Abstract
paper presents fusion based pattern recognition approach of feature scores for chemical class identification of volatile organic compounds (VOCs) by response analysis of model surface acoustic wave (SAW) sensor array. Diverse features are generated by analysis of sensor array response using three feature extraction methods: principal component analysis (PCA), independent component analysis (ICA) and kernel principal component analysis (KPCA). Thereafter feature vectors are fused with three straightforward fusion strategies including i) summation, ii) multiplication and iii) combination of feature vectors. Chemical class recognition efficiency of fused feature is experimentally verified by feeding them to the input of support vector machine (SVM) classifier. Experimental outcomes are based on analysis of 12 data sets generated with SAW sensor model simulation, containing different intensity of noise and outliers. It has been observed that in research of three fusion schemes; fusion by summation of feature vectors achieves persistently highest correct chemical class recognition rate (average 90%) of VOCs followed by combination and multiplication. Though in case of less noisy SAW sensor array response, fusion by combination of feature vectors results comparable class recognition efficiency to that of fusion by summation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.