Lightweight Interleaved Residual Dense Network for Gas Identification of Industrial Polypropylene Coupled With an Electronic Nose

Shi, Yan; Yuan, Hangcheng; Zhang, Qinglun; Sun, Aixin; Liu, Jingjing; Men, Hong

doi:10.1109/tim.2021.3117377

Cited by 26 publications

(2 citation statements)

References 41 publications

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“…Cao et al proposed an improved cellular neural network to achieve feature extraction and recognition of different liquor gas information [22]. Shi et al proposed a lightweight interleaved residual dense network for efficient classification of industrial polypropylene gas [23]. Zhang et al proposed a channel attention CNN to identify the gas information of liquor [24].…”

Section: Jinst 17 P08016mentioning

confidence: 99%

A deep learning method combined with electronic nose to identify the rice origin

Yan

Liu

2022

J. Inst.

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The rice quality is directly affected by the natural environment in which it is grown. It is common to pass off poor quality as good quality rice sold in rice markets. Therefore, an effective rice quality detection method should be proposed. This work proposes a deep learning method combined with an electronic nose (e-nose) to realize the classification of rice origin, and then realize the detection of rice quality. Firstly, channel-spatial collaborative attention (CSCA) is proposed to adaptively focus on key features of gas information. Secondly, three shallow convolutional neural network (CNN) structures are introduced to achieve gas information classification. Finally, CSCA and three CNN structures are combined to construct a classification network for rice gas recognition. The results show that in the comparison of multi-structure models, LeNet5-CSCA has the best classification performance, with a classification accuracy of 97.70%, a precision of 97.45%, a recall of 97.67%, and a F1-score of 97.56%.

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Section: Jinst 17 P08016mentioning

confidence: 99%

A deep learning method combined with electronic nose to identify the rice origin

Yan

Liu

2022

J. Inst.

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“…The classification accuracy achieved by IGRCCN is as high as 98.33%, surpassing the performance of other deep learning models . Shi et al introduced a lightweight interleaved residual dense network (LIRD), which was combined with an electronic nose to achieve intelligent recognition of gas information in polypropylene materials, achieving a classification accuracy of over 99.00% . Existing studies have established a robust correlation between pattern recognition algorithms and the performance of electronic noses, and this relationship has been extensively investigated.…”

mentioning

confidence: 99%

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture

Yang,

Hu,

Feng

et al. 2024

ACS Sens.

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The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long shortterm memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO 2 , NO 2 , and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO 2 gas, which is a noteworthy achievement. The determination coefficient (R 2 ) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

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Performance evaluation of lightweight pattern recognition algorithms for portable environmental monitoring electronic noses

Wang,

Li,

Yang

et al. 2025

Building and Environment

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Lightweight Interleaved Residual Dense Network for Gas Identification of Industrial Polypropylene Coupled With an Electronic Nose

Cited by 26 publications

References 41 publications

A deep learning method combined with electronic nose to identify the rice origin

A deep learning method combined with electronic nose to identify the rice origin

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture

Performance evaluation of lightweight pattern recognition algorithms for portable environmental monitoring electronic noses

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