Ramification algorithm for graphene sample-defect localization

机译：石墨烯样品缺陷定位的分枝算法

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Recent development of a new 2D material graphene necessitates sample characterization (in particular localization and distribution of defects). The presence of defects is unavoidable, however, it is possible to determine and predict defect distribution in graphene samples prior to the actual device making. A ramification algorithm is used for the above purpose.

机译：新的2D材料石墨烯的最新开发需要进行样品表征（特别是缺陷的定位和分布）。缺陷的存在是不可避免的，但是，可以在实际制造器件之前确定和预测石墨烯样品中的缺陷分布。分枝算法用于上述目的。

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来源
《Automatic Target Recognition XXV》|2015年|947611.1-947611.12|共12页
会议地点 Baltimore MD(US)
作者
Andre Sokolnikov;
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作者单位

'Visual Solutions Applications', South Bend, IN;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
graphene; adaptive modeling; prediction modeling; tree-based algorithm; 2D systems;

机译：石墨烯自适应建模；预测建模；基于树的算法； 2D系统;

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3. Ramifications of optical pumping on the interpretation of time-resolved photoemission experiments on graphene [J] . Ulstrup Soren, Johannsen Jens Christian, Cilento Federico, Journal of Electron Spectroscopy and Related Phenomena . 2015,第Null期

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4. Ramification algorithm for graphene sample-defect localization [C] . Andre Sokolnikov Automatic Target Recognition . 2015

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7. Sparsifying Distributed Algorithms with Ramifications in Massively Parallel Computation and Centralized Local Computation [O] . Mohsen Ghaffari, Jara Uitto 2019

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Ramification algorithm for graphene sample-defect localization

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