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首页> 外文期刊>International journal for uncertainty quantifications >SURROGATE MODELING OF INDOOR DOWN-LINK HUMAN EXPOSURE BASED ON SPARSE POLYNOMIAL CHAOS EXPANSION
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SURROGATE MODELING OF INDOOR DOWN-LINK HUMAN EXPOSURE BASED ON SPARSE POLYNOMIAL CHAOS EXPANSION

机译:基于稀疏多项式混沌扩展的室内下链路人曝光的替代模型

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摘要

Human exposure induced by wireless communication systems increasingly draws the public attention. Here, an indoor down-link scenario is concerned and the exposure level is statistically analyzed. The electromagnetic field emitted by a WiFi box is measured and electromagnetic dosimetry features are evaluated from the whole-body specific absorption rate as computed with a finite-difference time-domain (a.k.a. FDTD) code. Due to computational cost, a statistical analysis is performed based on a surrogate model, which is constructed by means of so-called sparse polynomial chaos expansion, where the inner cross validation (ICV) is used to select the optimal hyperparameters during the model construction and assess the model performance. However, the model assessment based on ICV tends to be overly optimistic with small data sets. The method of cross-model validation is used and outer cross validation is carried out for the model assessment. The effects of the data preprocessing are investigated as well. On the basis of the surrogate model, the global sensitivity of the exposure to input parameters is analyzed from Sobol' indices.
机译:无线通信系统诱导的人类曝光越来越多地引起公众的关注。在这里,关注室内下链接方案,统计分析曝光率。测量由WiFi箱发出的电磁场,并且通过用有限差分时间域(A.K.A.FDTD)代码计算的全身特定吸收率来评估电磁剂量分子。由于计算成本,基于代理模型执行统计分析,该代理模型是通过所谓的稀疏多项式混沌扩展构成的,其中内交叉验证(ICV)用于在模型构造期间选择最佳超参数。评估模型性能。但是,基于ICV的模型评估往往与小数据集过度乐观。使用跨模型验证的方法,并对模型评估进行外交交叉验证。还研究了数据预处理的影响。在替代模型的基础上,从Sobol'指数分析了对输入参数的全局敏感性。

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