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Progressive prediction algorithm by multi-interval data sampling in multi-task learning for real-time gas identification.

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Title: Progressive prediction algorithm by multi-interval data sampling in multi-task learning for real-time gas identification.
Authors: Fu, Ce1 (AUTHOR), Zhang, Kuanguang1 (AUTHOR), Guan, Huixin1 (AUTHOR), Deng, Shuai1 (AUTHOR), Sun, Yue1 (AUTHOR), Ding, Yang1 (AUTHOR), Wang, Junsheng1,2 (AUTHOR) wangjsh@dlmu.edu.cn, Liu, Jianqiao1,2 (AUTHOR) jqliu@dlmu.edu.cn
Source: Sensors & Actuators B: Chemical. Nov2024, Vol. 418, pN.PAG-N.PAG. 1p.
Subjects: Gas analysis, Environmental quality, Air quality, Environmental protection, Environmental health, Prediction algorithms
Abstract: Effective surveillance of harmful gases is paramount in the safeguard of human health and the protection of environmental air quality, thereby emphasizing the imperative for robust detection strategies, such as electronic noses. While the integration of intelligent algorithms has significantly improved the detection capabilities of electronic noses, most existing models focus on enhancing accuracy but usually ignore the crucial need for detection speed. To address this issue, the progressive prediction algorithm (PPA) is proposed to accomplish real-time gas recognition. The implementation of PPA incorporates time correction and multi-interval data sampling for gas analysis. Leveraging multi-task networks, this method proposes a novel network architecture that predicts gas type and concentration. The architecture synergizes the flexible receptive field size of time convolutional networks (TCN) with the long-term temporal dependency capture of gated recurrent units (GRU) to optimize overall model performance. The PPA achieves a classification accuracy of 99.3 % and a regression R2 score of 0.927 within half of the response time. For practical applications, this work enables the prediction of gas type and concentration during the early stages of sensor response, facilitating rapid detection of hazardous gases. [Display omitted] • Progressive prediction algorithm (PPA) proposed for real-time gas recognition. • PPA gives fast prediction of type and concentration for individual target gas. • Classification Acc. of 99.3 % and regression R2 of 0.927 within 1/2 response time. • Proposed PPA maintains validity on a small dataset by transfer learning. • PPA enables prediction in early stages for rapid detection of hazardous gas. [ABSTRACT FROM AUTHOR]
Copyright of Sensors & Actuators B: Chemical is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 178939884
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Progressive prediction algorithm by multi-interval data sampling in multi-task learning for real-time gas identification.
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  Data: <searchLink fieldCode="AR" term="%22Fu%2C+Ce%22">Fu, Ce</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Kuanguang%22">Zhang, Kuanguang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guan%2C+Huixin%22">Guan, Huixin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deng%2C+Shuai%22">Deng, Shuai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Yue%22">Sun, Yue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ding%2C+Yang%22">Ding, Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Junsheng%22">Wang, Junsheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wangjsh@dlmu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Jianqiao%22">Liu, Jianqiao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jqliu@dlmu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Sensors+%26+Actuators+B%3A+Chemical%22">Sensors & Actuators B: Chemical</searchLink>. Nov2024, Vol. 418, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Gas+analysis%22">Gas analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+quality%22">Environmental quality</searchLink><br /><searchLink fieldCode="DE" term="%22Air+quality%22">Air quality</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+protection%22">Environmental protection</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+health%22">Environmental health</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+algorithms%22">Prediction algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Effective surveillance of harmful gases is paramount in the safeguard of human health and the protection of environmental air quality, thereby emphasizing the imperative for robust detection strategies, such as electronic noses. While the integration of intelligent algorithms has significantly improved the detection capabilities of electronic noses, most existing models focus on enhancing accuracy but usually ignore the crucial need for detection speed. To address this issue, the progressive prediction algorithm (PPA) is proposed to accomplish real-time gas recognition. The implementation of PPA incorporates time correction and multi-interval data sampling for gas analysis. Leveraging multi-task networks, this method proposes a novel network architecture that predicts gas type and concentration. The architecture synergizes the flexible receptive field size of time convolutional networks (TCN) with the long-term temporal dependency capture of gated recurrent units (GRU) to optimize overall model performance. The PPA achieves a classification accuracy of 99.3 % and a regression R2 score of 0.927 within half of the response time. For practical applications, this work enables the prediction of gas type and concentration during the early stages of sensor response, facilitating rapid detection of hazardous gases. [Display omitted] • Progressive prediction algorithm (PPA) proposed for real-time gas recognition. • PPA gives fast prediction of type and concentration for individual target gas. • Classification Acc. of 99.3 % and regression R2 of 0.927 within 1/2 response time. • Proposed PPA maintains validity on a small dataset by transfer learning. • PPA enables prediction in early stages for rapid detection of hazardous gas. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Sensors & Actuators B: Chemical is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1016/j.snb.2024.136271
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        Text: English
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        Type: general
      – SubjectFull: Environmental quality
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      – SubjectFull: Air quality
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      – SubjectFull: Environmental protection
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      – SubjectFull: Environmental health
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      – SubjectFull: Prediction algorithms
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      – TitleFull: Progressive prediction algorithm by multi-interval data sampling in multi-task learning for real-time gas identification.
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            NameFull: Fu, Ce
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            NameFull: Zhang, Kuanguang
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            – D: 01
              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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