التفاصيل البيبلوغرافية
| العنوان: |
一种SOFC燃烧室燃烧状态识别方法. (Chinese) |
| العنوان البديل: |
Combustion state recognition of SOFC combustor. (English) |
| المؤلفون: |
王阳, 付晓薇, 李曦 |
| المصدر: |
Application Research of Computers / Jisuanji Yingyong Yanjiu; Aug2023, Vol. 40 Issue 8, p2531-2536, 6p |
| مصطلحات موضوعية: |
SOLID oxide fuel cells, CONVOLUTIONAL neural networks, FEATURE extraction, PROBLEM solving, COMBUSTION, SQUEEZED light, MOLECULAR recognition |
| الملخص (بالإنجليزية): |
To solve the problem of combustion state recognition in solid oxide fuel cell (SOFC) combustor, this paper proposed a combustion state recognition method based on attention mechanism and image feature pyramid. The method adopted the adaptive gamma correction with weighting distribution (AGCWD) to standardize the input images. It combined two single full connections with 1×1 convolution to replace the squeeze and excitation structure, and proposed a hybrid attention structure combined with spatial attention structure to enhance the ability of feature extraction. To provide multi-scale information communication capability, it constructed the multi-scale bidirectional fusion pyramid by means of bidirectional computation and multi-scale fusion. The experimental results show that the proposed method reaches 99.22% accuracy under the premise of 3.98 M parameters and 397 M floating point operations (FLOPs), and effectively identifies the combustion state in SOFC combustor. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
针对固体氧化物燃料电池 (SOFC) 燃烧室燃烧状态识别问题, 提出一种基于注意力机制与图像特征金字塔的SOFC燃烧室燃烧状态识别方法。该方法使用加权分布的自适应伽马矫正算法 (AGCWD) 进行数据前处理, 对数据进行标准化；利用两个附加1×1卷积的全连接改进了压缩—激励结构, 并结合空间注意力, 提出了一种混合注意力结构, 提升了网络特征提取能力；为增强特征的多尺度信息交流能力, 使用双向计算和多尺度融合, 提出了多尺度双向融合金字塔。实验表明, 所提方法在参数量为3.98 M, 浮点运算数（FLOPs）为397 M的前提下, 识别准确率达到99.22%, 能够有效识别SOFC燃烧室燃烧状态。 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
