...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Measurement Science & Technology >ECT Attention Reverse Mapping algorithm: visualization of flow pattern heatmap based on convolutional neural network and its impact on ECT image reconstruction
【24h】

ECT Attention Reverse Mapping algorithm: visualization of flow pattern heatmap based on convolutional neural network and its impact on ECT image reconstruction

机译：注意力反向映射算法：基于卷积神经网络的流动模式热图的可视化及其对图像重建的影响

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The flow pattern is one of the most basic characteristic parameters of oil-gas two-phase flow, and it has a great influence on the accurate measurement of other parameters of two-phase flow. Over the past decade, the convolutional neural network (CNN) algorithm has been widely used in flow pattern research. Unfortunately, the flow pattern research based on the CNN algorithm is more on model structure optimization, and there is still little insight into the relationship between the CNN algorithm and the physical meaning of the flow pattern. Thus, in this paper, inspired by the neural network visualization gradient-based class activation mapping (Grad-CAM) method, we propose the electrical capacitance tomography (ECT) Attention Reverse Mapping algorithm (EARM) to explore the relationship between the physical meaning of flow patterns and the CNN algorithm. Specifically, the Grad-CAM method is used to obtain heatmaps of flow patterns, and the EARM algorithm combines the hotspot information of the flow pattern heatmap with the ECT image reconstruction principle, which deeply explores the relationship between the CNN flow pattern identification and the ECT image reconstruction algorithm. Furthermore, we conduct prediction experiments based on the parameters of the flow pattern hotspot capacitance data, and the experimental results are compared with the ECT original capacitance data parameter prediction. The prediction accuracy of oil-gas two-phase flow parameters has been improved by more than 50% on average, and experiments have verified the correctness of the visualization of CNN network flow pattern identification.
机译：None

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号