用深度神经网络分析70个传感器的电磁波暴露数据，提升高分辨率估计精度。
Comparative analysis of Realistic EMF Exposure Estimation from Low Density Sensor Network by Finite & Infinite Neural Networks
- 对比有限与无限宽度卷积网络在电磁波估计中的表现
- 基于70个真实传感器数据，实现更低的均方根误差
- 适合关注环境健康风险评估的研究者
理解射频电磁场（RF-EMF）在空间和时间上的分布模式对风险评估至关重要，有助于探索其对人体健康、野生动物及植物的影响。现有研究已采用多种机器学习方法进行电磁波暴露估算，但针对真实数据集的比较分析仍不足。本文提出基于有限与无限宽度卷积网络的方法，利用法国里尔市70个真实传感器的数据，对电磁波暴露水平进行估计与评估。通过对比执行时间和估算精度，验证了方法的有效性。为提高高分辨率网格下的估计精度，采用预条件梯度下降法优化核函数。以均方根误差（RMSE）作为评价指标，量化不同深度学习模型的性能表现。
原文摘要 · Abstract (English)
Understanding the spatial and temporal patterns of environmental exposure to radio-frequency electromagnetic fields (RF-EMF) is essential for conducting risk assessments. These assessments aim to explore potential connections between RF-EMF exposure and its effects on human health, as well as on wildlife and plant life. Existing research has used different machine learning tools for EMF exposure estimation; however, a comparative analysis of these techniques is required to better understand their performance for real-world datasets. In this work, we present both finite and infinite-width convolutional network-based methods to estimate and assess EMF exposure levels from 70 real-world sensors in Lille, France. A comparative analysis has been conducted to analyze the performance of the methods' execution time and estimation accuracy. To improve estimation accuracy for higher-resolution grids, we utilized a preconditioned gradient descent method for kernel estimation. Root Mean Square Error (RMSE) is used as the evaluation criterion for comparing the performance of these deep learning models.
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