Abstract
This article concerns identifying objects generating signals from various sensors. Instead of using traditional hand-made time series features we feed the signals as input channels to a convolutional neural network. The network learned low- and high-level features from data. We describe the process of data preparation, filtering, and the structure of the convolutional network. Experiment results showed that the network was able to learn to recognize objects with high accuracy.
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Zȩbik, M., Korytkowski, M., Angryk, R., Scherer, R. (2017). Convolutional Neural Networks for Time Series Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_57
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DOI: https://doi.org/10.1007/978-3-319-59060-8_57
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