Power-Efficient Trainable Neural Networks towards Accurate Measurement of Irregular Cavity Volume

Abstract

Irregular cavity volume measurement is a critical step in industrial production. This technology is used in a wide variety of applications. Traditional studies, such as waterflooding-based methods, have suffered from the following shortcomings, i.e., significant measurement error, low efficiency, complicated operation, and corrosion of devices. Recently, neural networks based on the air compression principle have been proposed to achieve irregular cavity volume measurement. However, the balance between data quality, network computation speed, convergence, and measurement accuracy is still underexplored. In this paper, we propose novel neural networks to achieve accurate measurement of irregular cavity volume. First, we propose a measurement method based on the air compression principle to analyze seven key parameters comprehensively. Moreover, we integrate the Hilbert–Schmidt independence criterion (HSIC) into fully connected neural networks (FCNNs) to build a trainable framework. This enables the proposed method to achieve power-efficient training. We evaluate the proposed neural network in the real world and compare it with typical procedures. The results show that the proposed method achieves the top performance for measurement accuracy and efficiency.