在真实应用中用多通道光谱传感器重建光谱辐射,使用卷积神经网络
Optics express
|September 15, 2023
概括
这项研究引入了一种使用10通道光谱传感器和卷积神经网络准确估计照明条件的新方法. 这项技术有助于确保建筑空间和人类福祉的最佳照明.
科学领域:
- 照明技术 照明技术
- 建筑科学 建筑科学
- 人类的福祉 人类的福祉
背景情况:
- 照明显著影响建筑的感知和人类的福祉.
- 未来的照明需要动态监控和对最佳条件进行补偿.
- 以前的研究表明,使用低成本传感器来估计照明参数是有前途的.
研究的目的:
- 在现实世界照明场景中开发一种用于估计绝对光谱辐射的新方法.
- 使用10通道光谱传感器和卷积神经网络 (CNN) 进行准确的照明分析.
- 评估在各种室内照明条件下拟议方法的性能.
主要方法:
- 采用了10通道的光谱传感器来捕获照明数据.
- 一种卷积神经网络 (CNN) 方法被开发用于光谱重建.
- 在三个室内地点使用可调节的白色地板灯系统进行实验,模拟不同的照明条件 (LED,光灯,,日光).
主要成果:
- 该方法精确估计了光谱功率分布,平均平方根平均误差为1.6%.
- 颜色精度很高,平均 Δu'v' 不到 0.001.001.
- 照明精度平均为2.7%,证明了该方法的可靠性.
结论:
- 提出的基于CNN的方法有效地估计了来自10通道传感器的光谱辐射.
- 该系统在重建照明条件方面取得了很高的准确性,这对于建筑和福祉应用至关重要.
- 传感器元米制被确定为在特定光混合物中对光谱重建准确性的潜在限制.
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