Application of Continuous Wavelet Analysis in Distinguishing Breaking and Nonbreaking Waves in the Wind–Wave Time Series

Elsayed, Mohamed A. K.

doi:10.2112/05-0443.1

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…6 Measured and predicted wave height at point 1 d, Egmond, the Netherlands that are located above the inner bar in Egmond site.. The peaks shown in these plots correspond to the events of wave breaking as found in our earlier study (Elsayed, 2008). Besides these results agree with the findings of Liu (1993).…”

Section: Hrms (M)supporting

confidence: 86%

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Analyzing wave breaking in a barred beach using wavelet

Elsayed

2011

J Coast Conserv

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In this paper, a cross-shore profile evolution model, Uniform Beach Sediment Transport-TimeAveraged Cross-Shore (UNIBEST-TC), is used. The model was developed at WL/Delft hydraulic laboratory in the Netherlands. The model is used to predict wave height in a barred beach (Egmond site, The Netherlands) and the results show that there is a good agreement between the measured and predicted values by the model. In the present study, Morlet wavelet is used to distinguish the breaking waves; it is integrated over frequency to provide the temporal variation of localized total energy. The study shows that the local peaks of the energy densities correspond to the events of wave breaking in the predicted-wave time series. Furthermore, the wave energy distribution shows a tendency to decrease in the off-shore direction of the inner bar.

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Section: Hrms (M)supporting

confidence: 86%

“…The MATLAB code developed by Torrence and Compo (1998) to compute the continuous wavelet transform of the time series of the signal is modified to compute the localized total energy and this approach was carried out successfully by Elsayed (2008) andLIU (1993) .…”

Section: Local Total Energymentioning

confidence: 99%

Analyzing wave breaking in a barred beach using wavelet

Elsayed

2011

J Coast Conserv

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“…Spectral and wavelet analysis are widely used in signal processing and analysis (Elsayed, 2008;Pomeroy et al, 2015). They were used in the analysis to process flow velocity and SSC data.…”

Section: Spectral and Wavelet Analysismentioning

confidence: 99%

Field observations of seabed scour dynamics in front of a seawall during winter gales

Sun

Xu²,

Zhang³

et al. 2022

Front. Mar. Sci.

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Seawalls are vital for protecting coastal areas. However, the seabed in front of seawalls may undergo severe scouring. This can result in destabilization of the seawall structure, the underlying mechanisms of which remains unclear. Therefore, an integrated observation system consisting of acoustic and optical instruments was deployed in areas with severe seabed scouring. This observation system was used to observe sediment dynamics elements such as waves, currents, tides, turbulence and suspended sediment concentration (SSC) for 31 days in winter. Using advanced time–frequency analysis techniques including wavelet transform and spectrum analysis, we examined the dynamic factors associated with sediment resuspension and transport. In calm weather conditions, a notable increase in SSC was not observed indicating that the tidal dynamics were not sufficient for sediment suspension. During high winds, the SSC increased sharply to 12,222 mg/L, and the sediment vertical diffusion flux induced by turbulence was coupled to SSC, indicating that the increased SSC was predominantly attributed to local resuspension. Consistent temporal distribution of turbulence-induced sediment vertical diffusion flux and momentum flux in high wavelet power spectra highlights the important role of turbulence in sediment dynamics. Enhanced longshore currents during high wave conditions intensified sediment transportation. Horizontal net sediment fluxes notably increased to 769 t/m2 per day during winter gales, which had a significant effect on seabed erosion. This study reveals the key processes associated with seabed scouring in front of seawalls during gale events.

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“…For non-stationary signal, Fourier analysis may give false results, leading to faults in the diagnosis. For this kind of problem, wavelet analysis method has incomparable advantages [7]. Because of wavelet decomposition, especially in the wavelet packet decomposition technique, any signal (stationary or non-stationary) decomposes to the wavelet scale forms a cluster on the basic functions, yielding complete characterization of signal information channel sequence, along with the sequence of alternatives which can be used for in depth analysis, to obtain accurate fault diagnosis.…”

Section: )mentioning

confidence: 99%

Application Research on Fault Diagnosis of the Filter Unit Based on Intelligent Algorithm of GA and WNN

Zhao¹,

Mingfang²,

Wang³

et al. 2015

TOMEJ

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This paper focuses on the potential of GA algorithm for adaptive random global search, and WNN resolution as well as the ability of fault tolerance to build a multi intelligent algorithm based on the GA-WNN model using the filter unit of analog circuit for fault diagnosis. Construction of GA-WNN model was divided into two stages; in the first stage GA was used to optimize the initial weights, threshold, expansion factor and translation factor of WNN structure; while in the second stage, initially, based on WNN training and learning, global optimal solution was obtained. In the process of using analog output signal by using wavelet decomposition, the absolute value of coefficient of each frequency band sequence was obtained along with the energy characteristics of the cross joint, with a combination of feature vectors as the input of the neural network. Through the pretreatment method, in order to reduce the neural network input, neural grid size of neurons was reduced in each layer and the convergence speed of neural network was increased. The experimental results show that the method can diagnose single and multiple soft faults of the circuit, with high speed and high precision.

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Application of Continuous Wavelet Analysis in Distinguishing Breaking and Nonbreaking Waves in the Wind–Wave Time Series

Cited by 5 publications

References 15 publications

Analyzing wave breaking in a barred beach using wavelet

Analyzing wave breaking in a barred beach using wavelet

Field observations of seabed scour dynamics in front of a seawall during winter gales

Application Research on Fault Diagnosis of the Filter Unit Based on Intelligent Algorithm of GA and WNN

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