Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction
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 co...
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doaj-5dc82eb1b96d4438bc2aa751537bfa092020-12-22T00:03:41ZengMDPI AGElectronics2079-92922020-12-0192205220510.3390/electronics9122205Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality ReductionYanan Zou0Jianhui Lv1School of Science, Jilin Institute of Chemical Technology, Jilin 132022, ChinaInternational Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaElectronic 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.https://www.mdpi.com/2079-9292/9/12/2205electronic noserecurrent neural networkdimensionality reductionlocally linear embedding |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanan Zou Jianhui Lv |
spellingShingle |
Yanan Zou Jianhui Lv Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction Electronics electronic nose recurrent neural network dimensionality reduction locally linear embedding |
author_facet |
Yanan Zou Jianhui Lv |
author_sort |
Yanan Zou |
title |
Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction |
title_short |
Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction |
title_full |
Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction |
title_fullStr |
Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction |
title_full_unstemmed |
Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction |
title_sort |
using recurrent neural network to optimize electronic nose system with dimensionality reduction |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2020-12-01 |
description |
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. |
topic |
electronic nose recurrent neural network dimensionality reduction locally linear embedding |
url |
https://www.mdpi.com/2079-9292/9/12/2205 |
work_keys_str_mv |
AT yananzou usingrecurrentneuralnetworktooptimizeelectronicnosesystemwithdimensionalityreduction AT jianhuilv usingrecurrentneuralnetworktooptimizeelectronicnosesystemwithdimensionalityreduction |
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1724374570327080960 |