首页|Uncertainty-aware hierarchical segment-channel attention mechanism for reliable and interpretable multichannel signal classification
Uncertainty-aware hierarchical segment-channel attention mechanism for reliable and interpretable multichannel signal classification
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
Multichannel signal data analysis has been crucial in various industrial applications, such as human activity recognition, vehicle failure predictions, and manufacturing equipment monitoring. Recently, deep neural networks have come into use for multichannel signal data because of their ability to automatically extract useful features from complex multichannel signals. However, deep neural networks are black-box models whose internal working mechanisms cannot be put in a form readily understood by humans. To address this issue, we have proposed an uncertainty-aware hierarchical segment-channel attention model that consists of a time segment and channel level attentions. The hierarchical attention mechanism enables a neural network to identify important time segments and channels critical for prediction, making the model explainable. In addition, the model uses variational inferences to provide uncertainty information that yields a confidence interval that can be easily explained. We conducted experiments on simulated and real-world datasets to demonstrate the usefulness and applicability of our method. The results confirm that our method can attend to important time segments and sensors while achieving better classification performance. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.
Explainable neural networkAttention mechanismBayesian neural networkMultichannel signalMultivariate time seriesCONVOLUTIONAL NEURAL-NETWORKTIME
NSTL
Elsevier
