School of Integrated Circuits, Tsinghua University, Beijing 100084, China.
Suzhou Huiwen Nanotechnology Co., Ltd., Suzhou 215004, China.
Sensors (Basel). 2023 Feb 22;23(5):2433. doi: 10.3390/s23052433.
The sensitivity and selectivity profiles of gas sensors are always changed by sensor drifting, sensor aging, and the surroundings (e.g., temperature and humidity changes), which lead to a serious decline in gas recognition accuracy or even invalidation. To address this issue, the practical solution is to retrain the network to maintain performance, leveraging its rapid, incremental online learning capacity. In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-incremental learning, and can be retrained quickly with a new gas at a low accuracy cost. Compared with gas recognition approaches such as support vector machine (SVM), k-nearest neighbor (KNN), principal component analysis (PCA) +SVM, PCA+KNN, and artificial neural network (ANN), our network achieves the highest accuracy of 98.75% in five-fold cross-validation for identifying nine types of gases, each with five different concentrations. In particular, the proposed network has a 5.09% higher accuracy than that of other gas recognition algorithms, which validates its robustness and effectiveness for real-life fire scenarios.
气体传感器的灵敏度和选择性特性曲线总是会受到传感器漂移、传感器老化以及环境因素（例如温度和湿度变化）的影响，这会导致气体识别精度严重下降，甚至导致失效。为了解决这个问题，实际的解决方案是通过利用其快速、增量式在线学习能力来重新训练网络以保持性能。在本文中，我们开发了一种基于生物启发的尖峰神经网络（SNN）来识别九种易燃和有毒气体，它支持小样本类增量学习，并且可以以较低的精度成本快速重新训练新气体。与气体识别方法（如支持向量机（SVM）、k-最近邻（KNN）、主成分分析（PCA）+SVM、PCA+KNN 和人工神经网络（ANN））相比，我们的网络在五折交叉验证中实现了 98.75%的最高识别九种气体（每种气体有五个不同浓度）的准确率。特别是，所提出的网络比其他气体识别算法的准确率高 5.09%，这验证了它在实际火灾场景中的鲁棒性和有效性。
