Electrical impedance tomography (EIT), as a merging technology, has been widely used in the industrial and clinical fields. However, the causes of the uncertainty of measuring reference voltages, which are affected by medium temperature, measurement errors, and reference conductivity distribution that varies with the patient's posture, have brought obstacles to applying EIT in industry and medicine. In the paper, two methods: the Multiple Measurements (MM) method and the deep learning method, convolutional neural network (CNN) are proposed to establish the nonlinear mapping between measurement voltages and reference voltages. The novelty of the article is firstly adopting the deep learning method to estimate the reference voltages from measurement voltages for the timedifference EIT. Both static experiments-water tank experiments and dynamic experiments-two-phase flow experiments were carried out. Compared with the two existing estimation methods: best homogeneous (BH) approximation and measurement-scale feature (MSF), and the proposed MM method, the deep learning method shows excellent results in quantitative analysis of the relative errors of reference voltages and ground truth. In addition, the CNN method also displays a better performance in qualitative analysis in terms of the reconstructed tomographic images. The study shows the potential to realtime estimate the reference voltages for time-difference EIT in the industrial and medical fields.

Oil-water two-phase flow as a typical twophase flow type widely exists in various industrial processes and the accurate measurement of oil volume fraction plays a significant role in transporting and separating oil-water mixture in the processes. Electrical Impedance Tomography (EIT) as a merging technology with the advantages of non-invasive, low cost and real-time measurement is widely applied in the industrial field to measure the volume fraction for different types of twophase flows. However, the measurement process of taking homogeneous reference voltages is time-consuming and costly. To cope with the problem, in the paper, by establishing an end-to-end mapping between measurement voltages and volume fraction, we propose an Attention UNet-Fully Connected (AU-FC) architecture. Relying on the attention mechanism, the reconstructed voltages having a strong correlation or a weak correlation with volume fraction is highlighted or suppressed respectively. Oil-water two-phase flow experiment was conducted in the NEL facility to collect EIT voltage data. Compared with six stateof-the-art and existing machine learning methods, the proposed method performs better in predicting volume fraction. The results indicate that the proposed AU-FC architecture can accurately and real-time predict the volume fraction of oil-water two-phase flow, which improves the application potential of EIT combined with deep learning method in the industrial field.

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