Optimised data synchronisation methods for continuous force calibration

This paper explores optimised data synchronisation methods, particularly automated techniques, for continuous force calibration developed within the ComTraForce project. Through rigorous experimentation and analysis, the study demonstrates the efficacy of resampling and synchronisation methodologies in enhancing calibration precision. Results indicate significant improvements in accuracy and reliability, particularly in the context of continuous force calibration applications. Additionally, uncertainty analysis approaches are discussed to provide insights into synchronisation error assessment. The findings underscore the importance of advanced synchronisation methods in achieving reliable calibration outcomes in continuous force testing environments.

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Publication Details

Journal
Measurement Sensors
Published
2024-12-20
DOI
https://doi.org/10.1016/j.measen.2024.101341
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.35
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article

Optimised data synchronisation methods for continuous force calibration

Rafael Soares de Oliveira, Falk Tegtmeier, Andy Knott, Jonas Sander
1 citations
Measurement Sensors
Neural Networks and Applications
0.35
article

Optimised data synchronisation methods for continuous force calibration

Rafael Soares de Oliveira, Falk Tegtmeier, Andy Knott, Jonas Sander
article en
1 citations

Abstract

This paper explores optimised data synchronisation methods, particularly automated techniques, for continuous force calibration developed within the ComTraForce project. Through rigorous experimentation and analysis, the study demonstrates the efficacy of resampling and synchronisation methodologies in enhancing calibration precision. Results indicate significant improvements in accuracy and reliability, particularly in the context of continuous force calibration applications. Additionally, uncertainty analysis approaches are discussed to provide insights into synchronisation error assessment. The findings underscore the importance of advanced synchronisation methods in achieving reliable calibration outcomes in continuous force testing environments.

Measurement SensorsVol. 38
Physikalisch-Technische Bundesanstalt (DE), National Physical Laboratory (GB), Instituto Nacional de Metrologia, Qualidade e Tecnologia (BR)
Openalex Percentile: Top 30%
Neural Networks and Applications
0.35
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Optimised data synchronisation methods for continuous force calibration — Rafael Soares de Oliveira, Falk Tegtmeier, et al. · Measurement Sensors (2024) | TGRS Research Map | TGRS