Application of Bivariate Reproducing Kernel-Based Best Interpolation Method in Electrical Tomography.

Yongguang Tan, Jingqi Wang, Junqi Yu, Boqi Wu, Jinchao Shen, Xiangchen Guo

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yongguang TanSchool of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Jingqi WangSchool of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Junqi YuSchool of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Boqi WuKey Laboratory of Architectural Cold Climate Energy Management, Jilin Jianzhu University, Ministry of Education, Changchun 132600, China.

Jinchao ShenSchool of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Xiangchen GuoSchool of Building Services Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Funding

Key Laboratory of Architectural Cold Climate Energy Management, Ministry of Education JLJZHDKF022023005

6 · The paper itself

Abstract

Electrical Tomography (ET) technology is widely used in multiphase flow detection due to its advantages of low cost, visualization, fast response, non-radiation, and non-invasiveness. However, ill-posed solutions lead to low image reconstruction resolution, which limits its practical engineering applications. Although existing interpolation approximation algorithms can alleviate the effects of the ill-posed solutions to some extent, the imaging results remain suboptimal due to the limited approximation capability of these methods. This paper proposes a Bivariate Reproducing Kernel-Based Best Interpolation (BRKBI) method, which offers smaller approximation errors and clearer image reconstruction quality compared to existing methods. The effectiveness of the BRKBI method is validated through theoretical analysis and experimental comparisons.

Indexed as

bivariate reproducing kernelelectrical tomographyinterpolation approximationspatial resolution

Identifiers

PMID39598943

DOI10.3390/s24227165

PMCPMC11598363

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.