SAW Sensor Array Data Fusion for Chemical Class Recognition of Volatile Organic Compounds
SAW Sensor Array Data Fusion for Chemical Class Recognition of Volatile Organic Compounds
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发表时间：
2013
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通讯作者：
S. K. Jha;K. Hayashi
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作者：
S. K. Jha;K. Hayashi
针对基于化学传感器阵列的电子鼻系统，研究了基于数据融合的人工智能单元的开发。重点研究了模型声表面波(SAW)传感器阵列响应的特征级融合，用于挥发性有机化合物(VOCs)的化学分类识别。特征提取采用三种方法：主成分分析(PCA)、独立成分分析(ICA)和核主成分分析(KPCA)。融合后的特征由三种无监督融合方案生成，并结合支持向量机分类器进行验证。通过对12个模型声表面波传感器阵列数据的分析，得出了研究结论。结果表明，在三种特征融合方案中，基于求和结果的特征融合对VOCs的分类识别率最高。
Present study deals the development of data fusion based artificial intelligence unit for the chemical sensor array based electronic nose (E-Nose) system. We focus particularly on feature level fusion of model surface acoustic wave (SAW) sensor array response for chemical class identification of volatile organic compounds (VOCs). Three methods are used for feature extraction namely: principal component analysis (PCA); independent component analysis (ICA) and kernel principal component analysis (KPCA). Fused features are generated with three unsupervised fusion schemes and validated in combination with support vector machine (SVM) classifier. Study is concluded by the analysis of 12 model SAW sensor array data sets. It suggests that amongst the three feature fusion schemes; feature fusion by summation result highest class recognition rate of VOCs.