Neural network approximations for nonlinear interactions in wind wave spectra: direct mapping for wind seas in deep water

2005 ◽  
Vol 8 (3) ◽  
pp. 253-278 ◽  
Author(s):  
Hendrik L. Tolman ◽  
Vladimir M. Krasnopolsky ◽  
Dmitry V. Chalikov
Keyword(s):  
Neural Network ◽  
Deep Water ◽  
Wind Wave ◽  
Wave Spectra ◽  
Nonlinear Interactions ◽  
Direct Mapping

scholarly journals On Natural Modulational Bandwidth of Deep-Water Surface Waves

Fluids ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 67
Author(s):  
Alexander Babanin ◽  
Miguel Onorato ◽  
Luigi Cavaleri
Keyword(s):  
Deep Water ◽  
Water Surface ◽  
Laboratory Experiments ◽  
Wind Wave ◽  
Wave Spectra ◽  
Field Observations ◽  
Wave Steepness ◽  
Side Band ◽  
Wave Trains

We suggest that there exists a natural bandwidth of wave trains, including trains of wind-generated waves with a continuous spectrum, determined by their steepness. Based on laboratory experiments with monochromatic waves, we show that, if no side-band perturbations are imposed, the ratio between the wave steepness and bandwidth is restricted to certain limits. These limits are consistent with field observations of narrow-banded wind-wave spectra if a characteristic width of the spectral peak and average steepness are used. The role of the wind in such modulation is also discussed.

scholarly journals Brain Musics: History, Precedents, and Commentary on Whalley, Mavros and Furniss

2015 ◽  
Vol 9 (3-4) ◽  
pp. 277 ◽  
Author(s):  
Miguel Ortiz ◽  
Mick Grierson ◽  
Atau Tanaka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Musical Performance ◽  
Eeg Signals ◽  
Live Electronics ◽  
Broad Area ◽  
Direct Mapping ◽  
Challenges And Opportunities ◽  
History Of ◽  
Small Ensemble

<p>Whalley, Mavros and Furniss (this issue) explore questions of agency, control and interaction, as well as the embodied nature of musical performance in relation to the use of human-computer interaction through the work <em>Clasp Together (beta) </em>for small ensemble and live electronics. The underlying concept of the piece focuses on direct mapping of a human neural network (embodied by a performer within the ensemble) to an artificial neural network running on a computer. With our commentary, we contextualize the work by offering a brief history of music that uses brainwaves. We review the use of EEG signals for musical performance and point at precedents in EEG-based musical practice. We hope to more clearly situate <em>Clasp Together (beta)</em> in the broad area of Brain Computer Musical Interfaces and discuss the challenges and opportunities that these technologies offer for composers.</p>

The fractional power law of wind wave growth in deep water for short fetch

2002 ◽  
Vol 1 (2) ◽  
pp. 105-108 ◽  
Author(s):  
Guan Changlong ◽  
Sun Qun ◽  
Philippe Fraunie
Keyword(s):  
Power Law ◽  
Deep Water ◽  
Wind Wave ◽  
Fractional Power ◽  
Wave Growth

Breaking waves and the equilibrium range of wind-wave spectra

1997 ◽  
Vol 342 ◽  
pp. 377-401 ◽  
Author(s):  
S. E. BELCHER ◽  
J. C. VASSILICOS
Keyword(s):  
High Frequency ◽  
Wind Wave ◽  
Breaking Waves ◽  
Breaking Wave ◽  
Wave Spectra ◽  
Scale Invariant ◽  
Self Similar ◽  
Dynamical Properties ◽  
Dynamical Balance ◽  
Equilibrium Range

When scaled properly, the high-wavenumber and high-frequency parts of wind-wave spectra collapse onto universal curves. This collapse has been attributed to a dynamical balance and so these parts of the spectra have been called the equilibrium range. We develop a model for this equilibrium range based on kinematical and dynamical properties of breaking waves. Data suggest that breaking waves have high curvature at their crests, and they are modelled here as waves with discontinuous slope at their crests. Spectra are then dominated by these singularities in slope. The equilibrium range is assumed to be scale invariant, meaning that there is no privileged lengthscale. This assumption implies that: (i) the sharp-crested breaking waves have self-similar shapes, so that large breaking waves are magnified copies of the smaller breaking waves; and (ii) statistical properties of breaking waves, such as the average total length of breaking-wave fronts of a given scale, vary with the scale of the breaking waves as a power law, parameterized here with exponent D.

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