An electronic nose for CO concentration prediction based on GL-TCN

Li, Xiaoyü; Jiang, Qingming; Ni, Sen; Xu, Yuebin; Xu, Min; Jia, Pengfei

doi:10.1016/j.snb.2023.133821

Sensors and Actuators B: Chemical

2023

DOI: 10.1016/j.snb.2023.133821

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An electronic nose for CO concentration prediction based on GL-TCN

Xiaoyü Li

Qingming Jiang

Sen Ni

et al.

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Cited by 13 publications

(2 citation statements)

References 33 publications

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“…GL-TCN demonstrated superior fitting performance compared to conventional models like long short-term memory (LSTM) and TCN, even when the amount of data was reduced. 35 Zeng et al introduced a dual-channel temporal convolutional network (TCN) to improve the accuracy of regression prediction for gas mixture concentrations in electronic nose. The experimental results demonstrated that the dual-channel TCN architecture performed better than other models.…”

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confidence: 99%

“…Li et al proposed an improved temporal convolutional network (TCN) for predicting CO gas concentrations. GL-TCN demonstrated superior fitting performance compared to conventional models like long short-term memory (LSTM) and TCN, even when the amount of data was reduced . Zeng et al introduced a dual-channel temporal convolutional network (TCN) to improve the accuracy of regression prediction for gas mixture concentrations in electronic nose.…”

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confidence: 99%

See 1 more Smart Citation

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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confidence: 99%

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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MGGTSP‐CAT: Integrating Temporal Convolution and LSTM for Multi‐Scale Greenhouse Gas Time Series Prediction via Cross‐Attention Mechanism

Zhong,

Zheng

2024

Advcd Theory and Sims

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Greenhouse gas‐induced global warming represents one of the most pressing environmental challenges currently faced, with methane, a primary constituent of greenhouse gases, exerting a more potent greenhouse effect than carbon dioxide. Precise prediction of methane concentrations is therefore of paramount importance. Owing to the complex interplay of factors affecting methane levels, existing forecasting methodologies often fall short in terms of accuracy and long‐term applicability. In response, this article leverages a multiscale time series to propose an attention‐based deep learning model. This approach synthesizes data across varied time spans, enhancing the extraction of periodic patterns and inter‐scale feature correlations. The model employs a hybrid framework and attention mechanisms to adaptively discern both short‐ and long‐term dependencies, as well as spatiotemporal correlations, capturing the intricate coupling of change mechanisms in the environment. Focusing on the Yanqing district of Beijing, a dataset integrating multiple features is developed and conduct forecasts 48 h in advance for both surface methane concentrations and the average molar fraction of total methane columns, comparing our model against several benchmarks. The model demonstrates superior performance, achieving an RMSE of 5.6123, MAE of 3.4959, and an R2 of 0.9148, which significantly exceeded the accuracy and generalizability of all baseline models.

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Stationary sources exhaust gases sensors application by MoTeSe monolayer doped with ZrO2(1,2) cluster: A DFT study

Xie,

Zhao,

Zheng

et al. 2024

Chemical Physics Letters

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An electronic nose for CO concentration prediction based on GL-TCN

Cited by 13 publications

References 33 publications

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

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

MGGTSP‐CAT: Integrating Temporal Convolution and LSTM for Multi‐Scale Greenhouse Gas Time Series Prediction via Cross‐Attention Mechanism

Stationary sources exhaust gases sensors application by MoTeSe monolayer doped with ZrO2(1,2) cluster: A DFT study

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