概括
这项研究引入了一种使用多分支卷积模型的新型多谱辐射温度计方法. 该方法通过克服排放性挑战,准确地测量各种材料的温度,显示出高的概括能力.
科学领域:
- 热物理学的热物理.
- 光学工程是指光学工程.
- 机器学习 机器学习
背景情况:
- 多光谱温度测量受到未知的材料辐射率的阻碍.
- 现有的算法在辐射率变化和广泛的材料适用性方面扎.
- 精确的温度传感在各种工业和科学应用中至关重要.
研究的目的:
- 开发一个强大的多光谱辐射温度测量方法,独立于发射率.
- 创建一个适用于各种材料的多功能算法.
- 为了提高非接触式温度测量的准确性和可靠性.
主要方法:
- 实施一个改进的多分支卷积神经网络 (CNN).
- CNN包含温度反转,波长,电压比率,辐射率和参考温度的分支.
- 利用特征提取和相互作用来准确预测温度.
主要成果:
- 模拟实验表明最大绝对误差低于7K.
- 现实世界的火箭实验显示最大误差为9.13K.
- 该模型在不同的数据集和条件中表现出强大的概括能力.
结论:
- 拟议的方法有效地解决了多光谱温度计中未知发射率的挑战.
- 多分支CNN模型证明了对各种材料和辐射率模型的广泛适用性.
- 这项研究为辐射温度计理论和实践的进步提供了新的途径.
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