Data completion for improved OD-estimation

Matschke, Irina; Heinig, K.; Friedrich, Bernhard

doi:10.1049/cp:20040006

Cited by 1 publication

(1 citation statement)

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“…While the literature is rich in applications of this kind of algorithms for underwater vision and sonar [8–10], it is quite scarce when it comes to other underwater parameters [11–15]. While many AI techniques and algorithms are available and documented for this kind of data processing [16–20], ANN are a very popular method, with many easy to find implementations, and what is more important, easy to operate at a high level (even with user-friendly graphical interfaces) by a non-expert in the field.…”

Section: Introductionmentioning

confidence: 99%

Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors

Baladrón

Aguiar

Calavia

et al. 2012

Sensors

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This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are difficult and costly. Therefore, completion and prediction of the missing data can greatly improve the quality of the underwater datasets. A performance study using real data is presented to validate the approach, concluding that the proposed architecture is able to provide very low errors. The numbers show as well that the solution is especially suitable for cases where large portions of data are missing, while in situations where the missing values are isolated the improvement over other simple interpolation methods is limited.

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