IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3118-d801408.html
   My bibliography  Save this article

An Explicable Neighboring-Pixel Reconstruction Algorithm for Temperature Distribution by Acoustic Tomography

Author

Listed:
  • Qirong Qiu

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

  • Wanting Zhou

    (School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
    National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing 100190, China)

  • Qing Zhao

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Shi Liu

    (Ningbo Image Probing Measurement Technology Co., Ltd., Ningbo 315211, China)

Abstract

Acoustic process tomography is a powerful tool for monitoring multiphase flow and combustion. However, its capability of revealing details of the interrogation zone is restricted by the ill-posed and rank deficiency problems. In each projection, a probing sound beam only passes the pixels along its propagation path, resulting in a large number of zero-valued elements in the measurement matrix. This is more pronounced as the resolution of the imaging zone becomes gradually finer, which is detrimental to image reconstruction. In this study, a mathematically explicable reconstruction algorithm of regularization is proposed by assigning each zero-valued pixel with a combination of the values of the neighboring pixels, ruled by the appropriate regularization factors. The formula to determine the regularization factors is also derived. Simulations are carried out to verify this new approach, and some representative cases are presented. As a result, the ambiguity of the inverse process is removed, and the accuracy of the image reconstruction is significantly improved. The results show the robustness of the algorithm and certain advantages over the standard Tikhonov regularization formula.

Suggested Citation

  • Qirong Qiu & Wanting Zhou & Qing Zhao & Shi Liu, 2022. "An Explicable Neighboring-Pixel Reconstruction Algorithm for Temperature Distribution by Acoustic Tomography," Energies, MDPI, vol. 15(9), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3118-:d:801408
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3118/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3118/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuhui Wu & Xinzhi Zhou & Li Zhao & Chenlong Dong & Hailin Wang, 2021. "A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, September.
    2. Qian Kong & Genshan Jiang & Yuechao Liu & Jianhao Sun, 2019. "3D Temperature Distribution Reconstruction in Furnace Based on Acoustic Tomography," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-15, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yin, Linfei & Zhou, Hang, 2024. "Modal decomposition integrated model for ultra-supercritical coal-fired power plant reheater tube temperature multi-step prediction," Energy, Elsevier, vol. 292(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3118-:d:801408. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.