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Analytical optimizations and numerical simulations are applied to enhance power transferring through a metal insulator metal (MIM) plasmonic junction.
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Generally, these optimization problems can be regarded as a class of complex combinatorial problems that cannot be mathematically modeled, and be solve with analytical optimization algorithms or they may result in a local optimal solution.
Our previous work proposed a combined use of analytical optimization techniques and evolutionary algorithms to efficiently identify an optimal set of design alternatives with respect to performance and costs.
When α≠2, however, the analytical optimization of δ is not feasible.
This game was solved using three different approaches (analytical, optimization, and hybrid).
Recently, chemometrics have been utilized in analytical optimization methods because they provide more advantages such as less laboratory work.
After that, MREKLM adopts an analytical optimization approach to directly deal with multi-class problems.
This fact makes the problem non-derivable, preventing the use of classical analytical optimization techniques.
Correlation filters for target detection are usually designed by analytical optimization of performance criteria.
This modeling approach, used for analytical optimization of monitoring network gives the results that can be interactively visualized since it gives 3-D model visualization.
The paper presents a generalization of multi-dimensional linear regression to facilitate multi-sensor fault detection and signal reconstruction through the use of analytical optimization.
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