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基于级联视觉Transformer与多尺度特征融合的燃烧场温度层析成像

司菁菁 王晓莉 程银波 刘畅

司菁菁, 王晓莉, 程银波, 刘畅. 基于级联视觉Transformer与多尺度特征融合的燃烧场温度层析成像[J]. 电子与信息学报, 2023, 45(10): 3511-3519. doi: 10.11999/JEIT221034
引用本文: 司菁菁, 王晓莉, 程银波, 刘畅. 基于级联视觉Transformer与多尺度特征融合的燃烧场温度层析成像[J]. 电子与信息学报, 2023, 45(10): 3511-3519. doi: 10.11999/JEIT221034
SI Jingjing, WANG Xiaoli, CHENG Yinbo, LIU Chang. Temperature Tomography for Combustion Field Based on Hierarchical Vision Transformer and Multi-scale Features Merging[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3511-3519. doi: 10.11999/JEIT221034
Citation: SI Jingjing, WANG Xiaoli, CHENG Yinbo, LIU Chang. Temperature Tomography for Combustion Field Based on Hierarchical Vision Transformer and Multi-scale Features Merging[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3511-3519. doi: 10.11999/JEIT221034

基于级联视觉Transformer与多尺度特征融合的燃烧场温度层析成像

doi: 10.11999/JEIT221034
基金项目: 河北省自然科学基金(F2021203027),燕山大学基础创新科研培育项目(2021LGZD011),河北省重点实验室项目(202250701010046)
详细信息
    作者简介:

    司菁菁:女,教授,研究方向为多媒体信号处理

    王晓莉:女,硕士生,研究方向为基于深度学习的逆问题求解

    程银波:男,讲师,研究方向为深度学习

    刘畅:男,讲师,研究方向为激光吸收光谱及其层析成像技术

    通讯作者:

    程银波 cyb@hebau.edu.cn

  • 中图分类号: TN919.8

Temperature Tomography for Combustion Field Based on Hierarchical Vision Transformer and Multi-scale Features Merging

Funds: The Natural Science Foundation of Hebei Province (F2021203027), The Cultivation Project for Basic Research and Innovation of Yanshan University (2021LGZD011), Hebei Key Laboratory Project (202250701010046)
  • 摘要: 可调谐二极管激光吸收光谱层析成像(TDLAT)是一种重要的光学非侵入式燃烧诊断技术,可实现燃烧场2维横截面气体温度和浓度等流场参数分布的重建。该文将视觉Transformer(ViT)与多尺度特征融合引入TDLAT领域,研究有限数量测量数据与整个测量空间温度分布的非线性映射,提出基于级联ViT与多尺度特征融合的燃烧场温度层析成像网络(HVTMFnet)。该网络提取并融合TDLAT测量数据的局部-全局相关特征,实现整个测量空间的层次化温度分布重建。仿真实验与实际TDLAT系统实验均表明,HVTMFnet重建图像的质量优于现有的基于卷积神经网络(CNN)和基于残差网络的温度层析成像方案。与基于CNN的温度层析成像方案相比,HVTMFnet的重建误差能够降低49.2%~72.1%。
  • 图  1  层次化离散模型示例

    图  2  HVTMFnet模型

    图  3  TDLAT实验系统中的光路布置示意图

    图  4  6种重建模型的误差值比较

    图  5  4种网络模型对代表性单峰样本的重建图像及残差图像

    图  6  4种网络模型对代表性双峰样本的重建图像及残差图像

    图  7  4种网络模型对代表性真实单峰火焰燃烧场的重建图像

    图  8  4种网络模型对代表性真实双峰火焰燃烧场的重建图像

    表  1  实验中HVTMFnet模型的参数设置

    ${H_{m - 1}} \times {W_{m - 1}} \times {C_{m - 1}}$${H_{{\text{patch}}}}$${W_{{\text{patch}}}}$$N_{{\text{patch}}}^m$${D_m}$$D_{{\text{FFN}}}^m$$N_{{\text{layer}}}^m$$N_{{\text{head}}}^m$$d_{{\text{head}}}^m$${H_m} \times {W_m} \times {C_m}$
    m=14×8×222816642282×4×16
    m=22×4×1622212851268161×2×128
    下载: 导出CSV

    表  2  实验中采用的数据集参数设置

    $ {T_{{\text{min}}}} $$ {X_{{\text{min}}}} $$ {u_d} $$ {v_d} $$ {\beta _d} $$ {\mu _{x,d}} $$ {\mu _{y,d}} $$ {\sigma _d} $
    300 K0.01U(300,600)U(0.10,0.11)U(0.70,1)U(34,65)U(34,65)U(10,25)
    下载: 导出CSV

    表  3  4种网络模型训练时间及重建时间的对比

    H-CNNHTT-ResNetPI-CNNHVTMFnet
    训练时间(min)0.812.672.866.10
    重建时间(s)2.23×10–67.24×10–65.45×10–57.83×10–6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-08
  • 修回日期:  2022-10-24
  • 网络出版日期:  2022-10-26
  • 刊出日期:  2023-10-31

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