基于SA-ELM的声学层析成像温度分布重建算法

SA-ELM based method for reconstructing temperature distribution in acoustic tomography measurement

在化工领域中,获取准确的温度分布信息具有极其重要的意义。声学层析成像(AT)具有非侵入传感、廉价等优点,因而被认为是一种具有广阔发展前景的可视化温度分布测量方法。将AT用于温度分布测量,提出一种SA-ELM算法改进重建质量。首先,利用稳健估计建立了基于L1范数的目标泛函,采用模拟退火算法(SA)对目标泛函进行求解,得到粗网格下的温度分布;最后,采用极限学习机(ELM)来预测经细化网格后的温度分布。数值仿真和实验研究途径评估该方法的可行性与有效性,结果表明SA-ELM算法能够有效提高温度分布重建质量和鲁棒性,从而为AT反问题的求解提供了一种新的有效方法。

Obtaining accurate information of temperature distribution plays an important role in chemical industry. As a result of advantages such as low cost and non-intrusive sensing, acoustic tomography （AT） is considered to be a promising visualization measurement method for temperature distribution. An SA-ELM algorithm was proposed to improve reconstruction quality of temperature distribution after AT measurement. First, robust estimation was used to establish the L1 norm objective functions. Then, the objective functions were solved to obtain temperature distribution on coarse discrete grids by simulated annealing algorithm （SA）. Finally, temperature distribution on fine grids was predicted by extreme learning machine （ELM） method. Numerical simulations and experimental study showed that the SA-ELM method could improve quality and robustness of temperature distribution reconstruction. Hence, an effective new method is developed for solving reverse challenge in AT measurement.