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Hardware Failure Prediction on Imbalanced Times Series Data: Generation of Artificial Data Using Gaussian Process and Applying LSTMFCN to Predict Broken Hardware
Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represe...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Springer International Publishing
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887121/ https://www.ncbi.nlm.nih.gov/pubmed/33409816 http://dx.doi.org/10.1007/s10278-020-00411-4 |