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Pipeline image diagnosis algorithm based on neural immune ensemble learning
International Journal of Pressure Vessels and Piping ( IF 3 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.ijpvp.2020.104249
Xiao Yu , YuHua Lu , Qiang Gao

Abstract In order to provide direction guidance for the heat loss of steam pipeline and ensure the safe and energy-saving operation of steam pipe network, it is very important to realize rapidly and accurate diagnosis of high-temperature area of the pipeline. This paper is aimed at the problem of the complicated working environment of the steam pipe, which causes the infrared image obtained have strong background interference. A neural immune ensemble learning algorithm, based on the ensemble learning theory and the coordination between nervous system and immune system, is proposed to diagnose abnormal high temperature region in infrared steam pipeline image. Experimental results show that the proposed algorithm can effectively diagnose the high temperature region of steam pipeline. Compared with other image classification algorithms, the results show that the neural immune ensemble learning algorithm has higher diagnostic accuracy and can meet the needs of actual engineering applications.

中文翻译:

基于神经免疫集成学习的流水线图像诊断算法

摘要 为了为蒸汽管道热损失提供方向指导,保障蒸汽管网安全节能运行,实现管道高温区域的快速准确诊断十分重要。本文针对蒸汽管道复杂的工作环境,导致获得的红外图像具有较强的背景干扰的问题。提出一种基于集成学习理论和神经系统与免疫系统协调的神经免疫集成学习算法,用于诊断红外蒸汽管道图像中的异常高温区域。实验结果表明,该算法能够有效地诊断蒸汽管道高温区域。与其他图像分类算法相比,
更新日期:2021-02-01
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