◽  
Transform Method ◽  
Singularity Detection ◽  
Study Results ◽  
Soil Stress ◽  
Stress Signal ◽  
Stress Signals

Abstract The paper presented the wavelet transform method for d e-noising and singularity detection to soil compressive stress signal. The study results show that the reconstruction signals by the wavelet de-noising keeps the low frequency component at [0, 31.25Hz] of the original signal and improves the high frequency property at other frequency bands. The impaction time from the start time to resonance time of the stress signals is varies with the depth of the soil. With the increase of times of compaction, the impaction time of the stress is decreasing in every layer. But the speed of reaching compacted status in each layer is different.

scholarly journals Seismic Damage Analysis of Concrete Gravity Dam Based on Wavelet Transform

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Dunben Sun ◽  
Qingwen Ren
Keyword(s):  
Wavelet Transform ◽  
Dynamic Response ◽  
Bearing Capacity ◽  
Low Frequency ◽  
Gravity Dam ◽  
Moderate Intensity ◽  
Concrete Gravity Dam ◽  
Time Frequency ◽  
Wavelet Energy ◽  
Seismic Bearing Capacity

The key to the dam damage assessment is analyzing the remaining seismic carrying capacity after an earthquake occurs. In this paper, taking Koyna concrete gravity dam as the object of study, the dynamic response and damage distribution of the dam are obtained based on the concrete damage plastic constitutive model. By using time-frequency localization performance of wavelet transform, the distribution characteristics of wavelet energy for gravity dam dynamic response signal are revealed under the action of different amplitude earthquakes. It is concluded by numerical study that the wavelet energy is concentrated in low-frequency range with the improving of seismic amplitude. The ultimate peak seismic acceleration is obtained according to the concentration degree of low-frequency energy. The earthquake damage of the dam under the moderate-intensity earthquake is simulated and its residual seismic bearing capacity is further analyzed. The new global damage index of the dam is proposed and the overall damage degree of the dam can be distinguished using defined formula under given earthquake actions. The seismic bearing capacity of the intact Koyna dam is 591 gal considering the dam-water interaction and its residual seismic bearing capacity after simulating earthquake can be calculated.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

2021 ◽  
pp. 1-81
Author(s):  
Xiaokai Wang ◽  
Zhizhou Huo ◽  
Dawei Liu ◽  
Weiwei Xu ◽  
Wenchao Chen
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Seismic Data ◽  
Random Noise ◽  
Principal Component ◽  
Frequency Resolution ◽  
Reflection Point ◽  
Transform Method ◽  
Time Frequency ◽  
The Time Domain

Common-reflection-point (CRP) gather is one extensive-used prestack seismic data type. However, CRP suffers more noise than poststack seismic dataset. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain but also in the time-frequency domain. Therefore, we firstly use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the time-frequency domain, as the SSWT has better time-frequency resolution and reconstruction properties. Then we propose to use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the time-frequency domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples show that our proposed method can effectively attenuate random noise with a better attenuation performance than the commonly-used principal component analysis, FX filter, and the continuous wavelet transform method.

ON THE USE OF THE REASSIGNED WAVELET TRANSFORM FOR MODE IDENTIFICATION

2004 ◽  
Vol 12 (02) ◽  
pp. 175-196 ◽  
Author(s):  
MICHAEL I. TAROUDAKIS ◽  
GEORGE TZAGKARAKIS
Keyword(s):  
Wavelet Transform ◽  
Shallow Water ◽  
Underwater Acoustics ◽  
Low Frequency ◽  
Energy Concentration ◽  
Propagation Mode ◽  
Mode Identification ◽  
Time Frequency ◽  
Frequency Representation ◽  
Calculation Point

This paper is concerned with the use of the reassigned wavelet transform for mode identification in shallow water acoustic propagation. Mode identification is important for inverse procedures in underwater acoustics. An efficient way to recognize the modal structure of the acoustic field when a single hydrophone is available is to refer to the time frequency analysis of the recorded signal using wavelet transform. However, the standard wavelet transform in some cases may result in an obscure representation of the dispersion curves. Thus, a reassigned process is proposed which brings important improvements in the time frequency representation of the signal. This is achieved by moving the calculation point of the scalogram in the center of gravity of the energy concentration, associated with each one of the propagating modes. This argument is supported by two illustrative examples corresponding to propagation of low frequency tomographic signals, in shallow water.

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

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