scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

scholarly journals Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

2021 ◽  
pp. 135481662110584
Author(s):  
Ying Wang ◽  
Hongwei Zhang ◽  
Wang Gao ◽  
Cai Yang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Spillover Effects ◽  
Short Term ◽  
Time Frequency ◽  
Frequency Domains ◽  
Media Hype ◽  
The Time Domain ◽  
The Impact

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

Channel Estimating Based on Space-Time-Frequency Pilot for MIMO-OFDM

2012 ◽  
Vol 429 ◽  
pp. 179-185
Author(s):  
Hui Liu ◽  
Jing Shan Jiao ◽  
Fu Chun Zhang ◽  
Ling Zhou
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Space Time ◽  
Three Dimensions ◽  
Space Domain ◽  
Time Frequency ◽  
Time Space ◽  
Mimo Ofdm ◽  
The Time Domain ◽  
Inverse Operation

The pilots that are transmitted by different transmitting antennas must be orthogonal after being shifted. So the time domain channel estimating solution is deduced through LS based on the MIMO-OFDM channel estimating model. The time domain solution need the inverse operation of matrix, and its operating quantity is large. So the three dimensions pilot based on space domain, time domain and frequency domain is designed. The method need not the inverse operation of matrix for the time domain channel estimating solution and can reduce the complexity of channel estimating and make the channel estimating error minimum. It is shown from the simulation that the channel estimating method of this paper based on space domain, time space and frequency domain pilot has better MSE and BER performances compared with the traditional LS algorithm and the document algorithm.

scholarly journals Automatic traveltime picking using instantaneous traveltime

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. T53-T58 ◽  
Author(s):  
Christos Saragiotis ◽  
Tariq Alkhalifah ◽  
Sergey Fomel
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Field Data ◽  
Seismic Event ◽  
Seismic Signals ◽  
Recording Time ◽  
Time Frequency ◽  
Frequency Representation ◽  
Automatic Picking ◽  
The Time Domain

Event picking is used in many steps of seismic processing. We present an automatic event picking method that is based on a new attribute of seismic signals, instantaneous traveltime. The calculation of the instantaneous traveltime consists of two separate but interrelated stages. First, a trace is mapped onto the time-frequency domain. Then the time-frequency representation is mapped back onto the time domain by an appropriate operation. The computed instantaneous traveltime equals the recording time at those instances at which there is a seismic event, a feature that is used to pick the events. We analyzed the concept of the instantaneous traveltime and demonstrated the application of our automatic picking method on dynamite and Vibroseis field data.

scholarly journals Transformation Algorithm of Dielectric Response in Time-Frequency Domain

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ji Liu ◽  
Daning Zhang ◽  
Xinlao Wei ◽  
Hamid Reza Karimi
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Dielectric Response ◽  
Low Frequency ◽  
Dissipation Factor ◽  
Depolarization Current ◽  
Ultralow Frequency ◽  
Time Frequency ◽  
Transformation Algorithm ◽  
The Time Domain

A transformation algorithm of dielectric response from time domain to frequency domain is presented. In order to shorten measuring time of low or ultralow frequency dielectric response characteristics, the transformation algorithm is used in this paper to transform the time domain relaxation current to frequency domain current for calculating the low frequency dielectric dissipation factor. In addition, it is shown from comparing the calculation results with actual test data that there is a coincidence for both results over a wide range of low frequencies. Meanwhile, the time domain test data of depolarization currents in dry and moist pressboards are converted into frequency domain results on the basis of the transformation. The frequency domain curves of complex capacitance and dielectric dissipation factor at the low frequency range are obtained. Test results of polarization and depolarization current (PDC) in pressboards are also given at the different voltage and polarization time. It is demonstrated from the experimental results that polarization and depolarization current are affected significantly by moisture contents of the test pressboards, and the transformation algorithm is effective in ultralow frequency of 10−3 Hz. Data analysis and interpretation of the test results conclude that analysis of time-frequency domain dielectric response can be used for assessing insulation system in power transformer.

Summarization of pattern recognition method for phase sensitive optical time domain reflecting system

2018 ◽  
Vol 47 (7) ◽  
pp. 722001
Author(s):  
付群健 Fu Qunjian ◽  
于 淼 Yu Miao ◽  
常天英 Chang Tianying ◽  
张 瑾 Zhang Jin ◽  
罗政纯 Luo Zhengchun ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Time Domain ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Phase Sensitive ◽  
Optical Time

scholarly journals Novel Burst Suppression Segmentation in the Joint Time-Frequency Domain for EEG in Treatment of Status Epilepticus

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Jaeyun Lee ◽  
Woo-Jin Song ◽  
Hyang Woon Lee ◽  
Hyun-Chool Shin
Keyword(s):  
Status Epilepticus ◽  
Frequency Domain ◽  
Time Domain ◽  
Suppression Ratio ◽  
Likelihood Estimation ◽  
Burst Suppression ◽  
Time Frequency ◽  
Frequency Domains ◽  
Conventional Methods ◽  
The Time Domain

We developed a method to distinguish bursts and suppressions for EEG burst suppression from the treatments of status epilepticus, employing the joint time-frequency domain. We obtained the feature used in the proposed method from the joint use of the time and frequency domains, and we estimated the decision as to whether the measured EEG was a burst segment or suppression segment by the maximum likelihood estimation. We evaluated the performance of the proposed method in terms of its accordance with the visual scores and estimation of the burst suppression ratio. The accuracy was higher than the sole use of the time or frequency domains, as well as conventional methods conducted in the time domain. In addition, probabilistic modeling provided a more simplified optimization than conventional methods. Burst suppression quantification necessitated precise burst suppression segmentation with an easy optimization; therefore, the excellent discrimination and the easy optimization of burst suppression by the proposed method appear to be beneficial.

Sign in / Sign up

Export Citation Format

Share Document