TY  - JOUR
A1  - Schmitt, Robert
A1  - Scholl, Ingrid
A1  - Cai, Yu
A1  - Xia, Ji
A1  - Dziwoki, Paul
A1  - Harding, Martin
A1  - Pavim, Alberto
T1  - Machine Vision System for Inline Inspection in Carbide Insert Production
JF  - Bildverarbeitung  für die Medizin 2010 : Algorithmen, Systeme, Anwendungen ; Proceedings des Workshops vom 14. bis 16. März in Aachen / Thomas M. Deserno ... (Hrsg.)
Y1  - 2010
SN  - 978-3-642-11967-5
N1  - Proceedings of the 36th International MATADOR Conference ; Workshop Bildverarbeitung  für die Medizin <2010, Aachen>
SP  - 339
EP  - 342
PB  - Springer
CY  - Berlin
ER  - 
TY  - JOUR
A1  - Marx, Ulrich
A1  - Schenk, Friedrich
A1  - Behrens, Jan
A1  - Meyr, Ulrike
A1  - Wanek, Paul
A1  - Zang, Werner
A1  - Schmitt, Robert
A1  - Brüstle, Oliver
A1  - Zenke, Martin
A1  - Klocke, Fritz
T1  - Automatic production of induced pluripotent stem cells
JF  - Procedia CIRP : First CIRP Conference on BioManufacturing
Y1  - 2013
SN  - 2212-8271
VL  - Vol. 5
SP  - 2
EP  - 6
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Mueller, Tobias
A1  - Segin, Alexander
A1  - Weigand, Christoph
A1  - Schmitt, Robert H.
T1  - Feature selection for measurement models
JF  - International journal of quality & reliability management
N2  - Purpose
In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.

Design/methodology/approach
In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.

Findings
Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.

Originality/value
For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.
KW  - Feature selection
KW  - Modelling
KW  - Measurement models
KW  - Measurement uncertainty
Y1  - 2022
U6  - https://doi.org/10.1108/IJQRM-07-2021-0245
SN  - 0265-671X
IS  - Vol. ahead-of-print, No. ahead-of-print.
PB  - Emerald Group Publishing Limited
CY  - Bingley
ER  -