文献类型：期刊文献

英文题名：Task Enhancement and Global-Local Information Fusion for Few-Shot Gas Recognition

作者：Tian, Heng[1]; Wu, Zhihao[1]; Yang, Hai[1]; Wang, Zhe[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education Department of Computer Science and Engineering, Shanghai, China

年份：2025

外文期刊名：Proceedings of the International Joint Conference on Neural Networks

收录：EI(收录号：20260620021418)

语种：英文

外文关键词：Classification (of information) - Data mining - Extraction - Gas detectors - Information fusion - Information retrieval - Learning algorithms - Learning systems

摘要：Gas recognition under few-shot conditions presents several challenges, primarily due to sensor selectivity and the complexities of data collection. While meta-learning techniques have shown promise in improving adaptability to various gas types, sensor configurations, and environmental conditions with limited data, limitations remain. To address these issues, researchers employ diverse sensor arrays, supported by neural networks that capture complex data dependencies. However, gas recognition in few-shot scenarios is still in its early stages, highlighting the need for further research. In this paper, we propose an innovative meta-learning-based framework based on task enhancement and global-local information fusion for few-shot gas classification. Firstly, to enhance data utilization and generalization capabilities, we propose the Global and Local Information Fusion (GLIF). Specifically, GLIF has two modules: the Global Information Extraction Module (GIEM) and the Local Information Extraction Module (LIEM). GIEM captures long-range dependencies in the gas signal, while LIEM focuses on detailed local features using convolutional layers, reducing model complexity and the risk of overfitting. Secondly, to improve performance in challenging tasks, we design a Special Task Enhancement Module (STEM). Concretely, STEM identifies the most challenging categories, aggregates tasks containing these categories, and resamples them for focused retraining based on a ranking-based strategy. Experimental results validate the effectiveness of our proposed method, demonstrating high recognition accuracy and robust generalization ability. These traits are particularly evident in scenarios involving gas drift and limited samples, highlighting the potential value and impact of our approach in real-world applications. ? 2025 IEEE.