scholarly journals APPLYING THE WAVELET TRANSFORM TO STUDY THE FEATURES OF FREAK WAVES

2011 ◽  
Vol 1 (32) ◽  
pp. 69 ◽  
Author(s):  
Li-Chung Wu ◽  
Beng-Chun Lee ◽  
Chia Chuen Kao ◽  
Dong-Jiing Doong ◽  
Chih-Chiang Chang
Keyword(s):  
Wavelet Transform ◽  
Wave Energy ◽  
The Other ◽  
Freak Waves ◽  
Energy Characteristics ◽  
Freak Wave

The issues of freak waves are more and more popular since the late 1980s. This study tries to use the wavelet scalogram of freak wave records to investigate the energy characteristics during the occurrence of the freak waves. Through the analysis of the wave energy and phase, it is found that as freak waves occur, the component waves will lead to constructive superposition due to similar phases. The wavelet scalogram provides the other idea to explain the feature of freak waves.

scholarly journals FREAK WAVE AND WEATHER CONDITION

2011 ◽  
Vol 1 (32) ◽  
pp. 70
Author(s):  
Nobuhito Mori ◽  
Hajime Mase ◽  
Tomohiro Yasuda
Keyword(s):  
Numerical Simulations ◽  
Weather Condition ◽  
The Other ◽  
Surface Elevation ◽  
Fourth Quadrant ◽  
Wave Interactions ◽  
Freak Waves ◽  
Freak Wave

The kurtosis of the surface elevation, Benjamin-Feir Index (BFI) and directional spread are measures of nonlinear four-wave interactions and freak waves. The dependence of kurtosis, BFI and directional spread under typhoon conditions are examined by numerical simulations. The BFI is significantly large in the fourth quadrant of the typhoon while the directional spread is small in the fourth quadrant. It was found that the potentially possible area of freak wave occurrence is the fourth quadrant of the typhoon rather than the other quadrants.

scholarly journals Localized coherence of freak waves

2016 ◽  
Vol 23 (5) ◽  
pp. 341-359 ◽  
Author(s):  
Arnida L. Latifah ◽  
E. van Groesen
Keyword(s):  
Wavelet Transform ◽  
Critical Group ◽  
Time Signal ◽  
Wave Group ◽  
Freak Waves ◽  
Freak Wave ◽  
Irregular Wave ◽  
Local Coherence ◽  
Wavelet Transform Analysis ◽  
Energy Convergence

Abstract. This paper investigates in detail a possible mechanism of energy convergence leading to freak waves. We give examples of a freak wave as a (weak) pseudo-maximal wave to illustrate the importance of phase coherence. Given a time signal at a certain position, we identify parts of the time signal with successive high amplitudes, so-called group events, that may lead to a freak wave using wavelet transform analysis. The local coherence of the critical group event is measured by its time spreading of the most energetic waves. Four types of signals have been investigated: dispersive focusing, normal sea condition, thunderstorm condition and an experimental irregular wave. In all cases presented in this paper, it is shown that a high correlation exists between the local coherence and the appearance of a freak wave. This makes it plausible that freak waves can be developed by local interactions of waves in a wave group and that the effect of waves that are not in the immediate vicinity is minimal. This indicates that a local coherence mechanism within a wave group can be one mechanism that leads to the appearance of a freak wave.

Wavelet transform based coherence analysis of freak wave and its impact

2005 ◽  
Vol 32 (13) ◽  
pp. 1572-1589 ◽  
Author(s):  
S.H. Kwon ◽  
H.S. Lee ◽  
C.H. Kim
Keyword(s):  
Wavelet Transform ◽  
Coherence Analysis ◽  
Freak Wave

Non-Gaussian Extreme Waves in the Central North Sea

1997 ◽  
Vol 119 (3) ◽  
pp. 146-150 ◽  
Author(s):  
J. Skourup ◽  
N.-E. O. Hansen ◽  
K. K. Andreasen
Keyword(s):  
Wave Height ◽  
North Sea ◽  
Upper Bound ◽  
Wave Crest ◽  
Extreme Waves ◽  
Freak Waves ◽  
Freak Wave ◽  
Wave Trains ◽  
Central North Sea ◽  
Crest Height

The area of the Central North Sea is notorious for the occurrence of very high waves in certain wave trains. The short-term distribution of these wave trains includes waves which are far steeper than predicted by the Rayleigh distribution. Such waves are often termed “extreme waves” or “freak waves.” An analysis of the extreme statistical properties of these waves has been made. The analysis is based on more than 12 yr of wave records from the Mærsk Olie og Gas AS operated Gorm Field which is located in the Danish sector of the Central North Sea. From the wave recordings more than 400 freak wave candidates were found. The ratio between the extreme crest height and the significant wave height (20-min value) has been found to be about 1.8, and the ratio between extreme crest height and extreme wave height has been found to be 0.69. The latter ratio is clearly outside the range of Gaussian waves, and it is higher than the maximum value for steep nonlinear long-crested waves, thus indicating that freak waves are not of a permanent form, and probably of short-crested nature. The extreme statistical distribution is represented by a Weibull distribution with an upper bound, where the upper bound is the value for a depth-limited breaking wave. Based on the measured data, a procedure for determining the freak wave crest height with a given return period is proposed. A sensitivity analysis of the extreme value of the crest height is also made.

The Study of Tire Pattern Noise by Using Wavelet Transform

2011 ◽  
Vol 105-107 ◽  
pp. 267-270 ◽  
Author(s):  
Sung Wook Hwang ◽  
Jin Hyuk Han ◽  
Ki Duck Sung ◽  
Sang Kwon Lee
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
The Other ◽  
Road Noise ◽  
Signal Process ◽  
The Road ◽  
Pattern Noise ◽  
On The Road ◽  
Non Stationary Signal ◽  
The Relationship

Tire noise is classified by pattern noise and road noise in a vehicle. Especially pattern noise has impulsive characteristics since it is generated by impacting of tire’s block on the road. Therefore, a special signal process is needed other than traditional Fourier Transform, because the characteristic of signal is varying with time. On the other hand, the pattern noise is a kind of non-stationary signal and is related to the impulsive train of pitch sequence of a block. In this paper, Wavelet Transform is applied to verify the impulse signal caused by impact of block and groove and to verify the relationship between the pattern noise and the train of pitch sequence.

Differential privacy preserving clustering using Daubechies-2 wavelet transform

2015 ◽  
Vol 13 (04) ◽  
pp. 1550028 ◽  
Author(s):  
Mohammad Reza Ebrahimi Dishabi ◽  
Mohammad Abdollahi Azgomi
Keyword(s):  
Wavelet Transform ◽  
Clustering Analysis ◽  
Differential Privacy ◽  
Original Data ◽  
Privacy Preserving ◽  
The Other ◽  
High Dimensionality ◽  
Worst Case ◽  
Strong Notion

Most of the existing privacy preserving clustering (PPC) algorithms do not consider the worst case privacy guarantees and are based on heuristic notions. In addition, these algorithms do not run efficiently in the case of high dimensionality of data. In this paper, to alleviate these challenges, we propose a new PPC algorithm, which is based on Daubechies-2 wavelet transform (D2WT) and preserves the differential privacy notion. Differential privacy is the strong notion of privacy, which provides the worst case privacy guarantees. On the other hand, most of the existing differential-based PPC algorithms generate data with poor utility. If we apply differential privacy properties over the original raw data, the resulting data will offer lower quality of clustering (QOC) during the clustering analysis. Therefore, we use D2WT for the preprocessing of the original data before adding noise to the data. By applying D2WT to the original data, the resulting data not only contains lower dimension compared to the original data, but also can provide differential privacy guarantee with high QOC due to less noise addition. The proposed algorithm has been implemented and experimented over some well-known datasets. We also compare the proposed algorithm with some recently introduced algorithms based on utility and privacy degrees.

A Numerical Prediction on the Transient Response of a Spar-Type Floating Offshore Wind Turbine in Freak Waves

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Yan Li ◽  
Xiaoqi Qu ◽  
Liqin Liu ◽  
Peng Xie ◽  
Tianchang Yin ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Wind Turbine ◽  
Offshore Wind ◽  
Numerical Code ◽  
Modulation Method ◽  
Offshore Wind Turbine ◽  
Freak Waves ◽  
Freak Wave ◽  
Floating Offshore Wind Turbine ◽  
The Impact

Abstract Simulations are conducted in time domain to investigate the dynamic response of a spar-type floating offshore wind turbine (FOWT) under the freak wave scenarios. Toward this end, a coupled aero-hydro-mooring in-house numerical code is adopted to perform the simulations. The methodology includes a blade-element-momentum (BEM) model for simulating the aerodynamic loads, a nonlinear model for simulating the hydrodynamic loads, a nonlinear restoring model of Spar buoy, and a nonlinear algorithm for simulating the mooring cables. The OC3 Hywind spar-type FOWT is adopted as an example to study the dynamic response under the freak wave conditions, meanwhile the time series of freak waves are generated using the random frequency components selection phase modulation method. The motion of platform, the tension applied on the mooring lines, and the power generation performance are documented in several cases. According to the simulations, it is indicated that when a freak wave acts on the FOWT, the transient motion of the FOWT is induced in all degrees-of-freedom, as well as the produced power decreases rapidly. Furthermore, the impact of freak wave parameters on the motion of FOWT is discussed.

Freak Wave Generation in a Wave Basin With HOSM-WG Method

2015 ◽  
Author(s):  
Hidetaka Houtani ◽  
Takuji Waseda ◽  
Wataru Fujimoto ◽  
Keiji Kiyomatsu ◽  
Katsuji Tanizawa
Keyword(s):  
Control Method ◽  
Irregular Waves ◽  
Frequency Bandwidth ◽  
Freak Waves ◽  
Freak Wave ◽  
Wave Basin ◽  
Wave Trains ◽  
Directional Wave ◽  
Wave Maker ◽  
The Difference

A method to produce freak waves with arbitrary spectrum in a fully directional wave basin is presented here. This is an extension of Waseda, Houtani and Tanizawa at OMAE 2013[1], which used “HOSM-WG” based on the higher-order spectral method (HOSM). We used the following three methods to improve the HOSM-WG in [1]: “separation of free waves from bound waves,” “using Biesel’s transfer function in wavenumber space” and “using Schaffer’s 2nd-order wave maker control method.” Modulational wave trains, freak waves in unidirectional irregular waves and freak waves in short-crested irregular waves were generated in a wave basin. The experimental results using the improved HOSM-WG were compared to the HOSM simulation, and good agreements were found. The effectiveness of the improved HOSM-WG was ascertained. We showed that the difference between HOSM-WG and HOSM simulations became larger as wave steepness, frequency bandwidth of the spectrum or directional spreading became larger.

Traditional Chinese Medicine Pulse-Condition Simulation and Wavelet Recognition Method

2010 ◽  
Vol 139-141 ◽  
pp. 2029-2032
Author(s):  
Dong Cao ◽  
Jian Wei Ye ◽  
Jun Yi ◽  
Wen Jie Ruan ◽  
Chong Chen
Keyword(s):  
Wavelet Transform ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Discrete Wavelet Transform ◽  
Subjective Experience ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Energy Characteristics ◽  
Pulse Condition

The human pulse-condition diagnosis is an important part of the traditional Chinese medicine (TCM) which is difficult to recognize accurately by doctor’s subjective experience. Objective identification of pulse-conditions has important meanings for modernization of TCM. In this paper human pulse-condition system transfer function model and model parameter estimation were introduced, which are used to construct four kinds of typical pulse-conditions simulation signals. There are normal pulse, taut pulse, slippery pulse and thready pulse. And then, discrete wavelet transform for extracting the multi-scale energy characteristics and wavelet packet decomposition for extracting the multi-band energy characteristics are proposed so as to recognize the pulse-conditions simulation signals. The results show that the recognition effect of discrete wavelet transform method is better. Moreover, the data features of characteristic parameters demonstrate the reality of simulation signals.

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