Difference of Convex Functions Programming With Machine-Learning Prior for the Imaging Problem in Electrical Capacitance Tomography

The electrical capacitance tomography technology has potential benefits for the process industry by providing visualization of material distributions. One of the main technical gaps and impediments that must be overcome is the low-quality tomogram. To address this problem, this study introduces the...

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Vydáno v:IEEE transactions on cybernetics Ročník 53; číslo 12; s. 7535 - 7547
Hlavní autoři: Lei, Jing, Liu, Qibin
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Capacitance
Convex analysis
Convex functions
Difference of convex functions programming problem
Dimensionality reduction
Electric fuses
electrical capacitance tomography
Electrical measurement
Image reconstruction
Imaging
inverse problem
Machine learning
Noise sensitivity
non-negative matrix factorization
Optimization
Performance evaluation
Physics
Programming
relevance vector machine
Relevance vector machines
Tomography
visualization measurement
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:The electrical capacitance tomography technology has potential benefits for the process industry by providing visualization of material distributions. One of the main technical gaps and impediments that must be overcome is the low-quality tomogram. To address this problem, this study introduces the data-guided prior and combines it with the electrical measurement mechanism and the sparsity prior to produce a new difference of convex functions programming problem that turns the image reconstruction problem into an optimization problem. The data-guided prior is learned from a provided dataset and captures the details of imaging targets since it is a specific image. A new numerical scheme that allows a complex optimization problem to be split into a few less difficult subproblems is developed to solve the challenging difference of convex functions programming problem. A new dimensionality reduction method is developed and combined with the relevance vector machine to generate a new learning engine for the forecast of the data-guided prior. The new imaging method fuses multisource information and unifies data-guided and measurement physics modeling paradigms. Performance evaluation results have validated that the new method successfully works on a series of test tasks with higher reconstruction quality and lower noise sensitivity than the popular imaging methods.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3173336