most citedDeep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data

4 citations · 4 across the 1 of their papers we have counts for

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1 paper

cs.LG2022★ 4 cited

Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data

Anthony Geglio, Eisa Hedayati, Mark Tascillo +3

This work investigates a practical and novel method for automated unsupervised fault detection in vehicles using a fully convolutional autoencoder. The results demonstrate the algo…