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The novelty of numerical target marching is that it alleviates this difficulty using the Green's function G0(r d,0) of light reflection from a semi-infinite medium, i.e., the clean image of the host medium, obtained directly from experimentally measured array images.
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In this work we developed a massively parallel algorithm on the graphical processing unit (GPU) to alleviate this difficulty in the case of dynamic brittle fracture.
In order to alleviate this difficulty and its consequences, we propose a framework for organizing the space of decision problems based on problem characteristics.
To alleviate this difficulty, a new RBDO methodology developed integrating the HMV method with a proposed response surface method, which is specifically developed for reliability analysis and optimization.
Possibly the most promising approach to alleviate this difficulty is surrogate-based optimization (SBO), where direct optimization of expensive models is replaced by an iterative enhancement and re-optimization of fast surrogate models.
An efficient approach to alleviate this difficulty is to conduct a re-sampling procedure in the SIS algorithm.
To alleviate this difficulty, their solution relies on the Semantic Web (SW) in order to support dynamic provision of redundant services by describing them in terms of semantics.
An alternative approach is to apply the large system analysis, that is, the number of active users tends to infinity, to alleviate this difficulty by defining an interference function.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com