在线估计燃烧氧含量与图像增强软传感器使用不平衡的火焰图像
Shuang Gao1, Angpeng Liu2, Yuxin Jiang2
1School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, People's Republic of China.
ACS omega
|November 6, 2023
概括
精确的氧含量测量对于燃烧效率至关重要. 本研究介绍了一种生成模型,用于创建合成火焰图像,提高氧气水平预测的准确性并减少数据收集需求.
科学领域:
- 燃烧工程 燃烧工程
- 图像处理 图像处理
- 机器学习 机器学习
背景情况:
- 精确的氧含量测量对于优化燃烧效率和经济性能至关重要.
- 使用火焰图像的软测量技术为氧含量分析提供了有前途的方法.
- 在不同氧气水平的图像特征获取和在不平衡条件下生成图像方面仍然存在挑战.
研究的目的:
- 开发一种新的基于生成的回归模型,用于从火焰图像中准确估计氧含量.
- 在不平衡的数据条件下解决图像特征采集和生成方面的挑战.
- 增强培训数据集,提高氧含量测量的准确性.
主要方法:
- 开发了一个基于生成的回归模型来学习潜在向量和捕获火焰特征.
- 该模型几乎可以生成高质量的标记火焰图像,增强训练数据集.
- 一个基于卷积的回归模型直接从增强火焰图像中估计氧含量.
主要成果:
- 生成模型成功捕获了火焰特征,并生成了信息性的合成火焰图像.
- 通过生成图像来增强数据减少了对广泛数据收集实验的需求.
- 与一些常见的技术相比,拟议的方法实现了更准确的氧含量估计.
结论:
- 开发的基于生成的回归模型有效地提高了从火焰图像中估计的氧含量.
- 合成图像生成为燃烧分析中的数据增强提供了可行的解决方案.
- 这种方法提高了氧气测量的准确性和过程的效率.
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