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Volume 7, issue 2 | Copyright
J. Sens. Sens. Syst., 7, 489-506, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Regular research article 20 Sep 2018

Regular research article | 20 Sep 2018

DAV3E – a MATLAB toolbox for multivariate sensor data evaluation

Manuel Bastuck1,2, Tobias Baur1, and Andreas Schütze1 Manuel Bastuck et al.
  • 1Lab for Measurement Technology, Saarland University, 66123 Saarbrücken, Germany
  • 2Applied Sensor Science, SAS, IFM, Linköping University, 58183 Linköping, Sweden

Abstract. We present DAV3E, a MATLAB toolbox for feature extraction from, and evaluation of, cyclic sensor data. These kind of data arise from many real-world applications like gas sensors in temperature cycled operation or condition monitoring of hydraulic machines. DAV3E enables interactive shape-describing feature extraction from such datasets, which is lacking in current machine learning tools, with subsequent methods to build validated statistical models for the prediction of unknown data. It also provides more sophisticated methods like model hierarchies, exhaustive parameter search, and automatic data fusion, which can all be accessed in the same graphical user interface for a streamlined and efficient workflow, or via command line for more advanced users. New features and visualization methods can be added with minimal MATLAB knowledge through the plug-in system. We describe ideas and concepts implemented in the software, as well as the currently existing modules, and demonstrate its capabilities for one synthetic and two real datasets. An executable version of DAV3E can be found at (last access: 14 September 2018). The source code is available on request.

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Short summary
Predictions about systems too complex for physical modeling can be made nowadays with data-based models. Our software DAV³E is an easy way to extract relevant features from cyclic raw data, a process often neglected in other software packages, based on mathematical methods, incomplete physical models, or human intuition. Its graphical user interface further provides methods to fuse data from many sensors, to teach a model the prediction of new data, and to check the model’s performance.
Predictions about systems too complex for physical modeling can be made nowadays with data-based...