scholarly journals Time–frequency analysis of the sea state with the Andrea freak wave

2014 ◽  
Vol 14 (12) ◽  
pp. 3143-3150 ◽  
Author(s):  
Z. Cherneva ◽  
C. Guedes Soares
Keyword(s):  
Time Series ◽  
Energy Distribution ◽  
Universal Time ◽  
Sea State ◽  
Freak Wave ◽  
High Wave ◽  
Time Frequency ◽  
Wigner Spectrum ◽  
Central North Sea ◽  
Field Wave

Abstract. The nonlinear and nonstationary properties of a special field wave record are analysed with the Wigner spectrum with the Choi–Williams kernel. The wave time series, which was recorded at the Ekofisk complex in the central North Sea at 00:40 UTC (universal time coordinated) on 9 November 2007, contains an abnormally high wave known as the "Andrea" wave. The ability of the Wigner spectrum to reveal the wave energy distribution in frequency and time is demonstrated. The results are compared with previous investigations for different sea states and also the state with Draupner's abnormal "New Year" wave.

scholarly journals Time-frequency analysis of the sea state with the "Andrea" freak wave

2014 ◽  
Vol 2 (2) ◽  
pp. 1481-1503
Author(s):  
Z. Cherneva ◽  
C. Guedes Soares
Keyword(s):  
Time Series ◽  
Energy Distribution ◽  
North Sea ◽  
Sea State ◽  
Freak Wave ◽  
High Wave ◽  
Time Frequency ◽  
Wigner Spectrum ◽  
Central North Sea ◽  
Field Wave

Abstract. The non-linear and non-stationary properties of a special field wave record are analyzed with the Wigner spectrum with the Choi–Williams kernel. The wave time series, which was recorded at the Ekofisk complex in the Central North Sea at 00:40 UTC on 9 November 2007, contains an abnormally high wave known as "Andrea" wave. The ability of the Wigner spectrum to reveal the wave energy distribution in frequency and time is demonstrated. The results are compared with previous investigations for different sea states and also the state with the abnormal Draupner's New Year wave.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals Survey of stochastic models for wind and sea state time series

2007 ◽  
Vol 22 (2) ◽  
pp. 113-126 ◽  
Author(s):  
V. Monbet ◽  
P. Ailliot ◽  
M. Prevosto
Keyword(s):  
Time Series ◽  
Stochastic Models ◽  
Sea State ◽  
State Time

Simulation Study of Damage Identification Method Based on Lamb Wave Scattering in Aluminium Plate

2012 ◽  
Vol 433-440 ◽  
pp. 2611-2618
Author(s):  
Zhen Hua Tian ◽  
Hong Yuan Li ◽  
Hong Xu
Keyword(s):  
Energy Distribution ◽  
Lamb Wave ◽  
Damage Identification ◽  
Optimization Method ◽  
Propagation Path ◽  
Transient Dynamic Analysis ◽  
Time Frequency ◽  
Damage Location ◽  
Identification Method ◽  
Short Time

The propagation of scattering Lamb wave in plate was simulated using transient dynamic analysis in ANSYS. In order to extract the characteristic information of received signal for damage identification, the short time Fourier transform based on time-frequency analysis was utilized, and then the energy distribution and envelop of received signal were obtained. Based on the displacement contour of simulation and energy distribution, the propagation of scattering wave in plate with a through hole was examined. Also, a mathematic relationship between damage location and scattering signal was developed, with the help of wave propagation path through actuator, damage and sensor. A nonlinear optimization method was applied on the mathematic relationship to obtain the damage location. The damage identification method using scattering Lamb wave was therefore established.

Wavelet Entropy Energy Measure (WEEM): A multiscale measure to grade a geophysical system's predictability

2021 ◽  
Author(s):  
Ravi Kumar Guntu ◽  
Ankit Agarwal
Keyword(s):  
Time Series ◽  
Energy Distribution ◽  
Energy Concentration ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Wavelet Entropy ◽  
Wavelet Energy ◽  
Energy Measure ◽  
Geophysical Time Series ◽  
Geophysical System

<p>Model-free gradation of predictability of a geophysical system is essential to quantify how much inherent information is contained within the system and evaluate different forecasting methods' performance to get the best possible prediction. We conjecture that Multiscale Information enclosed in a given geophysical time series is the only input source for any forecast model. In the literature, established entropic measures dealing with grading the predictability of a time series at multiple time scales are limited. Therefore, we need an additional measure to quantify the information at multiple time scales, thereby grading the predictability level. This study introduces a novel measure, Wavelet Entropy Energy Measure (WEEM), based on Wavelet entropy to investigate a time series's energy distribution. From the WEEM analysis, predictability can be graded low to high. The difference between the entropy of a wavelet energy distribution of a time series and entropy of wavelet energy of white noise is the basis for gradation. The metric quantifies the proportion of the deterministic component of a time series in terms of energy concentration, and its range varies from zero to one. One corresponds to high predictable due to its high energy concentration and zero representing a process similar to the white noise process having scattered energy distribution. The proposed metric is normalized, handles non-stationarity, independent of the length of the data. Therefore, it can explain the evolution of predictability for any geophysical time series (ex: precipitation, streamflow, paleoclimate series) from past to the present. WEEM metric's performance can guide the forecasting models in getting the best possible prediction of a geophysical system by comparing different methods. </p>

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.

scholarly journals A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang
Keyword(s):  
Time Series ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Forecast Model ◽  
Practical Significance ◽  
Frequency Signal ◽  
Time Frequency ◽  
Simple Basis ◽  
Best Fitting ◽  
Track Irregularity

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

Time-Frequency Deep Metric Learning for Multivariate Time Series Classification

2021 ◽  
Author(s):  
Zhi Chen ◽  
Yongguo Liu ◽  
Jiajing Zhu ◽  
Yun Zhang ◽  
Rongjiang Jin ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Metric Learning ◽  
Time Series Classification ◽  
Time Frequency ◽  
Deep Metric Learning
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