化学工程2011，Vol.39Issue(12)：59-63,5.
基于图像动态纹理特征的气固流化床流型识别
Flow regime identification of gas-solid fluidized bed based on images dynamic texture characteristics
周云龙 1李莹 1赵红梅1
作者信息
- 1. 东北电力大学能源与动力工程学院,吉林吉林132012
- 折叠
摘要
Abstract
The exact identification of flow regime is an important content to detect the parameters of gas-solid two-phase flow fluidized bed. A new flow regime identification method based on images optical flow technique and dynamic texture was proposed. The experiment was conducted on gas-solid fluidized bed system and the flow images were captured by a high speed photography system. The flow images are of five typical regimes of gas-solid two-phase flow fluidized bed, including bubbling bed, slugging bed, turbulent bed, wall pressing flow, and thin phase conveying. First, the different flow images captured were pretreated by denoising and contrast stretching, then, optical flow technique was used to get the optical flow field of continuous two frames images. The image dynamic texture characteristics were extracted by gray level co-occurrence matrix, regarded as input variable. Those samples were separately sent to elasticity BP neural net, Elman neural net and BP neural net work for optimization. Thus the image texture eigenvectors of flow regime were identified. The experimental results show that the combination between dynamic textures and elasticity BP neural net can more effectively identify the five typical regimes of gas-solid two-phase flow fluidized bed. The whole identification accuracy is 98% , opening up a new avenue of flow pattern recognition.关键词
气固流化床/流型识别/光流法/灰度共生矩阵/图像处理Key words
gas-solid fluidized bed/ flow regime identification/ optical flow technique/ gray level co-occurrence matrix/ image processing分类
数理科学引用本文复制引用
周云龙,李莹,赵红梅..基于图像动态纹理特征的气固流化床流型识别[J].化学工程,2011,39(12):59-63,5.
