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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...
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| Опубликовано в: : | J Digit Imaging |
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| Главные авторы: | , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Springer International Publishing
2021
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7887121/ https://ncbi.nlm.nih.gov/pubmed/33409816 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s10278-020-00411-4 |
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