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
Author(s):  
Pavan Kumar Yeditha ◽  
Tarun Pant ◽  
Maheswaran Rathinasamy ◽  
Ankit Agarwal

Abstract With the increasing stress on water resources for a developing country like India, it is pertinent to understand the dominant streamflow patterns for effective planning and management activities. This study investigates the spatiotemporal characterization of streamflow of six unregulated catchments in India. Firstly, Mann Kendall (MK) and Changepoint analysis were carried out to detect the presence of trends and any abrupt changes in hydroclimatic variables in the chosen streamflows. To unravel the relationships between the temporal variability of streamflow and its association with precipitation and global climate indices, namely, Niño 3.4, IOD, PDO, and NAO, continuous wavelet transform is used. Cross-wavelet transform and wavelet coherence analysis was also used to capture the coherent and phase relationships between streamflow and climate indices. The continuous wavelet transforms of streamflow data revealed that intra-annual (0.5 years), annual (1 year), and inter-annual (2–4 year) oscillations are statistically significant. Furthermore, a better understanding of the in-phase relationship between the streamflow and precipitation at intra-annual and annual time scales were well-captured using wavelet coherence analysis compared to cross wavelet transform. Furthermore, our analysis also revealed that streamflow observed an in-phase relationship with IOD and NAO, whereas a lag correlation with Niño 3.4 and PDO indices at intra-annual, annual and interannual time scales.


2011 ◽  
Vol 1 (32) ◽  
pp. 69 ◽  
Author(s):  
Li-Chung Wu ◽  
Beng-Chun Lee ◽  
Chia Chuen Kao ◽  
Dong-Jiing Doong ◽  
Chih-Chiang Chang

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.


2016 ◽  
Vol 23 (5) ◽  
pp. 341-359 ◽  
Author(s):  
Arnida L. Latifah ◽  
E. van Groesen

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.


1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.


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