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Updated: Jan 7, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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对于表面计量学的多任务深度学习.

Dawid Kucharski1, Adam Gąska2, Tomasz Kowaluk3

  • 1Division of Metrology and Measurement Systems, Institute of Mechanical Technology, Faculty of Mechanical Engineering, Poznan University of Technology, 60-965 Poznan, Poland.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
概括
此摘要是机器生成的。

深度学习框架准确地预测了表面纹理参数及其从触觉和光学测量中的不确定性. 该工具有助于在计量学中选择和接受仪器.

关键词:
人工智能的人工智能是人工智能.符合规范的预测预测深度学习是一种深度学习.表面计量学 表面计量学不确定性量化不确定性量化

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科学领域:

  • 计量学 计量学 计量学
  • 机器学习 机器学习
  • 表面科学是一门学科.

背景情况:

  • 表面质地参数对于产品性能和质量控制至关重要.
  • 准确预测表面参数及其不确定性对于可靠的计量工作流程至关重要.
  • 现有的方法往往难以整合来自不同测量系统的数据,并提供可靠的不确定性估计.

研究的目的:

  • 开发一个可重现的深度学习框架,用于预测表面纹理参数及其标准不确定性.
  • 共同分类测量系统类型和回归关键表面参数 (Ra,Rz,RONt) 和它们的不确定性.
  • 为明智的仪器选择和接受决策提供校准预测.

主要方法:

  • 使用了包括触觉和光学测量系统在内的多仪器数据集.
  • 开发了一个深度学习框架,用于表面参数及其不确定性的独立回归头.
  • 在不确定性建模中采用定量和异种回归,并进行后期合规校准.
  • 实现单目标回归器和测量系统类型的分类器.

主要成果:

  • 表面参数 (Ra,Rz,RONt) 和它们的标准不确定性 (Ra,Rz) 的高预测准确度.
  • 在分类测量系统类型方面获得了92.85%的准确性.
  • 证明单个目标模型的表现优于天真的多输出模型,避免负转移.
  • 在学习 RONt.的标准不确定性时发现了挑战.

结论:

  • 开发的深度学习框架为表面计量学提供了一种可重复和准确的方法.
  • 校准预测提高了表面参数和不确定性估计的可靠性.
  • 该框架支持在计量工作流程中选择和接受仪器的知情决策.