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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Thermal strain is a concept that arises when we consider how temperature changes affect structures. Unlike the conventional assumption that structures remain constant under load, real-world scenarios often involve temperature fluctuations that can significantly impact these structures. Consider a homogeneous rod with a uniform cross-section resting freely on a flat horizontal surface. If the rod's temperature increases, the rod elongates. This elongation is proportional to the temperature...
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Equipments Used to Measure Body Temperature

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Body temperature can be assessed using various devices and measured in Celsius or Fahrenheit.
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Glass-bulb thermometers are hollow glass tubes with a bulb tip containing liquid such as ethanol or mercury. Historically, glass bulb mercury thermometers were the standard device to measure body temperature. Today, mercury thermometers are prohibited in many countries due to the hazardous effects of mercury and the risk of exposure if the glass bulb breaks. In general,...
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多重表示热特性用于脉冲温度学中增强的缺陷分析.

Mohammed Salah1, Naoufel Werghi2, Davor Svetinovic2,3

  • 1Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.

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人工智能驱动的脉冲热像 (PT) 通过融合主组件热像 (PCT) 和热像信号重建 (TSR) 功能来增强非破坏性测试. 这种新的方法改善了工业部件的地下缺陷检测和深度估计.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能
  • 非破坏性测试是指非破坏性测试.

背景情况:

  • 人工智能驱动的脉冲热学 (PT) 对于工业部件的非破坏性测试 (NDT) 至关重要.
  • 目前的方法通常依赖于主组件热像 (PCT) 或热像信号重建 (TSR) 独立,限制性能.
  • 这些表示包含了对准确缺陷分析至关重要的互补语义特征.

研究的目的:

  • 引入PT-Fusion,一个新的功能融合网络,用于PT中增强的地下缺陷分析.
  • 为了解决使用PCT和TSR表示单独的局限性.
  • 改善PT检查中的缺陷细分和深度估计.

主要方法:

  • 开发了PT-Fusion,这是一个包含自适应称重融合门 (AWFG) 和Gating增强解码块 (GEDB) 模块的网络.
  • 实现PCT和TSR表示的热特征的自适应融合.
  • 引入了一种新的数据增强技术,使用热图序列的随机采样来克服数据稀缺.

主要成果:

  • 与U-Net,attention U-Net和3D-CNN相比,PT-Fusion表现优异,在缺陷细分和深度估计准确度方面实现了10%的利率.
  • PT-Fusion的性能与TransUNet和Swin-UNet等先进模型相美.
  • 与TransUNet和Swin-UNet相比,PT-Fusion的参数数量明显较少,结果可比.

结论:

  • PT-Fusion有效地融合了PCT和TSR的互补热特性,以改进PT分析.
  • 拟议的网络为地下缺陷检测和深度估计提供了更有效,更准确的解决方案.
  • PT-Fusion代表了人工智能驱动的非破坏性测试应用的重大进步.