Optimization of Extracted Features from an Explosive-Detecting Electronic Nose Using Genetic Algorithm

Española, Jason L.; Vicerra, Ryan Rhay P.; Dadios, Elmer P.

doi:10.1109/cis-ram47153.2019.9095825

Cited by 4 publications

(1 citation statement)

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“…Furthermore, electronic nose also has more advanced applications. For example, in [29], the authors made good optimization of extracted features for an explosive-detecting electronic nose by using GA. In [30], tofu shelf life was monitored by using electronic nose based on curve fitting method.…”

Section: The Related Applicationsmentioning

confidence: 99%

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Zou

2020

Electronics

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Electronic nose is an electronic olfactory system that simulates the biological olfactory mechanism, which mainly includes gas sensor, data pre-processing, and pattern recognition. In recent years, the proposals of electronic nose have been widely developed, which proves that electronic nose is a considerably important tool. However, the most recent studies concentrate on the applications of electronic nose, which gradually neglects the inherent technique improvement of electronic nose. Although there are some proposals on the technique improvement, they usually pay attention to the modification of gas sensor module and barely consider the improvement of the last two modules. Therefore, this paper optimizes the electronic nose system from the perspective of data pre-processing and pattern recognition. Recurrent neural network (RNN) is used to do pattern recognition and guarantee accuracy rate and stability. Regarding the high-dimensional data pre-processing, the method of locally linear embedding (LLE) is used to do dimensionality reduction. The experiments are made based on the real sensor drift dataset, and the results show that the proposed optimization mechanism not only has higher accuracy rate and stability, but also has lower response time than the three baselines. In addition, regarding the usage of RNN model, the experimental results also show its efficiency in terms of recall ratio, precision ratio, and F1 value.

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Section: The Related Applicationsmentioning

confidence: 99%