基于均值点展开的单变元降维法在EIT不确定性量化研究中的应用

doi:10.19595/j.cnki.1000-6753.tces.200971

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Abstract In electrical impedance tomography (EIT), the uncertainty of medium parameters will affect the calculation of the forward problem and then affect the image reconstruction. Therefore, it is of great significance to study the uncertainty quantification of EIT medium parameters. In this paper, the four-layer concentric circle model and the two-dimensional circle model were used as simulation examples to study the EIT forward problem. The conductivity distribution parameters were taken as non-interactive random input variables that subject to random uniform distribution. The univariate dimension reduction method (UDRM) based on the mean-point expansion was used to calculate the mean value, standard deviation, probability distribution and other relevant statistical information of voltage distribution on boundary electrodes, and the influence of the uncertainty of conductivity on the output boundary voltage distribution was analyzed. The results were compared with the results of Monte Carlo simulation (MCS) method and polynomial chaos expansion (PCE) method. It is shown that UDRM can deal with low-dimensional uncertainty problems accurately and efficiently, and can effectively alleviate the “curse of dimensionality” problem when dealing with high-dimensional uncertainty problems.

Key words： Electrical impedance tomography (EIT) uncertainty quantification univariate dimension reduction method (UDRM) Monte Carlo simulation (MCS) polynomial chaos expansion (PCE)

Received: 04 August 2020

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TM470.4011

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	Zhao Yingge
	Li Ying
	Wang Lingyue
	Cui Yangyang
	Wang Guanxiong

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Zhao Yingge,Li Ying,Wang Lingyue等. The Application of Univariate Dimension Reduction Method Based on Mean Point Expansion in the Research of Electrical Impedance Tomography Uncertainty Quantification[J]. Transactions of China Electrotechnical Society, 2021, 36(18): 3776-3786.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200971 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I18/3776

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