[1]杨道业,周宾,许传龙,等.厚管壁电容层析成像图像重建算法[J].东南大学学报(自然科学版),2007,37(3):451-456.[doi:10.3969/j.issn.1001-0505.2007.03.020]
 Yang Daoye,Zhou Bin,Xu Chuanlong,et al.Image reconstruction algorithms for electrical capacitance tomography with thick-wall pipeline[J].Journal of Southeast University (Natural Science Edition),2007,37(3):451-456.[doi:10.3969/j.issn.1001-0505.2007.03.020]
点击复制

厚管壁电容层析成像图像重建算法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
37
期数:
2007年第3期
页码:
451-456
栏目:
其他
出版日期:
2007-05-20

文章信息/Info

Title:
Image reconstruction algorithms for electrical capacitance tomography with thick-wall pipeline
作者:
杨道业1 周宾1 许传龙1 贡志林2 王式民1
1 东南大学洁净煤发电及燃烧技术教育部重点实验室, 南京 210096; 2 江苏省特种设备安全监督检验研究院, 镇江 212009
Author(s):
Yang Daoye1 Zhou Bin1 Xu Chuanlong1 Gong Zhilin2 Wang Shimin1
1 Key Laboratory of Clean Coal Power Generation and Combustion Technology of Ministry of Education, Southeast University, Nanjing 210096, China
2 Jiangsu Province Special Equipment Safety Supervision Inspection Institute, Zhenjiang 212009, China
关键词:
层析成像 厚管壁 电容传感器 图像重建 BP神经网络
Keywords:
tomography thick-wall pipeline capacitance sensor image reconstruction BP neural networks
分类号:
TB934
DOI:
10.3969/j.issn.1001-0505.2007.03.020
摘要:
探讨了适合厚管壁条件下的电容层析成像图像重建算法.针对厚管壁管道内几种不同流型,分别采用LBP算法、Landweber迭代算法和BP神经网络对8电极电容传感器进行成像重建计算.结果表明:在厚管壁情况下,LBP算法重建的图像质量很差; Landweber迭代算法在层流下的重建效果好于核心流和环状流; 而BP神经网络算法可以有效重建管道内的介质分布,但对于没有训练到的任意流型,其重建效果不够理想.
Abstract:
An ECT(electrical capacitance tomography)sensor with thick-wall pipeline is researched, including sensitivity map characteristic and suitable image reconstruction algorithms. For different flow distribution patterns in thick-wall pipeline, the algorithms such as linear back-projection(LBP), Landweber iteration and BP neural networks are adopted to reconstruct the images in 8-electrode ECT sensor. The reconstruction results demonstrate that the reconstructed image quality with LBP is poor. Meanwhile, the reconstructed image in stratified flow is better than the ones in core flow and annular flow with Landweber algorithm. More over, BP neural networks algorithm is the only effective way to reconstruct the inner-pipe permittivity distribution, but the result is not satisfactory when the flow distribution patterns are not trained.

参考文献/References:

[1] 李海青,黄志尧.特种检测技术及应用[M].杭州:浙江大学出版社,2000:6-7.
[2] Jaworski A J,Bolton G T.Design of an electrical capacitance tomography sensor for use with media of high dielectric permittivity [J].Measurement of Science and Technology,2000,11(6):743-757.
[3] 王保良.电容层析成像技术及其在两相流参数检测中的应用[D].杭州:浙江大学控制工程与工程学系,1998.
[4] Peng Lihui,Yang W Q.Image reconstruction algorithms for electrical capacitance tomography [J]. Measurement of Science and Technology,2003,11(1):R1-R13.
[5] 颜华,高静.电容层析成像的仿真研究[J].系统仿真学报,2003,15(11):1625-1627.
  Yan Hua,Gao Jing.The simulation study of the electrical capacitance tomography [J].Acta Simulata Systematica Sinica,2003,15(11):1625-1627.(in Chinese)
[6] Xie C G,Huang S M,Hoyle B S,et al.Electrical capacitance tomography for flow imaging:system model for development of image reconstruction algorithms and design of primary sensors [J]. IEE Proceeding-G,1992,139(1):89-98.
[7] 杨理践,颜华.电容层析成像系统测量数据的归一化处理[J].仪器仪表学报,2002,23(3):229-231.
  Yang Lijian,Yan Hua.Normalisation approach for capacitance tomography measurement data [J]. Chinese Journal of Scientific Instrument,2002,23(3):229-231.(in Chinese)
[8] Landweber L.An iterative formula for Fredholm integral equations of the first kind [J]. Amer J Math,1951,73(3):615-624.
[9] Yang W Q,Spink D M,York T A,et al.An image reconstruction algorithm based on Landweber iteration method for electrical capacitance tomography[J].Meas Sci Technol,1999,10(11):1065-1069.
[10] 周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社,2005:69-100.

备注/Memo

备注/Memo:
基金项目: 国家重点基础研究发展计划(973计划)资助项目(2004CB217702).
作者简介: 杨道业(1980—),男,博士生; 王式民(联系人),男,教授,博士生导师, smwang@seu.edu.cn.
更新日期/Last Update: 2007-05-20