期刊
IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
卷 69, 期 7, 页码 2352-2370出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TUFFC.2022.3177469
关键词
Temperature measurement; Temperature distribution; Acoustics; Image reconstruction; Ultrasonic variables measurement; Tomography; Temperature sensors; Equilibrium optimizer (EO); Gaussian process regression (GPR); temperature distribution measurement; ultrasonic tomography (UT)
资金
- Fundamental Research Funds for the Central Universities [ZYGX2019J081]
- Sichuan Science and Technology Plan [2020YFH0098]
- Guangdong Basic and Applied Basic Research Foundation [2021A1515011692]
This article proposes a novel two-step reconstruction method for precise temperature distribution measurement, which provides high-resolution images and maintains high accuracy. By utilizing equilibrium optimizer and Gaussian process regression techniques, it effectively improves the resolution of temperature distribution images and reduces reconstruction errors.
The precise temperature distribution measurement is crucial in many industrial fields, where ultrasonic tomography (UT) has broad application prospects and significance. In order to improve the resolution of reconstructed temperature distribution images and maintain high accuracy, a novel two-step reconstruction method is proposed in this article. First, the problem of solving the temperature distribution is converted to an optimization problem and then solved by an improved version of the equilibrium optimizer (IEO), in which a new nonlinear time strategy and novel population update rules are deployed. Then, based on the low-resolution and high-precision images reconstructed by IEO, Gaussian process regression (GPR) is adopted to enhance image resolution and keep the reconstruction errors low. After that, the number of divided grids and the parameters of IEO are also further studied to improve the reconstruction quality. The results of numerical simulations and experiments indicate that high-resolution images with low reconstruction errors can be reconstructed effectively by the proposed IEO-GPR method, and it also shows excellent robust performance. For a complex three-peak temperature distribution, a competitive accuracy with 3.10% and 2.37% error at root-mean-square error and average relative error is achieved, respectively. In practical experiment, the root-mean-square error of IEO-GPR is 0.72%, which is at least 0.89% lower than that of conventional algorithms.
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