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Rapid Identification of Multiple Gases

Published: 19 January 2022 Publication History

Abstract

Rapid identification of low-leveled toxic and harmful gases is of a challenge in current environmental monitoring. In this paper, we combined convolutional neural networks and bidirectional long short-term memory neural network, and proposed a method for fast identifying gases existing in trace amount in the environment. The attention mechanism was introduced to extract the key features of the input, and the Bayesian optimization method was applied to optimize the hyper-parameters. In order to evaluate the proposed method, we ran experiments using the low-concentration gas sensing data employing several existing predictive methods and the proposed one, and eventually compared their performances with recall and F1-score metrics. The results demonstrate that the performance of the proposed method exceeds that of the other methods, and also gives better performance on classifying gas components, given the gas concentration is below 125 ppm and the response time is limited to 0.5s.

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Cited By

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  • (2023)A Novel Gas Recognition Algorithm for Gas Sensor Array Combining Savitzky–Golay Smooth and Image Conversion RouteChemosensors10.3390/chemosensors1102009611:2(96)Online publication date: 29-Jan-2023

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cover image ACM Other conferences
AISS '21: Proceedings of the 3rd International Conference on Advanced Information Science and System
November 2021
526 pages
ISBN:9781450385862
DOI:10.1145/3503047
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 January 2022

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Author Tags

  1. Bayesian optimization
  2. attention mechanism
  3. gas recognition
  4. neural network

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • the National Natural Science Foundation of China
  • the University of Electronic Science and Technology of China

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AISS 2021

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Overall Acceptance Rate 41 of 95 submissions, 43%

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Cited By

View all
  • (2023)A Novel Gas Recognition Algorithm for Gas Sensor Array Combining Savitzky–Golay Smooth and Image Conversion RouteChemosensors10.3390/chemosensors1102009611:2(96)Online publication date: 29-Jan-2023

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