基于双粒子群协同优化的ECT图像重建算法

由于电容层析成像(electrical capacitance tomography, ECT)系统中电容传感器的敏感场是“软场”，然而传统的图像重建算法是在忽略“软场”效应的条件下构建的，因此在提高成像精度上存在瓶颈.针对该问题，在分析敏感场分布，并讨论“软场”效应及其对图像重建产生的影响的基础上，提出一种基于双粒子群协同优化的图像重建算法.该算法通过引入用于构造粒子群优化适应度函数的先验条件，消除了因忽略“软场”效应而产生的影响，并通过最小二乘支持向量机得到不同流型下的先验条件.同时，该算法通过借鉴Lotka-Volerrra双群协同竞争模型，提出一种双群协同竞争方案，通过群内与群间的学习竞争提高粒子多样性，从而提高粒子群的全局收敛能力和收敛速度.实验结果表明，该算法不仅成像精度高、易收敛，而且具有抵抗测量信号中噪声干扰的特点.

Image Reconstruction Algorithm for ECT Based on Dual Particle Swarm Collaborative Optimization

Since the sensitivity field in the capacitance sensor of electrical capacitance tomography system is “soft field”, and the “soft field” nature is ignored by the traditional image reconstruction algorithms, there is bottleneck in improving the imaging accuracy for the algorithms. To solve the problem, based on the analysis of the distribution of sensitivity field and the discussion of the “soft field” effect and its impact on the image reconstruction, a novel image reconstruction algorithm is proposed, which is dual particle swarm collaborative optimization. In the algorithm, to eliminate the impact generated by ignoring the “soft field” nature, a priori condition is used to construct the fitness function of particle swarm optimization. The priori conditions under the different flow patterns are obtained by the least square support vector machine. Meanwhile, by introducing the Lotka-Volterra model, a new cooperative-competitive scheme is discussed. The diversity of particles is increased by intraspecific and interspecific learning and competition. So the algorithm improves the global convergence and convergence rate. The experimental results illustrate that this algorithm not only has higher image precision and stronger convergence, but also is resistant to the interference of noise in the measurement signal.