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Aufsatz / Paper in Tagungsband (referiert)

Automated classification of a calf’s feeding state based on data collected by active sensors with 3D-accelerometer

Sturm V., Efrosinin D., Efrosinina N., Roland L., Iwersen M., Drillich M., Auer W.: Automated classification of a calf’s feeding state based on data collected by active sensors with 3D-accelerometer, in: Distributed Computer and Communication Networks, Series Communications in Computer and Information Science, Volume 700, Page(s) 120-134, Springer, 2017.

BibTeX

@INPROCEEDINGS{
title = {Automated classification of a calf’s feeding state based on data collected by active sensors with 3D-accelerometer},
type = {Aufsatz / Paper in Tagungsband (referiert)},
booktitle = {Distributed Computer and Communication Networks},
author = {Sturm, Valentin Gerhard and Efrosinin, Dmitry and Efrosinina, Natalia and Roland, Leonie and Iwersen, Michael and Drillich, Marc and Auer, Wolfgang},
language = {EN},
abstract = {The paper deals with the problem of time series classification for the feeding state of calves by means of features evaluated for acceleration real-time data sets. The eartags equipped with an active sensor were developed for location and animal activity identification. Video records synchronized with a sensor data were collected from three calves. After the data preprocessing including the reconstruction of lost information, filtering and frequency stabilization, new time series were used to develop a machine-learning algorithm with equidistant and non-equidistant time series segmentation method based on a modified Kolmogorov-Smirnov statistic. The proposed classification method has achieved a good recognition quality for the feeding state with a best overall accuracy of approximately 94%. Thus this methodology is useful in identifying the feeding state and we may expect the possibility to generalize it to the multi-state case as well. The further improvement of the algorithm is a subject of our future research.},
pages = {120-134},
publisher = {Springer},
address = {Berlin},
volume = {700},
series = {Communications in Computer and Information Science},
year = {2017},
}

Details

Zusammenfassung: The paper deals with the problem of time series classification for the feeding state of calves by means of features evaluated for acceleration real-time data sets. The eartags equipped with an active sensor were developed for location and animal activity identification. Video records synchronized with a sensor data were collected from three calves. After the data preprocessing including the reconstruction of lost information, filtering and frequency stabilization, new time series were used to develop a machine-learning algorithm with equidistant and non-equidistant time series segmentation method based on a modified Kolmogorov-Smirnov statistic. The proposed classification method has achieved a good recognition quality for the feeding state with a best overall accuracy of approximately 94%. Thus this methodology is useful in identifying the feeding state and we may expect the possibility to generalize it to the multi-state case as well. The further improvement of the algorithm is a subject of our future research.

Buchtitel: Distributed Computer and Communication Networks
Volume: 700
Erscheinungsjahr: 2017
Seitenreferenz: 120-134
Anzahl Seiten: 15
Verlag: Springer
Verlagsanschrift: Berlin
Serie: Communications in Computer and Information Science
Reichweite: International

Beteiligte

AutorInnen / HerausgeberInnen: DI Valentin Gerhard Sturm, Assoz.Univprof. Dr. Dmitry Efrosinin, Mag. Natalia Efrosinina, Leonie Roland, Dr. Michael Iwersen, Marc Drillich, Wolfgang Auer

Forschungseinheiten der JKU:

Wissenschaftszweige: 101 Mathematik | 101014 Numerische Mathematik | 101018 Statistik | 101019 Stochastik | 101024 Wahrscheinlichkeitstheorie

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