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.

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.

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.

Effect of Time and Frequency Manipulation on Syllable Perception in Developmental Dyslexics

1997 ◽  
Vol 40 (4) ◽  
pp. 912-924 ◽  
Author(s):  
Ken I. McAnally ◽  
Peter C. Hansen ◽  
Piers L. Cornelissen ◽  
John F. Stein
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Developmental Dyslexia ◽  
Stop Consonant ◽  
Stop Consonants ◽  
Systematic Improvement ◽  
Frequency Domains ◽  
The Time Domain ◽  
Time And Frequency Domains

Many people with developmental dyslexia have difficulty perceiving stop consonant contrasts as effectively as other people and it has been suggested that this may be due to perceptual limitations of a temporal nature. Accordingly, we predicted that perception of such stimuli by listeners with dyslexia might be improved by stretching them in time—equivalent to speaking slowly. Conversely, their perception of the same stimuli ought to be made even worse by compressing them in time—equivalent to speaking quickly. We tested 15 children with dyslexia on their ability to identify correctly consonant-vowel-consonant (CVC) stimuli that had been stretched or compressed in the time domain. We also tested their perception of the same CVC stimuli after the formant transitions had been stretched or compressed in the frequency domain. Contrary to our predictions, we failed to find any systematic improvement in their performance with either manipulation. We conclude that simple manipulations in the time and frequency domains are unlikely to benefit the ability of people with dyslexia to discriminate between CVCs containing stop consonants.

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 Application of Correlation Analysis for Assessment of Infrasound Signals Emission by Wind Turbines

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6891
Author(s):  
Tomasz Boczar ◽  
Dariusz Zmarzły ◽  
Michał Kozioł ◽  
Daria Wotzka
Keyword(s):  
Correlation Analysis ◽  
Frequency Domain ◽  
Coherence Function ◽  
Wind Farms ◽  
Wavelet Coherence ◽  
Technical Parameters ◽  
Time Frequency ◽  
Frequency Domains ◽  
Frequency Transformations ◽  
The Time Domain

The study reported in this paper is concerned with areas related to developing methods of measuring, processing and analyzing infrasound noise caused by operation of wind farms. The paper contains the results of the correlation analysis of infrasound signals generated by a wind turbine with a rated capacity of 2 MW recorded by three independent measurement setups comprising identical components and characterized by the same technical parameters. The measurements of infrasound signals utilized a dedicated measurement system called INFRA, which was developed and built by KFB ACOUSTICS Sp. z o.o. In particular, the scope of the paper includes the results of correlation analysis in the time domain, which was carried out using the autocovariance function separately for each of the three measuring setups. Moreover, the courses of the cross-correlation function were calculated separately for each of the potential combinations of infrasound range recorded by the three measuring setups. In the second stage, a correlation analysis of the recorded infrasound signals in the frequency domain was performed, using the coherence function. In the next step, infrasound signals recorded in three setups were subjected to time-frequency transformations. In this part, the waveforms of the scalograms were determined by means of continuous wavelet transform. Wavelet coherence waveforms were calculated in order to determine the level of the correlation of the obtained dependencies in the time-frequency domain. The summary contains the results derived from using correlation analysis methods in the time, frequency and time-frequency domains.

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 Causality between Oil Prices and Tourist Arrivals

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 134-154 ◽  
Author(s):  
Xu Huang ◽  
Emmanuel Silva ◽  
Hossein Hassani
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Price ◽  
Economic Activities ◽  
Oil Price Volatility ◽  
Causality Tests ◽  
The Time Domain ◽  
The Impact

This paper investigates the causal relationship between oil price and tourist arrivals to further explain the impact of oil price volatility on tourism-related economic activities. The analysis itself considers the time domain, frequency domain, and information theory domain perspectives. Data relating to the US and nine European countries are exploited in this paper with causality tests which include the time domain, frequency domain, and Convergent Cross Mapping (CCM). The CCM approach is nonparametric and therefore not restricted by assumptions. We contribute to existing research through the successful and introductory application of an advanced method and via the uncovering of significant causal links from oil prices to tourist arrivals.

scholarly journals Detecting the Void behind the Tunnel Lining by Impact-Echo Methods with Different Signal Analysis Approaches

2019 ◽  
Vol 9 (16) ◽  
pp. 3280 ◽  
Author(s):  
Rongning Cao ◽  
Meng Ma ◽  
Ruihua Liang ◽  
Chao Niu
Keyword(s):  
Frequency Domain ◽  
Signal Analysis ◽  
Dynamic Stiffness ◽  
Tunnel Lining ◽  
Impact Echo ◽  
Time Frequency ◽  
Void Detection ◽  
Wavelet Transform Analysis ◽  
The Time Domain ◽  
The Impact

A void behind the lining in a tunnel is considered to be a critical condition as it can significantly impair the tunnel service life. In this study, we adopted the impact-echo (IE) method to detect the voids. We designed two test conditions (tunnel lining with and without a void) for our experiments performed in a laboratory environment. The influences of void size and impact-void position were analysed using numerical simulations. The vibration response signals were analysed in the time, frequency, and time–frequency domains using various signal analysis approaches. The results were comparatively analysed to determine the best approach for void detection. The study helped establish that a tunnel void can be evaluated through the vibration energy (amplitude and duration) in the time domain, the resonance frequency and dynamic stiffness in the frequency domain, and the energy distribution in time–frequency domain. The wavelet transform analysis is the most appropriate method to observe the energy flow during the state changing and the dynamic stiffness method can determine the void position precisely.

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.

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