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Sensor Reconfigurability through Uncertainty Reduction in Adaptive Electrical Volume Tomography

Ospina Acero, Daniel
http://orcid.org/0000-0002-7844-5099
http://rave.ohiolink.edu/etdc/view?acc_num=osu1638212300985692

Abstract Details

2021, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
Electrical Capacitance Tomography (ECT) and its derived technologies represent one of the preferred mechanisms to study multi-phase flows in industrial applications due to their low cost, relatively fast imaging speed, non-invasiveness, non-intrusiveness and robustness. One important weakness of these technologies, however, is that they are part of what is known as soft-field imaging techniques, where the operating frequencies of the interrogating fields are very low, ultimately resulting in low spatial resolution. In addition, the problem of image reconstruction in ECT defines an inverse problem, which corresponds to a series of particular challenges in the solution process: mainly, lack of uniqueness in the solution, and high degree of numerical instability. This reality in practical applications results in severely underdetermined systems of equations, with particular sensitivity towards random perturbations in the input data. In the last few years there have been numerous efforts to address those difficulties in ECT-based systems, but they essentially can be categorized in two main sets. The first one corresponds to the exploration of different strategies in the algorithms to perform the image reconstruction, and the second one corresponds to different hardware mechanisms to try to obtain more information from the sensing domain. The work that we present in this dissertation deals with both. In the first case, we employ the Bayesian regression framework configured by the Relevance Vector Machine (RVM) to define an algorithm for ECT applications that can concurrently provide image reconstruction results and uncertainty estimates about the reconstruction. To illustrate the RVM operation in ECT, we simulate typical ECT scenarios, making explicit the connection between the reconstructed pixel values and the corresponding uncertainty estimates in each case. We compare the RVM reconstruction performance with that of the Iterative Landweber Method (ILM) and the least absolute shrinkage and selection operator (LASSO) in all the considered scenarios. The results show that, in addition to the key advantage of providing uncertainty measures, RVM can achieve similar reconstruction results with either lower or similar computational complexity. In the second case, we introduce an efficient synthetic electrode selection strategy for use in Adaptive Electrical Capacitance Volume Tomography (AECVT), which is a volumetric imaging technology that introduces different changes in the configuration of the sensor to allow for more flexible scanning procedures. The proposed strategy is based on the Adaptive Relevance Vector Machine (ARVM) method and allows to successively obtain synthetic electrode configurations that yield the most decrease in the image reconstruction uncertainty for the spatial distribution of the permittivity in the region of interest. The problem is first formulated as an instance of the Quadratic Unconstrained Binary Optimization (QUBO). By noting that the QUBO formulation is an NP-hard problem and thus prohibitive in practice, we then introduce the Reduced ARVM method, corresponding to the application of the ARVM method to a reduced search space. By using the Reduced ARVM method, good image reconstruction and low uncertainty levels can be achieved in AECVT with considerably fewer measurements. To corroborate our analysis, we present simulation results for three representative AECVT scenarios.
Fernando Teixeira (Advisor)
Bradley Clymer (Committee Member)
Robert Burkholder (Committee Member)
178 p.
Computer Engineering; Electrical Engineering; Electromagnetics; Electromagnetism

Recommended Citations

Citations

  • Ospina Acero, D. (2021). Sensor Reconfigurability through Uncertainty Reduction in Adaptive Electrical Volume Tomography [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1638212300985692

    APA Style (7th edition)

  • Ospina Acero, Daniel. Sensor Reconfigurability through Uncertainty Reduction in Adaptive Electrical Volume Tomography. 2021. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1638212300985692.

    MLA Style (8th edition)

  • Ospina Acero, Daniel. "Sensor Reconfigurability through Uncertainty Reduction in Adaptive Electrical Volume Tomography." Doctoral dissertation, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1638212300985692

    Chicago Manual of Style (17th edition)

osu1638212300985692
74
© 2021, some rights reserved.Creative Commons License Sensor Reconfigurability through Uncertainty Reduction in Adaptive Electrical Volume Tomography by Daniel Ospina Acero is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.