AI Article Synopsis
- - Electrical tomography sensors are essential for detecting and estimating parameters in pipelines, but they require calibration with sufficient labeled data to be effective.
- - The complexity of real-world environments often leads to inaccuracies in sensor calibration due to uncertainties or invalid labeling data, making it difficult to achieve reliable results.
- - The paper introduces a semi-supervised fuzzy clustering algorithm that uses accurate partial data to correct errors in labeled data, demonstrating improved accuracy and stability through experiments on a dredger.
Article Abstract
Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11125234 | PMC |
| http://dx.doi.org/10.3390/s24103068 | DOI Listing |
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