【作者】 刘春婷；

【导师】 颜华；

【作者基本信息】 沈阳工业大学 ， 检测技术与自动化装置， 2004， 硕士

【摘要】 电容层析成像技术具有非侵入、响应速度快、结构简单、成本低、安全性能好等优点，是一种非常有发展潜力的PT技术，可适用于两相流检测的很多应用领域。 目前在ECT系统中较常用的两种图像重建算法是线性反投影(LBP)算法和迭代算法。LBP算法重建速度快，适合在线实时成像，但重建图像的精度不高；迭代算法属于多步算法，可反复修正成像结果得到较高精度，缺点是速度较慢。由于ECT技术在线实时成像的精度较低，还不能满足工业生产的要求，限制了ECT技术的推广应用。 ECT图像重建精度不高的主要原因是ECT敏感场的“软场”问题以及投影数据有限。本文从两个方面着手研究改善ECT技术重建图像的质量，一是增加投影数据，获得较多的介质分布信息，二是图像重建算法。 增加ECT系统的测量极板数目，可以增加投影数据量。考虑到增加极板数须减少极板宽度，进而会加大测量电路的检测难度，本文采用神经网络实现投影数据的增加，由8极板投影数据通过一个预先训练好的神经网络获得16极板的投影数据。神经网络训练采用的是贝叶斯正则化和Levenberg-Marquardt法相结合的训练算法，可以提高网络的泛化能力并且收敛速度很快。仿真结果表明，这种方法可以提高重建图像的精度。 本文提出一种基于灵敏度矩阵奇异值分解的图像重建算法，用奇异值分解方法求ECT灵敏度矩阵的逆，可以获得较好的近似，并可以通过去除较小的奇异值增加重建的稳定性。该算法重建图像所用的时间与LBP算法相当，而重建图像的质量与迭代算法相近，是一种精度较高的实时重建算法。更多还原

【Abstract】 Electrical capacitance tomography (ECT) technique has the advantage of being non-intrusive, fast in response, simple in structure, low in cost and good in security, so it is one technique which has great developing potential in industrial application. ECT technique can be applied in many areas of two-phase flow detection.At present, linear back-projection (LBP) algorithm and iterative algorithm are the most common used algorithms. LBP algorithm has the advantage of fast speed in image reconstruction. This algorithm is fit for real-time image reconstruction, but its reconstruction quality is not accurate. Iterative algorithm is a multi-step algorithm; the reconstruction image will be amended repeatedly to acquire more accurate result. But more time consuming is its disadvantage. The quality of real-time image reconstructed by ECT system does not satisfy production needs, which limits ECT application in practical industry.There are two main reasons that cause ECT reconstruction images not accurate: the soft field characteristic of ECT sensor sensitivity, and the very small projection data. Two aspects to improve ECT image reconstruction quality are investigated in this paper. One is increasing the number of projection data to obtain more information of medium distribution; the other is researching rapid and accurate image reconstruction algorithm.Using more measurement electrodes in ECT system can obtain more projection data. However, when electrode number is increased, the electrode width must be lessened. This will bring detection difficulty of measured circuit. In this paper, artificial neural network technique is applied to acquire more projection data. 16-electrode ECT system’s projection data is acquired when the 28 measurements of an 8-electrode ECT sensor are fed into a pre-trained neural network. Levenberg-Marquardt algorithm and Bayesian regularization are used in network training and the network can convergence rapidly and generalize well. The simulation results show this algorithm can improve image reconstruction quality.In this paper, an image reconstruction algorithm based on singular value decomposition (SVD) of sensitivity matrix is proposed; the inverse of ECT sensitivity matrix is calculated use SVD method. This method can obtain a more accurate approximation of sensitivity inverse, andthe smaller singular value can be neglected to improve stability of the reconstruction result. The time consumed in reconstruction is similar to that of LBP algorithm. The image result is more accurate than that of LBP algorithm and similar to that of iterative algorithm. So this new method is a real-time and more accurate reconstruction algorithm.更多还原