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Initial experimental results showed an increase of the mode I fatigue crack growth rate after the application of a set of mode II overload cycles.
The principle is based on comparison (by a contrast function) of mode excitation factors extracted from real data with a set of mode excitation factors (for simulated source depths) extracted from replica data (modeled with Moctesuma).
The choice of parameters is governed by a set of mode angles which are used in describing geometrically how a ray propagates through the duct and out into free space.
Experimental errors are simulated for a set of mode shapes and Monte Carlo simulation is used to estimate the probability that the orthogonality metric exceeds a threshold due to experimental error alone.
Each growth cone outline can be projected onto each shape mode to give a set of mode scores.
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Moreover, a set of mode-dependent dynamic state feedback controllers are designed.
The constant values are refined by a set of mode-correction factors.
To guarantee the designed filters can correctly estimate the output of switched systems with unstable subsystems, a set of mode-dependent filters of a Luenberger-like observer type is constructed.
In the neural networks, there are a set of modes, which are determined by Markov chain.
This way, the number of modes is lower as clusters are used to group a set of modes into a single mode.
In CFDF, the computation for each actor is decomposed into a set of modes, where each mode is required to have constant production and consumption rates.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com