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通过使用增强模拟波场数据的全波场细分来优化分层成像.

Yitian Yan1, Kang Yang2, Jing Sun1

  • 1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

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概括
此摘要是机器生成的。

检测碳纤维增强聚合物的分层是至关重要的. 一个新的数据预处理策略增强了深度学习模型,用于准确的分层成像,即使实验数据有限.

关键词:
数据增强数据增强深度学习是一种深度学习.分层是分层的方法.非破坏性评价 - - 非破坏性评价扫描激光多普勒振动计扫描激光多普勒振动计信号处理 信号处理

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科学领域:

  • 材料科学 材料科学 材料科学
  • 结构健康监测 结构健康监测
  • 人工智能的人工智能

背景情况:

  • 碳纤维增强聚合物的灾难性结构故障可能是隐藏的分层造成的.
  • 准确的分层检测对于防止此类故障至关重要.
  • 对于全波场细分的深度学习有希望,但面临的挑战是数据采集成本和复杂的波场叠加.

研究的目的:

  • 提出和评估数据预处理策略,以改进基于深度学习的碳纤维增强聚合物的分层成像.
  • 增强用于分层检测的深度学习模型的性能和概括性.
  • 为了使有效的分层成像使用模拟数据用于现实世界的应用.

主要方法:

  • 开发了一个数据预处理策略,将波数过和混合噪声翻转增强相结合.
  • 波数过隔离了在频域中通过分层引入的引导波模式.
  • 噪音和翻转增强被用来改善不同测量条件下的模型概括性.

主要成果:

  • 拟议的预处理策略显著提高了对分层成像的深度学习模型性能.
  • 在模拟数据上训练的模型证明了对实验测量的有效应用.
  • 实现了0.8634的最高交叉与联合得分,产生了没有工件的分层图像.

结论:

  • 数据预处理策略有效地引导深度学习模型专注于分层相关的特征.
  • 这种方法改善了模型的概括性,使得它能够成功地应用于实验数据.
  • 这种方法为结构健康监测中准确的分层成像提供了具有成本效益的解决方案.