Investigation of Machine Learning-Based Acoustic 2D Gas Pyrometer

Fu Sung Lin, Man Ching Huang, Chia Hsi Wu, Jia Ling Lin, Chih Ying Li, Hsiao Chi Lin, Soma Pal, Chih Hsien Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Acoustic tomography (AT) is considered a promising visualization technique for gas temperature distribution (TD) in various industrial applications. The gas TD of the region of interest (ROI) can be reconstructed according to the sound velocities of multiple routes between transmitters and receivers. To improve the accuracy of the reconstruction, increasing the number of iterations is inevitable with the cost of being time-consuming. Recently, serval machine learning techniques are used to speed up the imaging reconstruction process, such as X-ray, CT, or MRI. However, to our knowledge, using machine learning on reconstructing gas TD from acoustic velocities is rarely reported. Therefore, in this research, a convolution neural network (CNN) is developed to examine the capability of reconstructing gas TD with a machine learning approach. Two models would be trained using two different datasets. One model was trained with ideal TD, the other one was trained with reconstructed TD. Besides, acoustic velocities from two gas TDs were applied to evaluate the 2D visualization performance. One TD was similar to the training data and the other were different. The results indicated that the model trained with ideal gas TD could track the hot spot more closely. However, the 2D visualization results using the model trained with ideal gas TD were poor compared to the other one trained with reconstructed TD when the input acoustic velocities of the TD with two hot spots. It indicated that the proposed method could successfully learn the relationship between TD and acoustic velocities from an ordinary reconstruction algorithm. Besides, the execution time of the proposed model was 0.109s, which is 96% less than the selected iterative reconstruction method. Consequently, the proposed neural networks should be considered as a reliable and efficient 2D gas TD reconstruction methodology.

Original languageEnglish
Title of host publicationIUS 2022 - IEEE International Ultrasonics Symposium
PublisherIEEE Computer Society
ISBN (Electronic)9781665466578
Publication statusPublished - 2022
Event2022 IEEE International Ultrasonics Symposium, IUS 2022 - Venice, Italy
Duration: 2022 Oct 102022 Oct 13

Publication series

NameIEEE International Ultrasonics Symposium, IUS
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727


Conference2022 IEEE International Ultrasonics Symposium, IUS 2022

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics


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