Electrical capacitance tomography image reconstruction by improved orthogonal matching pursuit algorithm

Yan, Hua; Wang, Yan; Wang, Yi Fan; Zhou, Ying

doi:10.1049/iet-smt.2019.0255

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…The OMP algorithm is a typical greedy algorithm. Compared with convex optimization, OMP takes less time [ 31 , 32 ], especially when the value of N is large. The steps of the OMP algorithm to solve Equation (8) are as follows: Initialization.…”

Section: MDL Algorithmmentioning

confidence: 99%

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

Wei

Yan

Zhou

2022

Sensors

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A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields. Its aim is to improve the under-determination of the inverse problem by the sparse representation of the sound slowness signal (i.e., reciprocal of sound velocity). In the MDL algorithm, the K-SVD dictionary learning algorithm is used to construct corresponding sparse dictionaries for sound slowness signals of different types of temperature fields; the KNN peak-type classifier is employed for the joint use of multiple dictionaries; the orthogonal matching pursuit (OMP) algorithm is used to obtain the sparse representation of sound slowness signal in the sparse domain; then, the temperature distribution is obtained by using the relationship between sound slowness and temperature. Simulation and actual temperature distribution reconstruction experiments show that the MDL algorithm has smaller reconstruction errors and provides more accurate information about the temperature field, compared with the compressed sensing and improved orthogonal matching pursuit (CS-IMOMP) algorithm, which is an algorithm based on compressed sensing and improved orthogonal matching pursuit (in the CS-IMOMP, DFT dictionary is used), the least square algorithm (LSA) and the simultaneous iterative reconstruction technique (SIRT).

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Section: MDL Algorithmmentioning

confidence: 99%

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

Wei

Yan

Zhou

2022

Sensors

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“…A methodology commonly used to study a variety of chemical engineering systems [48], including stirred-tank systems [23,49,50], is electrical capacitance/resistance tomography (ECT/ERT). While ECT carries a notable advantage over PEPT in terms of the relatively low cost of its setup and operation, the spatial resolution offered thereby is significantly lower [51], even despite recent improvements [52,53], thus limiting its ability to precisely determine the fine motion of small particles, a matter whose importance will become abundantly clear in Sect. 3.1.…”

Section: Research Article 23 Other Approachesmentioning

confidence: 99%

Positron Emission Particle Tracking for Liquid‐Solid Mixing in Stirred Tanks

et al. 2020

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Mixing in stirred tanks is vital to a wide range of industrial processes, typically requiring solids to be fully suspended and evenly distributed. The quality of suspension and mixing is typically measured visually. Such measurements are thus subject to human interpretation and can only feasibly be conducted in transparent systems – an uncommon condition in industrial systems. Visual measurements of homogeneity must also infer full 3D distributions of particles solely from the observation of a limited section thereof. This work details methods by which a system's homogeneity and state of suspension may be measured and quantified with no human interpretation, which can extract information from opaque systems and consider full 3D data acquired throughout the interior of a given system.

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“…Based on the different electrical features of the object in the sensitivity field, the ET techniques could be divided into electromagnetic tomography (EMT), electrical capacitance tomography (ECT), electrical impedance tomography (EIT), and electrical resistance tomography (ERT). Based on the principle of electromagnetic induction, the EMT can reconstruct the distribution state of the magnetic permeability for the medium in the sensitivity field [1][2][3][4][5][6]; Based on the principle of capacitance sensitivity, the ECT can reconstruct the distribution state of the dielectric constant for the medium in the sensitivity field [7][8][9][10][11][12][13][14][15][16][17]; Based on the principle of impedance sensitivity, the EIT can reconstruct the distribution state of the complex admittance for the medium in the sensitivity field [18][19][20][21][22][23][24]; Based on the principle of resistance sensing, the ERT can reconstruct the distribution state of the dielectric resistivity/conductivity for the medium in the sensitivity field [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of a Finite Element Model for Electrical Resistance Tomography of Human Lungs

Xiao¹

2021

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To improve the reconstruction quality of electrical resistance tomography (ERT) images, this paper designs and optimizes the finite element model for human lungs. According to the computer tomography (CT) scan image on human chest, the entire simulation domain was divided into a region of lungs, heart, and spine, and a region of adipose tissue, and the boundary curve equations of each region were derived by the improved particle swarm optimization (PSO). Based on the prior knowledge, a structural model was established for human lungs; the ERT forward problem was solved by the finite element model based on grid reconstruction, and the calculated boundary voltage of sensitivity field was taken as the theoretical value. Next, two optimization goals were set up: improving the calculation accuracy of forward problem, and easing the ill-conditionedness of the sensitivity matrix; two variables were configured: the number of layers of the finite element model in each region, and the polar diameter ratio of the finite element nodes on each layer to the finite element nodes on the boundary of each region corresponding to the same polar angle. On this basis, the finite element model was optimized by the improved PSO to adapt to human lung ERT. Simulation results show that, under the same experimental conditions, the proposed finite element model could solve the forward problem more accurately, improve the ill-conditionedness of the sensitivity matrix and the Hessian matrix, and make the sensitivity distribution more uniformly, thereby enhancing the accuracy of image reconstruction.

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Electrical capacitance tomography image reconstruction by improved orthogonal matching pursuit algorithm

Cited by 12 publications

References 30 publications

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

Positron Emission Particle Tracking for Liquid‐Solid Mixing in Stirred Tanks

Design and Optimization of a Finite Element Model for Electrical Resistance Tomography of Human Lungs

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