Development of an edge computing device by integrating deep learning model for flame tomographic reconstruction

集成深度学习模型进行火焰断层重建的边缘计算设备的开发

• 批准号: 2774335
• 金额: --
• 依托单位: University of Kent
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2774335
• 关键词: Development; edge; computing; device; integrating

Due to increasingly tight environment legislation, optical imaging techniques for combustion diagnostics are becoming prevalent in industrial environments for improved combustion efficiency and reduced pollutant emissions.Volumetric tomography (VT) which is one of these techniques is an effective methodused for combustion monitoring and diagnostics as it provides authentic 3D information for combustion diagnostics. The development of VT has helped in understanding of complex combustion processes such as flame ignition and combustion instability. Also, the unique features of VT include non-intrusiveness and easy implementation, which has made such an optical imaging technique suitable for the spatial and temporal monitoring and diagnostics of combustion systems.There are various reconstruction approaches suitable for volumetric image reconstruction (Fig 1). They include analytic reconstruction which is based on the mathematical inversion, iterative reconstruction based on numerical inversion and deep learning-based image reconstruction.

随着环境保护法规的日益严格，为了提高燃烧效率和减少污染物的排放，光学成像技术在燃烧诊断中的应用越来越广泛，其中的容积层析成像技术（Volumetric Tomography，VT）为燃烧诊断提供了真实的三维信息，是一种有效的燃烧监测和诊断方法。VT的发展有助于理解复杂的燃烧过程，如火焰点火和燃烧不稳定性。此外，VT的独特功能包括非侵入性和易于实施，这使得这种光学成像技术适用于燃烧系统的空间和时间监测和诊断。有各种适用于体积图像重建的重建方法（图1）。它们包括基于数学反演的解析重建，基于数值反演的迭代重建和基于深度学习的图像重建。