基于物理信息神经网络的短偶极子传感器响应线性化

IF 1.8 3区 生物学 Q3 BIOLOGY
Alessandro Fasse, Romain Meyer, Esra Neufeld, Maxim Haas, Nicolas Chavannes, Niels Kuster
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引用次数: 0

摘要

负载高阻线的短偶极二极管传感器通常用于直接测量高频电磁场振幅的时均平方。它们的精度,简单,宽带,高动态范围能力和最小散射使它们成为近场源应用的理想选择,特别是用于证明符合暴露限制。然而,使用这些传感器来覆盖多个数量级的场振幅需要对传感器响应进行特定信号的线性化。传统上,通过测量对每个信号或调制进行线性化,最近,通过基于校准传感器模型的模拟进行线性化。随着第五代移动通信(5G)的推出,这些方法变得昂贵得令人望而却步,5G增加了数千种不同而复杂的调制方案。为了应对这些挑战,我们首先开发了一种创新的方法来加速传感器模型仿真,提高精度,这使我们能够随后建立一个包含大量探头参数和信号特征配置的数据集。随后,利用易于获取的信号特征训练物理信息神经网络(PINN),以在相关动态范围内获得具有可接受不确定性的动态线性化参数。与传统的人工智能(AI)模型主要依赖于预先计算数据的模式识别相比,我们的方法确保模型捕获所研究的物理现象固有的内在关系和系统动力学。我们基于人工智能的方法在峰值比吸收率(SAR)值高达>时,误差低于0.4 dB;200 W kg−1 $\gt \,200\,{\text{W kg}}^{-1}$。此外,AI通过因子>;加速线性化参数的确定;$\gt $ 34,000 × $\times $并减少存储需求>;$\gt $ 350,000次,允许在现场计算线性化参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Short-Dipole Sensor Response Linearization Through Physics-Informed Neural Networks

Short-dipole diode sensors loaded with highly resistive lines are commonly used to measure the time-averaged square of the high-frequency electromagnetic field amplitude directly. Their precision, simplicity, broadband, high dynamic range capability, and minimal scattering make them ideal for application in the near-field of sources, particularly for demonstrating compliance with exposure limits. However, the usage of these sensors to cover multiple orders of magnitude of field amplitude requires signal-specific linearization of the sensor response. Traditionally, linearization had been performed for each signal or modulation by measurement and, more recently, by simulations based on a calibrated sensor model. These approaches have become prohibitively expensive with the launch of the fifth generation of mobile communication (5G), which added thousands of diverse and complex modulation schemes. In response to these challenges, we first developed an innovative approach to accelerate sensor model simulations with an enhancement of accuracy, which allows us to subsequently establish a data set comprising a large number of probe parameters and signal characteristic configurations. Subsequently, a physics-informed neural network (PINN) was trained with readily accessible signal characteristics to obtain on-the-fly linearization parameters with acceptable uncertainties across the relevant dynamic range. In contrast to traditional artificial intelligence (AI) models that predominantly rely on pattern recognition from precomputed data, our approach ensures that the model captures the intrinsic relationships and system dynamics inherent to the physical phenomena under study. Our AI-based approach achieves an error below 0.4 dB at peak specific absorption rate (SAR) values of up to > 200 W kg 1 $\gt \,200\,{\text{W kg}}^{-1}$ . In addition, AI accelerates the determination of linearization parameters by a factor > $\gt $  34,000 × $\times $ and reduces storage requirements > $\gt $  350,000 times, allowing linearization parameters to be computed on site.

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来源期刊
Bioelectromagnetics
Bioelectromagnetics 生物-生物物理
CiteScore
4.60
自引率
0.00%
发文量
44
审稿时长
6-12 weeks
期刊介绍: Bioelectromagnetics is published by Wiley-Liss, Inc., for the Bioelectromagnetics Society and is the official journal of the Bioelectromagnetics Society and the European Bioelectromagnetics Association. It is a peer-reviewed, internationally circulated scientific journal that specializes in reporting original data on biological effects and applications of electromagnetic fields that range in frequency from zero hertz (static fields) to the terahertz undulations and visible light. Both experimental and clinical data are of interest to the journal''s readers as are theoretical papers or reviews that offer novel insights into or criticism of contemporary concepts and theories of field-body interactions. The Bioelectromagnetics Society, which sponsors the journal, also welcomes experimental or clinical papers on the domains of sonic and ultrasonic radiation.
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