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The rate of heat transfer by emitted radiation is described by the Stefan-Boltzmann law of radiation:
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相关实验视频

Updated: Jun 25, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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通过使用机器学习进行光热放射测量数据分析.

Perry Xiao1, Daqing Chen1

  • 1School of Engineering, London South Bank University, London SE1 0AA, UK.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
概括
此摘要是机器生成的。

机器学习增强生物医学和工业用途的光热技术. 这项研究将机器学习算法应用于光热暂时发射放射测量 (OTTER) 进行精确的皮肤分析.

关键词:
这是分类分类的分类.深度学习是一种深度学习.机器学习是机器学习.光热技术是光热技术.这是一个回归回归的回归.皮肤水分化 肌肤水分化

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科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能的人工智能
  • 红外远程传感 红外远程传感

背景情况:

  • 光热技术利用红外遥感用于生物医学和工业应用.
  • 机器学习 (ML) 提供数据驱动的分析,没有明确的编程.
  • 光热瞬发射放射测量 (OTTER) 是一种用于皮肤分析的光热技术.

研究的目的:

  • 审查用于光热技术的机器学习的最新进展.
  • 在OTTER中提出用于数据分析的新型机器学习应用程序.
  • 评估各种用于OTTER数据解释的ML算法.

主要方法:

  • 关于光热应用中的机器学习的文献综述.
  • 实现和比较回归算法:随机森林,梯度提升,支向量机 (SVM),部分最小平方 (PLS).
  • 深度学习神经网络回归的应用用于OTTER数据分析.

主要成果:

  • 机器学习算法在分析OTTER数据方面表现出有效性.
  • 评估的算法显示了准确的皮肤水分,色素和局部物质透度测量的潜力.
  • 深度学习模型为复杂的光热数据解释提供了高级功能.

结论:

  • 机器学习显著改善了像OTTER这样的光热技术中的数据分析.
  • 该研究验证了各种ML回归模型对生物医学皮肤分析的有用性.
  • 未来的研究可以利用这些ML方法进行增强的非破坏性测试和生物医学应用.