Time–frequency energy distribution of phrenic nerve discharges during aspiration reflex, cough and quiet inspiration

2011 ◽  
Vol 102 (1) ◽  
pp. 81-90 ◽  
Author(s):  
Juliana Knociková
Keyword(s):  
Energy Distribution ◽  
Phrenic Nerve ◽  
Time Frequency ◽  
Reflex Cough ◽  
Aspiration Reflex

Simulation Study of Damage Identification Method Based on Lamb Wave Scattering in Aluminium Plate

2012 ◽  
Vol 433-440 ◽  
pp. 2611-2618
Author(s):  
Zhen Hua Tian ◽  
Hong Yuan Li ◽  
Hong Xu
Keyword(s):  
Energy Distribution ◽  
Lamb Wave ◽  
Damage Identification ◽  
Optimization Method ◽  
Propagation Path ◽  
Transient Dynamic Analysis ◽  
Time Frequency ◽  
Damage Location ◽  
Identification Method ◽  
Short Time

The propagation of scattering Lamb wave in plate was simulated using transient dynamic analysis in ANSYS. In order to extract the characteristic information of received signal for damage identification, the short time Fourier transform based on time-frequency analysis was utilized, and then the energy distribution and envelop of received signal were obtained. Based on the displacement contour of simulation and energy distribution, the propagation of scattering wave in plate with a through hole was examined. Also, a mathematic relationship between damage location and scattering signal was developed, with the help of wave propagation path through actuator, damage and sensor. A nonlinear optimization method was applied on the mathematic relationship to obtain the damage location. The damage identification method using scattering Lamb wave was therefore established.

The Energy Distribution of Tire-Road Noise and Noisy Speech Based on Wavelet Packet

2011 ◽  
Vol 464 ◽  
pp. 721-724 ◽  
Author(s):  
Zhi Yong He ◽  
Li Heng Luo
Keyword(s):  
Energy Distribution ◽  
Speech Enhancement ◽  
Mobile Communications ◽  
Wavelet Packet ◽  
Noise Signal ◽  
Wavelet Packets ◽  
Road Noise ◽  
Noisy Speech ◽  
Time Frequency ◽  
The Difference

Speech enhancement is very important for mobile communications or some other applications in car. The energy distribution of signal is the basis of algorithms which denoise noisy speech in time-frequency domain. In this work, the noise regarded is the tire-road noise when driving in expressway. Wavelet packets transform is used in the analysis. After decomposing noise signal and noisy speech signal by wavelet packet transform, the analysis for the difference of the energy distribution between noisy speech and noise is finished.

Copula-Based Time-Frequency Distribution Analysis for Planetary Gearbox Fault Detection

2017 ◽  
Author(s):  
Libin Liu ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Energy Distribution ◽  
Simulated Data ◽  
Frequency Resolution ◽  
Data Sets ◽  
Distribution Analysis ◽  
Frequency Distributions ◽  
Copula Theory ◽  
Planetary Gearbox ◽  
Time Frequency

Linear and bilinear time-frequency distributions (TFDs) have been employed in planetary gearbox fault diagnosis. For linear TFDs, there is a trade-off between the time localization and frequency resolution and the spectrogram may not have correct energy marginals. For bilinear TFDs, they cannot be interpreted as an energy distribution because of the existence of possible negative values even though they are designed for energy density representation. To overcome these shortcomings, TFDs based on copula theory have been reported in the literature. In this paper, we analyze two simulated data sets using linear TFD and copula-based TFD. The results show that the constructed copula-based TFD has desirable properties of being positive, free from cross-term interference, having high time-frequency resolution and matching well with true marginals. The copula-based TFD is also able to locate fault-induced impulses by vertical lines over a certain frequency range in the time-frequency domain. Consequently, this study confirms the advantages of the copula-based TFD as an energy distribution and its capability in fault detection for planetary gearboxes.

Estimation and Extraction of Radar Signal Features Using Modified B Distribution and Particle Filters

Frequenz ◽  
2016 ◽  
Vol 70 (9-10) ◽  
Author(s):  
Davorin Mikluc ◽  
Dimitrije Bujaković ◽  
Milenko Andrić ◽  
Slobodan Simić
Keyword(s):  
Energy Distribution ◽  
Process Model ◽  
Particle Filters ◽  
Maximum Energy ◽  
Radar Signal ◽  
Mean Power ◽  
Time Frequency ◽  
Signal Features ◽  
Power Spectral ◽  
Frequency Representation

AbstractThe research analyses the application of particle filters in estimating and extracting the features of radar signal time-frequency energy distribution. Time-frequency representation is calculated using modified B distribution, where the estimation process model represents one time bin. An adaptive criterion for the calculation of particle weighted coefficients whose main parameters are frequency integral squared error and estimated maximum of mean power spectral density per one time bin is proposed. The analysis of the suggested estimation application has been performed on a generated signal in the absence of any noise, and consequently on modelled and recorded real radar signals. The advantage of the suggested method is in the solution of the issue of interrupted estimations of instantaneous frequencies which appears when these estimations are determined according to maximum energy distribution, as in the case of intersecting frequency components in a multicomponent signal.

scholarly journals Time–frequency analysis of the sea state with the Andrea freak wave

2014 ◽  
Vol 14 (12) ◽  
pp. 3143-3150 ◽  
Author(s):  
Z. Cherneva ◽  
C. Guedes Soares
Keyword(s):  
Time Series ◽  
Energy Distribution ◽  
Universal Time ◽  
Sea State ◽  
Freak Wave ◽  
High Wave ◽  
Time Frequency ◽  
Wigner Spectrum ◽  
Central North Sea ◽  
Field Wave

Abstract. The nonlinear and nonstationary properties of a special field wave record are analysed with the Wigner spectrum with the Choi–Williams kernel. The wave time series, which was recorded at the Ekofisk complex in the central North Sea at 00:40 UTC (universal time coordinated) on 9 November 2007, contains an abnormally high wave known as the "Andrea" wave. The ability of the Wigner spectrum to reveal the wave energy distribution in frequency and time is demonstrated. The results are compared with previous investigations for different sea states and also the state with Draupner's abnormal "New Year" wave.

An Investigation of Tooth Wear Diagnosis of Gear Based on Process Systems Modeling

2012 ◽  
Vol 549 ◽  
pp. 834-838
Author(s):  
Feng Li Wang ◽  
Hui Xing
Keyword(s):  
Energy Distribution ◽  
Frequency Spectrum ◽  
Frequency Analysis ◽  
Vibration Signal ◽  
Tooth Wear ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Gear Fault ◽  
Local Wave ◽  
Instantaneous Energy

Targeting the advantages of local wave analysis(LWA) and the characteristics of gear fault vibration signals, LWA is introduced into gear fault diagnosis. The concept of the instantaneous energy in time- frequency analysis, based on local wave time-frequency spectrum, was used to measure the energy distribution of the signal in time-frequency domain. Furthermore, when tooth wear occurs in gear, the energy of the gear vibration signal would change correspondingly, whilst local wave time-frequency spectrum can exactly provide the instantaneous energy distribution of the signal with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local wave time-frequency spectrum. The analysis results from the experimental signals show that local wave time-frequency analysis could extract the characteristics information of the gear fault vibration signal effectively.

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