Predicting Loss of Communication Between Radio Enabled Devices Using Deep Recurrent Neural Networks
Sammanfattning: This thesis investigates the effectiveness of applying recurrent neural networks (RNN) to detect communication errors between radio devices, also known as supervision violations, on imbalanced data. The task is to classify whether a supervision violation is to occur within seven days. The available data is in the form of radio packets, which are being re-sampled and pre-processed such that they can be interpreted by RNNs. The RNNs are trained as both classifiers and generators to enable detection of supervision violations. Using RNNs, an extensive evaluation is made into different pre-processing methods, using multiple test sets and network architectures, applying average precision as metric and precision-recall curves as the main evaluation technique. The results show that it is possible to achieve an average precision of 0.75, and that experimentation with pre-processing parameters along with multiple testsets are needed to ensure a generalised model.
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