Capsule Neural Networks utilize capsules, which bind neurons into a single
vector and learn position equivariant features, which makes them more robust
than original Convolutional Neural Networks. CapsNets employ an affine
transformation matrix and dynamic routing with coupling coefficients to learn
robustly. In this paper, we investigate the effectiveness of CapsNets in
analyzing highly sensitive and noisy time series sensor data. To demonstrate
CapsNets robustness, we compare their performance with original CNNs on
electrocardiogram data, a medical time series sensor data with complex patterns
and noise. Our study provides empirical evidence that CapsNets function as
noise stabilizers, as investigated by manual and adversarial attack experiments
using the fast gradient sign method and three manual attacks, including offset
shifting, gradual drift, and temporal lagging. In summary, CapsNets outperform
CNNs in both manual and adversarial attacked data. Our findings suggest that
CapsNets can be effectively applied to various sensor systems to improve their
resilience to noise attacks. These results have significant implications for
designing and implementing robust machine learning models in real world
applications. Additionally, this study contributes to the effectiveness of
CapsNet models in handling noisy data and highlights their potential for
addressing the challenges of noise data in time series analysis.

CapsNets 在分析高敏感、噪音时间序列传感器数据方面具有良好的鲁棒性，通过与原始 CNNs 在心电图数据上的性能比较，证明了其噪音稳定化的功能和優越性。该研究结果对设计和实施鲁棒的机器学习模型在实际应用中具有重要意义，并强调了 CapsNet 模型在处理噪音数据方面的潜力和对时序分析中的噪音数据挑战的应对能力。