Advanced studies on eddy current testing using computational intelligence related to electromagnetic inverse problems

我们的任务是利用计算智能的背景知识，为涡流检测相关的定量无损评估开发可行的计算方法。(1)基于遗传算法和模糊推理系统的形状恢复算法提出了一种基于遗传规划和模糊推理系统的涡流检测定量无损评价算法。应用遗传算法从探头阻抗轨迹中提取和选择有效特征。利用提取的特征，模糊推理系统可以检测出测试样本中缺陷的存在、位置和大小。(2)基于多元样条法的快速计算反求器该方法利用正解的数据集，通过参数空间直接重构模型输出。因此，输出最小二乘问题的灵敏度可以是e…。更高的估值非常快。将进化计算及其在QNDEA协同进化算法(CEA)中的应用有效地应用于形状数目未知的裂纹形状识别问题。提出了一种包含候选形状和候选组合两个群体的CEA算法。实验结果表明，该方法不仅可以获得裂纹的数目，还可以识别裂纹的形状。(4)基于SQUID的无损检测系统对具有深度变化裂纹的导电材料进行了超导量子干涉器件的定量无损评价。提出了一种基于遗传算法的计算方法，用于利用SQUID数据恢复内部缺陷轮廓，并成功地对JSAEM基准问题进行了测试，包括带有天然裂纹的屏蔽管样本。较少

Our mission is to develop feasible computational methods for quantitative nondestructive evaluation related to eddy current testing with the background knowledge of computational intelligence. During the period of the research, our efforts were directed to the developments of the following inverse solvers.(1) Shape recovering algorithm using the GP-based fuzzy inference systemA quantitative nondestructive evaluation in eddy current testing was proposed by using genetic programming (GP) and fuzzy inference system. GP is applied to extract and select effective features from a probe impedance trajectory. With the use of the extracted features, a fuzzy inference system could detect presence, position, and size of a defect of test sample.(2) Fast computational inverse solver using the multivariate splinesIn this method, the model output was directly reconstructed by parameter space with data sets of the forward solutions. Hence the sensitivities of the output least square problem could be e … More valuated very fast. As a result, the tremendous amounts of computational savings have been achieved using the proposed scheme.(3) Evolutionary computation and its application to QNDEA coevolutionary algorithm (CEA) was effectively used for a crack shape identification problem where the number of shapes is unknown. A CEA including two populations of candidate shapes and of candidate combination was proposed. Results were demonstrated that the proposed method makes it possible to obtain the number of cracks as well as to identify their shapes.(4) Shape identification using the SQUID based NDE systemA quantitative nondestructive evaluation of using superconducting quantum interference devices (SQUIDs) was developed for conducting materials with a depth-varying crack. A computational method based on the genetic algorithm was proposed for recovering internal defect profiles with SQUID data.Consequently, all computations were successfully tested for the JSAEM benchmark problems, including the masked tube samples with natural cracks. Less