Enhancing electrical capacitance tomographic sensor design using fuzzy theory based quantifiers

Abstract

Electrical capacitance tomography is a non-invasive imaging technique that uses measured capacitances to recover the unknown permittivity distribution in the medium. A new method using fuzzy theory and quantifiers in enhancing the ECT sensor system design and the associated data fusion is presented. Unlike the traditional methods that set a single indicator as the optimization objective, the method presented in the paper provides a way to integrate multiple evaluation criteria (some may conflict with others) of ECT sensors into a single comparable index. The uniformity index, the correlation coefficient and combinatorial fuzzy index of ECTs are set as the optimization objectives respectively at the experimental stage to evaluate the validity of the method. The experiments are set up based on multi-index orthogonal design and the experimental results indicate that the fuzzy optimization method can derive an optimized sensor structure with an evenly distributed sensitivity field and a better imaging reconstruction result at the same time. It proves the method is intuitive, reliable, and practical.