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In this paper, the performances of microchannel evaporators with different manifold structures are experimentally investigated.
In this investigation, planar and three-dimensional resonant orbits are analyzed and cataloged in the Earth Moon system and the associated invariant manifold structures are computed and visualized with the aid of higher-dimensional Poincaré maps.
However, in many cases the underlying shape space may be better described by a nonlinear low dimensional (LD) manifold that can be learned by nonlinear dimensionality reduction (DR) techniques, where the learned manifold structures are often either target-dependent or view-dependent [29].
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Two 5-cell solid oxide fuel cell (SOFC) stacks with an external manifold structure are constructed and their degradation and thermal cycle performance are investigated at 750 °C.
Global linear structure is not applicable for high-dimensional dataset, but manifold structure is.
Besides, the underlying manifold structure is guaranteed to be clear by using the l1-norm regularization.
Experiments illustrate an optimized manifold structure is feasible for practical fabrication.
The Grassmann distance reflecting manifold structure is a suitable metric to measure distance between subspaces in the manifold.
The set of all orbits M/G together with a differentiable manifold structure is the orbit space, if the manifold structure is induced from a symmetry cell and is independent of the choice of the symmetry cell.
Even though the objective functions in these methods are different, the goal, preserving the manifold structure, is the same.
This manifold structure is problematic for the optimization procedures of the MLE and the assignment of reliable prior distributions in Bayesian analysis.
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