Study on Support Vector Machine for Image Reconstruction Algorithm of Electrical Capacitance Tomography

Support vector machine (SVM) is based on the special small samples theory with strong generalization ability, and is selected as an optimal theory for small samples classify problem. Electrical capacitance tomography (ECT) is a typical small samples and nonlinear mapping problem. In this paper the ECT image reconstruction algorithm based on SVM is proposed and a novel training method is proposed to improve the efficiency of SVM classifier by selecting active penalty parameters. Programming software based on Matlab6.5 and VC6.0, the simulation and experiment results indicates this algorithm has stronger space resolution and generalization ability, overall performance of the algorithm is better than some classic reconstruction algorithm such as LBP, etc. However, the algorithm requires the higher quality of training samples, otherwise less real-time, loss of application value. Keywords-support vector machine; electrical capacitance tomography; image reconstruction; space resolution; generalization ability Identification of Multiphase Flow widely in petroleum, chemical industry, river training and other areas of national product, as the complexity of multiphase fluid, random much, it is difficult using traditional detection techniques to accurately measure. Electrical Capacitance Tomography (ECT) technology is the use of multiphase medium tending to have a different dielectric constant, through the array of electrode capacitance changes, reflecting the multiphase medium pipeline distribution to extract the phase of each phase medium concentration, relative size, flow patterns and other characteristic parameters. The technology can be used to detect two-phase / multiphase flow processes, real-time display phase distribution images of multiphase fluid in the pipeline to achieve phase concentration, flow patterns and other characteristic parameters, is a measurement of high speed, non-invasive, low cost, applicable to a wide range of a new generation non-destructive functional imaging techniques [1]. Electrical capacitance tomography system includes capacitance sensor, data acquisition and signal processing unit, image reconstruction and analysis display three functional modules. As the "soft field" feature of system sensitive field, less independent capacitance measurements and other factors, makes the image reconstruction algorithm for the overall performance of the system plays a decisive role. As the current practical image reconstruction method are mostly qualitative algorithms (such as linear back projection algorithm), at the expense of accuracy to improve the real-time imaging, making the reliability of the extracted characteristic parameters less, and some high precision real-time imaging algorithm more poor, and no practical value. For less of independent capacitance value for the system, this paper design an image reconstruction algorithm for electrical capacitance tomography based on support vector machine (SVM) from statistical learning theory [2]. I. SVM ALGORITHM DESIGN Support vector machine by Vapnik first proposed (Vapnik, 1995), is the structural risk minimization method to achieve approximate. It integrates the largest interval of hyper-plane, Mercer core; convex quadratic planning, sparse solutions and slack variable technologies can be used for pattern recognition and nonlinear regression, which has become a standard tool for machine learning [3]. The 12-electrode capacitance sensor, for example, the system has 66 independent capacitance values, then an output unit for the support vector machines architecture shown in Figure 1, the number 0 m of the input layer units are equal to 66, the number 1 m of hidden layer units are decided by support vector number in the system. The core of support vector machines makes the non-linear input space into linearly separable feature space through the inner core function ) , ( i x x K [4]. Make 1 )} ( { m j j x    express a collection of non-linear Communications in Information Science and Management Engineering CISME CISME Vol.1 No.4 2011 PP.55-58 www.jcisme. org ○C World Academic Publishing 56 Fig.2 System meshing map and sample numbers transformation from the input space to feature space, in which x  is the input space vector. 1    k k d y Indicate existence of high dielectric constant material in the output unit. 1    k k d y Indicate existence of low dielectric constant material in the output unit. Where k y is output value of the output unit, k d is target value of the output unit. So the inner product kernel function as