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A Deep Learning Approach for Classification and Measurement of Hazardous Gases Using Multi-Sensor Data Fusion
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- Hussain, Mazhar, 1980- (författare)
- Mittuniversitetet,Institutionen för data- och elektroteknik (2023-),STC
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- O'Nils, Mattias, 1969- (författare)
- Mittuniversitetet,Institutionen för data- och elektroteknik (2023-),STC
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- Lundgren, Jan, 1977- (författare)
- Mittuniversitetet,Institutionen för data- och elektroteknik (2023-),STC
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visa fler...
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- Akbari-Saatlu, Mehdi (författare)
- Mittuniversitetet,Institutionen för ingenjörsvetenskap, matematik och ämnesdidaktik (2023-)
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- Hamrin, Rikard (författare)
- Mittuniversitetet,Institutionen för data- och elektroteknik (2023-),STC
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- Mattsson, Claes, 1978- (författare)
- Mittuniversitetet,Institutionen för ingenjörsvetenskap, matematik och ämnesdidaktik (2023-)
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(creator_code:org_t)
- IEEE conference proceedings, 2023
- 2023
- Engelska.
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Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings.
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Significant risks to public health and the environment are posed by the release of hazardous gases from industries such as pulp and paper. In this study, the aim was to develop a multi-sensor system with a minimal number of sensors to detect and identify hazardous gases. Training and test data for two gases, hydrogen sulfide and methyl mercaptan, which are known to contribute significantly to odors, were generated in a controlled laboratory environment. The performance of two deep learning models, a 1d-CNN and a stacked LSTM, for data fusion with different sensor configurations was evaluated. The performance of these models was compared with a baseline machine learning model. It was observed that the baseline model was outperformed by the deep learning models and achieved good accuracy with a four-sensor configuration. The potential of a cost-effective multi-sensor system and deep learning models in detecting and identifying hazardous gases is demonstrated by this study, which can be used to collect data from multiple locations and help guide the development of in-situ measurement systems for real-time detection and identification of hazardous gases at industrial sites. The proposed system has important implications for reducing pollution and protecting public health.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- Gas measurement
- Pulp & Paper
- Multi-sensor
- Data fusion
- Machine learning
- Deep learning
- CNN
- 1D-CNN
- SVM
- LSTM
- Gas classification
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
- Av författaren/redakt...
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Hussain, Mazhar, ...
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O'Nils, Mattias, ...
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Lundgren, Jan, 1 ...
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Akbari-Saatlu, M ...
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Hamrin, Rikard
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Mattsson, Claes, ...
- Om ämnet
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Elektroteknik oc ...
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och Annan elektrotek ...
- Artiklar i publikationen
- 2023 IEEE Sensor ...
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