Proceedings Article10.1109/ISOEN.2017.7968887
A CNN-based simplified data processing method for electronic noses
Pei-Feng Qi,Qing-Hao Meng,Ming Zeng +2 more
- 01 May 2017
- pp 1-3
38
TL;DR: A simplified method based on convolutional neural network (CNN) for e-noses that not only uses fewer sampling points to perform classification, but also can automatically implement feature generation without signal pre-processing step which significantly improves detection efficiency and simplifies data processing procedures of e-Noses.
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Abstract: Traditional data processing methods for electronic noses (e-noses) need to use the whole response curves (including rise, steady and recovery phases) of sensor array, which leads to a long sampling time. The traditional methods also perform many steps such as signal pre-processing, feature generation/reduction, and classification, which increase the difficulty of selecting a suitable method for each step. In view of the above problems, we present a simplified method based on convolutional neural network (CNN) for e-noses. CNN not only uses fewer sampling points to perform classification, but also can automatically implement feature generation without signal pre-processing step. This significantly improves detection efficiency and simplifies data processing procedures of e-noses. The performance of CNN was tested using a portable e-nose designed by us. The results showed that CNN not only took shorter sampling time (15 seconds), but also obtained higher classification accuracy (95.7%) than traditional methods (92.9%).
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Citations
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TL;DR: This work shows the distinct advantages of combining “big data” and “deep learning” in the gas-sensing field and further proves that the employment of MTL-CNN can significantly improve the training and application efficiency of the E-nose.
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44
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