Clustering-based Electrical Image Reconstruction Algorithms And Applications

With the rapid development in recent several ten years, Electrical Tomography(Electrical Tomography, ET) techniques have been successfully used in two-phaseflow/multiphase flow parameter measurement, and ET techniques have great potentialand could be widely used in industry.Electrical Tomography (Electrical Tomography, ET) is one of the techniques ofPT, which is widely researched. It has advantages of low-cost, high-speed, robust andnon-intrusive, et. al. Thus, ET technology has become a hot research.However, despite the ET technology has made great progress, the accuracy andspeed of image reconstruction have not been achieved more progress. In order toobtain better image quality and faster imaging speed, many researchers have madegreat efforts on image reconstruction algorithm, such as LBP and Land Weber est. But,the applications of these algorithms have different situations and limitations.To overcome the problems of speed and precision of the existing algorithms, wecreatively present a method of image reconstruction algorithm based on fuzzyclustering. Different from the existing algorithms, we map the information fromdifferent sensors into a vector space for clustering analysis., Then, all attributes ofeach vector are not equally important in clustering process,and alternatively theeffects of some attributes are enlarged while the other is reduced. Consequently, ouralgorithm can fully utilize the existing information to obtain the better reconstructedimages. Consequently, our algorithm is easily realized, lower time complexity, andupdated in a real-time and automatic manner. Thus our algorithm contributes a newway to the image reconstruction techniques.Finally, we did the simple image fusion of ERT and ECT from the perspectiveof data fusion.In addition, we apply Comsol's great simulation on electromagnetic data, to getour experimental data. It can get completely satisfactory data, on the other hand, toour new algorithm; the ideal data can be favorable to our algorithm verification andthe development of algorithms.