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Found 5 papers, 1 papers with code
Falls Prediction in eldery people using Gated Recurrent Units
Falls prevention, especially in older people, becomes an increasingly important topic in the times of aging societies.
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection.
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets.
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance.
A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework
This article presents a development of web application for quench prediction in \gls{te-mpe-ee} at CERN.
