Zeng Xianzhang, Shahzeb Muhammad, Cheng Xin, Shen Qiang, Xiao Hongyang, Xia Cao, Xia Yuanlin, Huang Yubo, Xu Jingfei, Wang Zhuqing
School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
Graduate School of International Cultural Studies, Tohoku University, Kawauchi 41, Aoba Ku, Sendai 980-8577, Miyagi, Japan.
Micromachines (Basel). 2024 Dec 16;15(12):1501. doi: 10.3390/mi15121501.
This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin-carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key techniques include Principal Component Analysis (PCA) for dimensionality reduction, the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation, and the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms for classification. PCA improved KNN and SVM classification, boosting the Area Under the Curve (AUC) scores by 15.7% and 25.2%, respectively. SMOTE increased KNN's accuracy by 2.1%, preserving data structure better than polynomial fitting. The results demonstrate a scalable approach to enhancing classification accuracy under data constraints. This approach shows promise for expanding gas sensor applicability in fields where data limitations previously restricted reliability and effectiveness.
本研究利用明胶 - 炭黑（CB - GE）复合薄膜传感器应对多维小气体传感器数据分类的挑战，在区分气体类型（乙醇、丙酮和空气）方面实现了91.7%的准确率。关键技术包括用于降维的主成分分析（PCA）、用于数据增强的合成少数类过采样技术（SMOTE）以及用于分类的支持向量机（SVM）和K近邻（KNN）算法。PCA改进了KNN和SVM分类，分别将曲线下面积（AUC）分数提高了15.7%和25.2%。SMOTE将KNN的准确率提高了2.1%，比多项式拟合更好地保留了数据结构。结果表明了一种在数据约束下提高分类准确率的可扩展方法。这种方法有望扩大气体传感器在以前数据限制阻碍可靠性和有效性的领域中的适用性。
