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相关概念视频

Preparation of Samples for Electron Microscopy01:20

Preparation of Samples for Electron Microscopy

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To be visualized by an electron microscope, either transmission or scanning, biological samples need to be fixed (stabilized) so the electron beam does not destroy them and dried thoroughly (desiccated/dehydrated) so the vacuum does not affect them. Fixation needs to be done as quickly as possible because the sample properties will start changing as soon as it is removed from its natural environment. For example, in a tissue sample, the oxygen levels begin decreasing, causing an altered...
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Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys
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一种高质量的样本生成方法,用于改善钢表面缺陷检查和检查.

Yu He1, Shuai Li1, Xin Wen1

  • 1Department of Software Engineering, Shenyang University of Technology, Shenyang 110870, China.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
概括

本研究引入了一种新的生成对抗网络 (GAN) 方法,用于创建高质量的钢板缺陷样本. 这种方法可以增强深度学习模型,以便更准确地检查表面缺陷.

关键词:
缺陷检查检查检查缺陷检查检查检查检查检查检查检查检查检查缺陷样本生成的产生缺陷样本.生成式对抗网络 (GAN) 是一种产生式对抗网络.生产和淘汰的过程.

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Micromechanical Tension Testing of Additively Manufactured 17-4 PH Stainless Steel Specimens
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相关实验视频

Last Updated: Jun 27, 2025

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 钢板的表面质量检查至关重要.
  • 深度神经网络需要足够的缺陷样本来进行准确的检查.
  • 通过摄像头收集足够的缺陷样本是一项挑战.

研究的目的:

  • 提出一种基于生成对抗网络 (GAN) 的方法,用于生成高质量的钢板缺陷样本.
  • 提高用于缺陷检查的深度学习模型的性能.
  • 为解决缺陷样本数据不足的问题.

主要方法:

  • 一个两阶段的样本生成过程:生产和消除.
  • 在钢板表面模拟缺陷形成.
  • 尽量减少两个阶段生成的样本之间的差异.

主要成果:

  • 拟议的GAN方法产生了高质量的缺陷样本.
  • 生成的样本提高了检查模型的培训.
  • 改进的检查模型显示性能有所提高.

结论:

  • 生产和消除GAN方法有效地产生现实的缺陷样本.
  • 这种方法显著提高了钢板缺陷检查模型的准确性.
  • 该技术为工业检查中的数据增强提供了可行的解决方案.