一种混合气体成分识别和度估计方法,用于不平衡气体传感器阵列样品
Yuheng Lin1, Jinlong Shi1,2, Wanyu Xia1
1Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究介绍了样本扩张方法,以提高气体检测的准确性. 这些技术提高了混合气体的识别和度估计,特别是当数据有限或不平衡时.
科学领域:
- 分析化学 分析化学
- 机器学习 机器学习
- 环境科学 环境科学
背景情况:
- 准确的气体检测对于环境监测和安全至关重要.
- 传统方法在不平衡的数据集和有限的样本上扎,降低了准确性.
- 需要新的方法来解决气体混合物分析中的这些局限性.
研究的目的:
- 开发和验证样本扩张方法,以改善气体混合物成分识别和度估计.
- 提高气体检测系统处理不平衡和不足数据的准确性.
- 为气体混合物的定性和定量分析提供强大的解决方案.
主要方法:
- 拟议的ADASYN-ELM方法用于定性分析:用于特征提取的内核主要组件分析 (KPCA),用于样本扩展的ADASYN,以及用于极端学习机器 (ELM) 参数优化的粒子群优化 (PSO) 和遗传算法 (GA).
- 拟议的S-SMOTE-MLSSVR方法用于定量分析:SMOTE (合成少数人过量采样技术) 变体 (S-SMOTE) 用于样本扩展,以及PSO/GA用于多个内核学习支持向量回归 (MLSSVR) 参数优化.
- 利用样本扩展技术来应对气体传感中不平衡和有限的数据集所带来的挑战.
主要成果:
- 样本扩展显著提高了分类和度估计的准确率.
- 在应用样本扩展技术后,平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE) 减少了.
- 提出的方法对气体混合物分析的性能产生了积极的影响,特别是在具有挑战性的数据集的情况下.
结论:
- 样本扩展是提高气体检测系统准确性的有效策略.
- 在ADASYN-ELM和S-SMOTE-MLSSVR方法提供了强大的解决方案,用于气体混合物的定性和定量分析.
- 这些发现对于提高气体传感技术在现实应用中的可靠性具有重要意义.
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