@inproceedings{ReginyaNikolaenkoVoronovetal.2018,
  author    = {Sergey Reginya and Vladislav Nikolaenko and Roman Voronov and Alexei Soloviev and Axel Sikora and Alex Moschevikin},
  title     = {MEMS Sensors Bias Thermal Profiles Classification Using Machine Learning},
  series = {Proceedings of the First International Workshop on Stochastic Modeling and Applied Research of Technology},
  volume    = {CEUR workshop proceedings 2278},
  editor    = {Evsey Morozov and Alexander Rumyantsev},
  publisher = {RWTH},
  address   = {Aachen},
  issn      = {1613-0073},
  url       = {https://nbn-resolving.org/urn:nbn:de:0074-2278-2},
  pages     = {17 -- 28},
  year      = {2018},
  abstract  = {The paper describes the methodology and experimental results for revealing similarities in thermal dependencies of biases of accelerometers and gyroscopes from 250 inertial MEMS chips (MPU-9250). Temperature profiles were measured on an experimental setup with a Peltier element for temperature control. Classification of temperature curves was carried out with machine learning approach. A perfect sensor should not have thermal dependency at all. Thus, only sensors inside the clusters with smaller dependency (smaller total temperature slopes) might be pre-selected for production of high accuracy inertial navigation modules. It was found that no unified thermal profile (“family” curve) exists for all sensors in a production batch. However, obviously, sensors might be grouped according to their parameters. Therefore, the temperature compensation profiles might be regressed for each group. 12 slope coefficients on 5 degrees temperature intervals from 0°C to +60°C were used as the features for the k-means++ clustering algorithm. The minimum number of clusters for all sensors to be well separated from each other by bias thermal profiles in our case is 6. It was found by applying the elbow method. For each cluster a regression curve can be obtained.},
  language  = {en}
}