School of Industrial and Management Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.
School of Industrial and Management Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.
Neural Netw. 2022 Jun;150:68-86. doi: 10.1016/j.neunet.2022.02.019. Epub 2022 Mar 4.
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.
多通道信号数据分析在各种工业应用中至关重要，例如人类活动识别、车辆故障预测和制造设备监控。最近，由于深度神经网络能够自动从复杂的多通道信号中提取有用特征，因此在多通道信号数据中得到了应用。然而，深度神经网络是黑盒模型，其内部工作机制无法以人类易于理解的形式呈现。为了解决这个问题，我们提出了一种不确定性感知的层次分段-通道注意力模型，该模型由时间分段和通道级注意力组成。分层注意力机制使神经网络能够识别对预测至关重要的重要时间片段和通道，从而使模型具有可解释性。此外，该模型使用变分推断来提供不确定性信息，生成易于解释的置信区间。我们在模拟和真实数据集上进行了实验，以证明我们的方法的有用性和适用性。结果证实，我们的方法可以关注重要的时间片段和传感器，同时实现更好的分类性能。
