Human Being Detection Utilizing Wavelet Transform: an Advanced Algorithm of Time Window Selection

2019 ◽  
Author(s):  
Farnaz Mahmoudi Shikhsarmast ◽  
Tingting Lyu ◽  
Hao Zhang
Keyword(s):  
Wavelet Transform ◽  
Time Window ◽  
Human Being ◽  
Advanced Algorithm ◽  
Window Selection

Cross-borehole tomography with correlation delay times

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. R1-R12 ◽  
Author(s):  
E. Diego Mercerat ◽  
Guust Nolet ◽  
Christophe Zaroli
Keyword(s):  
Time Window ◽  
Body Wave ◽  
Wave Dispersion ◽  
Spectral Element ◽  
Frequency Theory ◽  
P Wave ◽  
Checkerboard Pattern ◽  
Finite Frequency ◽  
Delay Times ◽  
Window Selection

We evaluated a comprehensive numerical experiment of finite-frequency tomography with ray-based (“banana-doughnut”) kernels that tested all aspects of this method, starting from the generation of seismograms in a 3D model, the window selection, and the crosscorrelation with seismograms predicted for a background model, to the final regularized inversion. In particular, we tested if the quasilinearity of crosscorrelation delays allowed us to forego multiple (linearized) iterations in the case of strong reverberations characterizing multiple scattering and the gain in resolution that can be obtained by observing body-wave dispersion. Contrary to onset times, traveltimes observed by crosscorrelation allowed us to exploit energy arriving later in the time window centered in the P-wave or any other indentifiable ray arrival, either scattered from, or diffracted around, lateral heterogeneities. We tested using seismograms calculated by the spectral element method in a cross-borehole experiment conducted in a 3D checkerboard cube. The use of multiple frequency bands allowed us to estimate body-wave dispersion caused by diffraction effects. The large velocity contrast (10%) and the regularity of the checkerboard pattern caused severe reverberations that arrived late in the crosscorrelation windows. Nevertheless, the model resulting from the inversion with a data fit with reduced [Formula: see text] resulted in an excellent correspondence with the input model and allowed for a complete validation of the linearizations that lay at the basis of the theory. The use of multiple frequencies led to a significant increase in resolution. Moreover, we evaluated a case in which the sign of the anomalies in the checkerboard was systematically reversed in the ray-theoretical solution, a clear demonstration of the reality of the “doughnut-hole” effect. The experiment validated finite-frequency theory and disqualified ray-theoretical inversions of crosscorrelation delay times.

Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform and Its Application in Wind Turbine Gearbox Fault Diagnosis

2021 ◽  
Author(s):  
Zhaohong Yu ◽  
Cancan Yi ◽  
Xiangjun Chen ◽  
Tao Huang
Keyword(s):  
Wavelet Transform ◽  
Wind Turbine ◽  
Time Window ◽  
Vibration Signal ◽  
Second Order ◽  
Center Frequency ◽  
Wind Turbine Gearbox ◽  
Adaptive Wavelet ◽  
Time Frequency ◽  
Window Width

Abstract Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing Time-Frequency (TF) Analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated Time-Frequency Representation (TFR), this paper proposes a new TFA method, namely Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform (AMWSST2). Firstly, a short-time window is innovatively introduced on the foundation of classical Continuous Wavelet Transform (CWT), and the window width is adaptively optimized by using the center frequency and scale factor. After that, a smoothing process is carried out between different segments to eliminate the discontinuity and thus Adaptive Wavelet Transform (AWT) is generated. Then, on the basis of the theoretical framework of Synchrosqueezing Transform (SST) and accurate Instantaneous Frequency (IF) estimation by the utilization of second-order local demodulation operator, Adaptive Second-order Synchrosqueezing Wavelet Transform (AWSST2) is formed. Considering that the quality of actual time-frequency analysis is greatly disturbed by noise components, through performing multiple Synchrosqueezing operations, the congregation of TFR energy is further improved, and finally, the AMWSST2 algorithm studied in this paper is proposed. Since Synchrosqueezing operations are performed only in the frequency direction, this method AMWSST2 allows the signal to be perfectly reconstructed. For the verification of its effectiveness, this paper applies it to the processing of the vibration signal of the gearbox of a 750 kW wind turbine.

scholarly journals Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 269 ◽  
Author(s):  
Wei Zhang ◽  
Zhipeng Li ◽  
Xuyang Gao ◽  
Yanjun Li ◽  
Yibing Shi
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Arrival Time ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Information Criteria ◽  
Echo Signal ◽  
Time Frequency ◽  
Ultrasonic Echo ◽  
Ultrasonic Anemometer

The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

scholarly journals An automated time-window selection algorithm for seismic tomography

2009 ◽  
Vol 178 (1) ◽  
pp. 257-281 ◽  
Author(s):  
Alessia Maggi ◽  
Carl Tape ◽  
Min Chen ◽  
Daniel Chao ◽  
Jeroen Tromp
Keyword(s):  
Seismic Tomography ◽  
Time Window ◽  
Selection Algorithm ◽  
Window Selection

Rail crack monitoring based on Tsallis synchrosqueezed wavelet entropy of acoustic emission signals: A field study

2017 ◽  
Vol 17 (6) ◽  
pp. 1410-1424 ◽  
Author(s):  
Dan Li ◽  
Kevin Sze Chiang Kuang ◽  
Chan Ghee Koh
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Time Window ◽  
Field Tests ◽  
Local Variation ◽  
Monitoring Strategy ◽  
Wavelet Entropy ◽  
Time Frequency ◽  
Crack Monitoring ◽  
Synchrosqueezed Wavelet Transform

This article focuses on the rail crack monitoring using acoustic emission technique in the field typically with complex cracking conditions and high operational noise. A novel crack monitoring strategy based on Tsallis synchrosqueezed wavelet entropy was developed, where synchrosqueezed wavelet transform was introduced to explore the time–frequency characteristics of acoustic emission signals and Tsallis entropy was adopted to quantify the local variation of acoustic emission wavelet coefficients more accurately. The mother wavelet of synchrosqueezed wavelet transform and three key parameters of time-Tsallis synchrosqueezed wavelet entropy, including characteristic frequency band, non-extensive parameter, and time window length, were appropriately determined. The performance of the strategy was validated through field tests with an incipient rail crack and trains running at operating speeds. Time-Tsallis synchrosqueezed wavelet entropy efficiently detected and located the crack by extracting the crack-related transients in acoustic emission signals that were easily submerged in the operational noise. Synchrosqueezed wavelet transform further helped to analyze the mechanisms of these crack-related transients, which were distinguished to be either crack propagation or impact. The experimental results demonstrated that the crack monitoring strategy proposed is able to detect both surface and internal rail cracks even in the noisy environment, highlighting its potential for field applications.

Principal component transforms of triaxial recordings by singular value decomposition

Geophysics ◽  
1991 ◽  
Vol 56 (4) ◽  
pp. 528-533 ◽  
Author(s):  
G. M. Jackson ◽  
I. M. Mason ◽  
S. A. Greenhalgh
Keyword(s):  
Singular Value Decomposition ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Principal Component ◽  
Singular Value ◽  
Data Matrix ◽  
Single Station ◽  
Window Selection ◽  
Value Decomposition

Polarization analysis can be achieved efficiently by treating a time window of a single‐station triaxial recording as a matrix and doing a singular value decomposition (SVD) of this seismic data matrix. SVD of the triaxial data matrix produces an eigenanalysis of the data covariance (cross‐energy) matrix and a rotation of the data onto the directions given by the eigenanalysis (Karhunen‐Loève transform), all in one step. SVD provides a complete principal components analysis of the data in the analysis time window. Selection of this time window is crucial to the success of the analysis and is governed by three considerations: the window should contain only one arrival; the window should be such that the signal‐to‐noise ratio is maximized; and the window should be long enough to be able to discriminate random noise from signal. The SVD analysis provides estimates of signal, signal polarization directions, and noise. An F‐test is proposed which gives the confidence level for the hypothesis of rectilinear polarization. This paper illustrates the analysis and interpretation of synthetic rectilinearly and elliptically polarized arrivals at a single triaxial station by SVD.

scholarly journals A Modified Digital Image Processing Enabled Stroke Rehabilitation System based on Wavelet Transform

2019 ◽  
Vol 9 (1S) ◽  
pp. 234-239
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Real Time ◽  
Stroke Rehabilitation ◽  
Finger Movement ◽  
Discrete Wavelet ◽  
Robotic Hand ◽  
Human Being ◽  
Feature Extraction Method ◽  
Gesture Control

stroke rehabilitation therapy is for people suffering from paralysis. Regular procedures need the involvement of analyst for the duration of the plenary, requires excessive amount. Recently, many approaches have been proposed with control strategy through gesture recognition. The main objective of this work is hand gesture control for stroke rehabilitation based on dwt based feature extraction method. Establishment of artificial machine based computer system assistance provides instruction for paralysis recovery system. The proposed system uses reverse biorthogonal wavelet with two-level decomposition. Preprocessing and image segmentation of real time movement of the hand gestures. Feature extraction is done using discrete wavelet transform (dwt) based approach which has very flexible and adaptable even at the cost of imperfect reconstruction. The prosthetic based robotic hand for human finger movement recovery function. Human being finger movement process helps to rotate motors packed inside the robotic finger section. Therefore, the five finger machine finger movement can imitate the real time human being gesture in a regular manner, which indicates the newly modified machine represents as a education device to provide recovery for the persons affected after paralysis.

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