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

我们的使命是发展可行的计算方法，定量无损评价有关的涡流检测与计算智能的背景知识。在研究期间，我们的努力是针对以下逆求解器的发展。(1)基于遗传程序设计（GP）的模糊推理系统的形状恢复算法将遗传程序设计（GP）与模糊推理系统相结合，提出了涡流检测的定量无损评价方法。GP被应用于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品的缺陷的存在、位置和大小。(2)多元样条快速计算反问题求解方法利用正问题解的数据集直接在参数空间重构模型输出。因此，输出最小二乘问题的灵敏度可以是e 关于我们 估值很快。因此，大量的计算节省已实现使用所提出的计划。(3)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种群的CEA。结果表明，所提出的方法可以获得裂纹的数量以及识别其形状。(4)基于超导量子干涉仪（SQUID）的无损检测系统（NDE）形状识别研究了超导量子干涉仪（SQUID）对含深度变化裂纹的导电材料的定量无损检测。提出了一种基于遗传算法的SQUID数据恢复内部缺陷轮廓的计算方法，并成功地对JSAEM基准问题进行了测试，包括带自然裂纹的屏蔽管样品。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