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At present, the digital meter recognition is widely used in the field of power transmission and transformation, but rarely used in the petrochemical industry. This paper combines traditional image processing techniques with deep learning methods and proposes a meter recognition method based on an improved neural network model. By establishing a neural network model architecture, setting up the three convolutional layers and adding a batch normalization processing operation, the digital meters of the oil extraction platform are recognized, and the recognition rate is about 99.17%, which achieve the expectations. Through comparative research, the model constructed in this paper has a higher recognition accuracy and reflects a better recognition effect.

Transfer function of intra-body communication (IBC) helps the understanding of signal transmission mechanism and hints for efficient communication system design. In this paper, transfer function with phase information based on quasi-static electromagnetic (EM) field modeling for a galvanic-type IBC limb is briefly reviewed. Then the compensation factor, which is calculated by the minimum mean square error (MMSE) method, is introduced to correct the inevitable deviation in actual shapes of human limb and electrical properties of tissues. To verify the effectiveness of correction factor, the square wave response is calculated and experimentally measured. Eventually, our results demonstrate good agreement between theoretical calculation and experimental measurement.

Intra-body communication (IBC) is a short-range body based communication using the human body as the transmission medium in biomedical application. Quasi-static electric field modeling of the galvanic coupling IBC human limb channel is analyzed. Governing function can be reduced to Laplace's equation, bone and joint effects are considered as the boundary conditions. Analytical solutions of voltage distribution in the human limb from the model are obtained. Effects of human limb's properties in the model are studied. Finally, in vivo validation and calculation results are presented. Especially, the gain is greatly affected by the ratio of the thickness of muscle and bone both in calculation result and in vivo experiment.