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It is natural to expend great effort on analyzing the belonging of patterns with large uncertainties.
By integrating fuzzy sets and rough sets, a hybrid unsupervised learning algorithm is designed for analyzing patterns with large uncertainties.
Rough sets theory is a new paradigm to deal with uncertainty and incompleteness, forming an interesting region which consisted of patterns with large uncertainties.
Furthermore, a fuzzy weighted factor is designed to work with membership degrees together for further determining where patterns with large uncertainties belong to.
Various novel patterns with large band gaps for out-of-plane and in-plane waves are obtained and optimized solutions are discussed and compared with those of square-latticed ones.
Figure 4 (A) Schematic of a patterned surface showing the basic design parameters, (B) polymer patterns with small pitch and (C) polymer patterns with large pitch.
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The ability to generate arbitrary radiation patterns with large-scale phased arrays has long been pursued.
Furthermore, the net currents can form highly correlated patterns, with large-scale vorticity emerging in the system, in the regime of small and intermediate alternating field amplitudes.
The change from one climatic regime to another was caused by only modest changes in the pattern of insolation within the Holocene interval as well as the interaction of these patterns with large-scale climate phenomena such as monsoons and El Niño/Southern Oscillation (ENSO).
The two-mode interaction leads to preferred vibration patterns with larger deflection inwards than outwards, and at internal resonance, significant energy transfer occurs between the modes.
In addition to the graphene atomic structure, the flat surface regions exhibit patterns with larger periodicity that can be interpreted as Moiré patterns formed by the atomic lattices of the graphene and the gold.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com