Uncertainty-aware data pipeline of calibrated MEMS sensors used for machine learning

Author:

Dorst TanjaORCID,Gruber Maximilian,Seeger Benedikt,Vedurmudi Anupam Prasad,Schneider Tizian,Eichstädt Sascha,Schütze Andreas

Funder

EURAMET European Metrology Programme for Innovation and Research

Universität des Saarlandes

European Metrology Programme for Innovation and Research

Horizon 2020

Deutsche Forschungsgemeinschaft

Bundesministerium für Bildung und Forschung

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanics of Materials,Electronic, Optical and Magnetic Materials

Reference27 articles.

1. Influence of synchronization within a sensor network on machine learning results;Dorst;J. Sens. Sens. Syst.,2021

2. Guide to the Expression of Uncertainty in Measurement;BIPM,2008

3. International Vocabulary of Metrology - Basic and General Concepts and Associated Terms;BIPM,2012

4. Zustandsbewertung industrieller Prozesse mittels multivariater Sensordatenanalyse am Beispiel hydraulischer und elektromechanischer Antriebssysteme;Helwig,2018

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1. Integrating metrological principles into the Internet of Things: a digital maturity model for sensor network metrology;tm - Technisches Messen;2023-10-16

2. Metrology for next generation “Phygital Sensors”;2023 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT);2023-06-06

3. A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0;Sustainability;2022-12-13

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