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2D temperature field reconstruction using optimized Gaussian radial basis function networks
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DOI：10.1016/j.measurement.2024.115170.png)
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Monitoring temperature fields by sensors and mapping the reconstructed temperature field holds great importance in numerous contexts. Concurrently, as sensing technologies continue to advance, there is a parallel development of temperature reconstruction algorithms. In this study, we present an enhanced Quadratic Optimization Gaussian Radial Basis Function approximation (QO-GRBF). We demonstrate the effectiveness of this algorithm by reconstructing a 10 cm x 10 cm temperature field using distributed optical fiber sensing system. The absolute error across four different temperature stages from 35 degrees C to 65 degrees C ranged from 0.21 degrees C to 0.45 degrees C, indicating a strong reconstruction ability. Furthermore, we compared the performance of QO-GRBF with GRBF in different point densities. The results reveal an over 30 % improvement in scenarios with denser data points, highlighting the efficacy of proposed algorithm. The data size chosen was quantified as well. There was a 10.22 % improvement observed when transitioning from selecting 4 points to selecting 6 points.
Keyword:
Temperature reconstruction algorithm
Gaussian Radial Basis Function approximation
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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