用图结构提升传感器阵列对混合气体的识别与浓度估计能力
Graph-Driven Models for Gas Mixture Identification and Concentration Estimation on Heterogeneous Sensor Array Signals
- 构建图注意力与胶囊网络，融合时序图结构增强特征提取
- 分类准确率超98.00%，浓度估计R2值超过0.96
- 适合工业级气体检测场景，尤其适用于异构数据集
准确识别气体混合物并估算其浓度在工业应用中至关重要。然而，现有模型在异构数据集上泛化能力差，限制了可扩展性与实用性。为此，本文提出两种新型深度学习模型：基于动态路由的图增强胶囊网络（GraphCapsNet）用于气体混合物分类，以及基于自注意力机制的图增强注意力网络（GraphANet）用于浓度估计。两个模型在加州大学欧文分校（UCI）机器学习库和自建数据集上验证，表现优于近期模型。分类任务中，GraphCapsNet在多个数据集上准确率超过98.00%；浓度估计中，GraphANet在多种气体组分上R2得分超过0.96。两者均表现出更高的精度与稳定性，为工业级气体分析提供了可扩展解决方案。
原文摘要 · Abstract (English)
Accurately identifying gas mixtures and estimating their concentrations are crucial across various industrial applications using gas sensor arrays. However, existing models face challenges in generalizing across heterogeneous datasets, which limits their scalability and practical applicability. To address this problem, this study develops two novel deep-learning models that integrate temporal graph structures for enhanced performance: a Graph-Enhanced Capsule Network (GraphCapsNet) employing dynamic routing for gas mixture classification and a Graph-Enhanced Attention Network (GraphANet) leveraging self-attention for concentration estimation. Both models were validated on datasets from the University of California, Irvine (UCI) Machine Learning Repository and a custom dataset, demonstrating superior performance in gas mixture identification and concentration estimation compared to recent models. In classification tasks, GraphCapsNet achieved over 98.00% accuracy across multiple datasets, while in concentration estimation, GraphANet attained an R2 score exceeding 0.96 across various gas components. Both GraphCapsNet and GraphANet exhibited significantly higher accuracy and stability, positioning them as promising solutions for scalable gas analysis in industrial settings.
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