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description Publicationkeyboard_double_arrow_right Article 2022 FinlandPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | ERA-PLANET, AKA | Atmosphere and Climate Co..., AKA | NanoBioMass - Natural Sec...EC| ERA-PLANET ,AKA| Atmosphere and Climate Competence Center (ACCC) / Consortium: ACCC ,AKA| NanoBioMass - Natural Secreted Nano Vesicles as a Source of Novel Biomass Products for Circular Economy / Consortium: NanoBiomassArnab Chattopadhyay; Andres Huertas; Andrew Rebeiro-Hargrave; Pak Lun Fung; Samu Varjonen; Tuomas Hieta; Sasu Tarkoma; Tuukka Petaja;handle: 10138/345363
Formaldehyde is a carcinogenic indoor air pollutant emitted from common wood-based materials. Low-cost sensing of formaldehyde is difficult due to inaccuracies in measuring low concentrations and susceptibility of sensors to changing indoor environmental conditions. Currently gas sensors are calibrated by manufacturers using simplistic models which fail to capture their complex behaviour. We evaluated different low-cost gas sensors to ascertain a suitable component to create a mobile sensing node and built a calibration algorithm to correct it. We compared the performance of 2 electrochemical sensors and 3 metal oxide sensors in a controlled chamber against a photo-acoustic reference device. In the chamber the formaldehyde concentrations, temperature and humidity were varied to assess the sensors in diverse environments. Pre-calibration, the electrochemical sensors (mean absolute error (MAE) = 70.8 ppb) outperformed the best performing metal oxide sensor (MAE = 335 ppb). A two-stage calibration model was built, using linear regression followed by random forest, where the residual of the first stage acted as a input for the second. Post-calibration, the metal oxide sensors (MAE = 154 ppb) improved compared to their electrochemical counterparts (MAE = 78.8 ppb). Nevertheless, the uncalibrated electrochemical sensor showed overall superior performance hence was selected for the mobile sensing node. Peer reviewed
HELDA - Digital Repo... arrow_drop_down HELDA - Digital Repository of the University of HelsinkiArticle . 2022 . Peer-reviewedData sources: HELDA - Digital Repository of the University of Helsinkiadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2022.3172864&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert HELDA - Digital Repo... arrow_drop_down HELDA - Digital Repository of the University of HelsinkiArticle . 2022 . Peer-reviewedData sources: HELDA - Digital Repository of the University of Helsinkiadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2022.3172864&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2020 FinlandPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:AKA | Centre of Excellence in A..., EC | ACTRIS-2, EC | ATM-GTP +2 projectsAKA| Centre of Excellence in Atmospheric Science From Molecular and Biolocigal processes to The Global Climate ,EC| ACTRIS-2 ,EC| ATM-GTP ,EC| ERA-PLANET ,EC| ACTRIS IMPMartha A. Zaidan; Naser Hossein Motlagh; Pak Lun Fung; David Lu; Hilkka Timonen; Joel Kuula; Jarkko V. Niemi; Sasu Tarkoma; Tuukka Petäjä; Markku Kulmala; Tareq Hussein;handle: 10138/321216
This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities. Peer reviewed
IEEE Sensors Journal arrow_drop_down HELDA - Digital Repository of the University of HelsinkiArticle . 2020 . Peer-reviewedData sources: HELDA - Digital Repository of the University of Helsinkiadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2020.3010316&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 40 citations 40 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert IEEE Sensors Journal arrow_drop_down HELDA - Digital Repository of the University of HelsinkiArticle . 2020 . Peer-reviewedData sources: HELDA - Digital Repository of the University of Helsinkiadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2020.3010316&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2016Publisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | ComBoNDTEC| ComBoNDTM. Salvato; Saverio De Vito; Elena Esposito; Ettore Massera; M. L. Miglietta; Grazia Fattoruso; Girolamo Di Francia;Artificial olfaction is an emerging application field for machine learning practitioners. In this paper, we propose a holistic approach to pattern classification in electronic noses applications. In particular, we show how classification results based on a complete measurement cycle can be combined with an assessment provided by real-time classifiers acting on the single instantaneous measurement sample. A running classification confidence measure allows for obtaining fast and reliable outcomes. A safety critical scenario has been selected for the testing of the proposed pattern analysis strategy involving the identification and discrimination of surface contaminants on composite panels in pre-bonding nondestructive tests during lightweight aircraft assembly. A reject option has been introduced to refuse low classification confidence panels improving both FP and FN rates. Results show how this strategy can efficiently exploit two different views of the electronic nose olfactive fingerprinting process that is currently seen as alternative.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2015.2513818&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu7 citations 7 popularity Average influence Top 10% impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2015.2513818&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu
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description Publicationkeyboard_double_arrow_right Article 2022 FinlandPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | ERA-PLANET, AKA | Atmosphere and Climate Co..., AKA | NanoBioMass - Natural Sec...EC| ERA-PLANET ,AKA| Atmosphere and Climate Competence Center (ACCC) / Consortium: ACCC ,AKA| NanoBioMass - Natural Secreted Nano Vesicles as a Source of Novel Biomass Products for Circular Economy / Consortium: NanoBiomassArnab Chattopadhyay; Andres Huertas; Andrew Rebeiro-Hargrave; Pak Lun Fung; Samu Varjonen; Tuomas Hieta; Sasu Tarkoma; Tuukka Petaja;handle: 10138/345363
Formaldehyde is a carcinogenic indoor air pollutant emitted from common wood-based materials. Low-cost sensing of formaldehyde is difficult due to inaccuracies in measuring low concentrations and susceptibility of sensors to changing indoor environmental conditions. Currently gas sensors are calibrated by manufacturers using simplistic models which fail to capture their complex behaviour. We evaluated different low-cost gas sensors to ascertain a suitable component to create a mobile sensing node and built a calibration algorithm to correct it. We compared the performance of 2 electrochemical sensors and 3 metal oxide sensors in a controlled chamber against a photo-acoustic reference device. In the chamber the formaldehyde concentrations, temperature and humidity were varied to assess the sensors in diverse environments. Pre-calibration, the electrochemical sensors (mean absolute error (MAE) = 70.8 ppb) outperformed the best performing metal oxide sensor (MAE = 335 ppb). A two-stage calibration model was built, using linear regression followed by random forest, where the residual of the first stage acted as a input for the second. Post-calibration, the metal oxide sensors (MAE = 154 ppb) improved compared to their electrochemical counterparts (MAE = 78.8 ppb). Nevertheless, the uncalibrated electrochemical sensor showed overall superior performance hence was selected for the mobile sensing node. Peer reviewed
HELDA - Digital Repo... arrow_drop_down HELDA - Digital Repository of the University of HelsinkiArticle . 2022 . Peer-reviewedData sources: HELDA - Digital Repository of the University of Helsinkiadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2022.3172864&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert HELDA - Digital Repo... arrow_drop_down HELDA - Digital Repository of the University of HelsinkiArticle . 2022 . Peer-reviewedData sources: HELDA - Digital Repository of the University of Helsinkiadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2022.3172864&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2020 FinlandPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:AKA | Centre of Excellence in A..., EC | ACTRIS-2, EC | ATM-GTP +2 projectsAKA| Centre of Excellence in Atmospheric Science From Molecular and Biolocigal processes to The Global Climate ,EC| ACTRIS-2 ,EC| ATM-GTP ,EC| ERA-PLANET ,EC| ACTRIS IMPMartha A. Zaidan; Naser Hossein Motlagh; Pak Lun Fung; David Lu; Hilkka Timonen; Joel Kuula; Jarkko V. Niemi; Sasu Tarkoma; Tuukka Petäjä; Markku Kulmala; Tareq Hussein;handle: 10138/321216
This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities. Peer reviewed
IEEE Sensors Journal arrow_drop_down HELDA - Digital Repository of the University of HelsinkiArticle . 2020 . Peer-reviewedData sources: HELDA - Digital Repository of the University of Helsinkiadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2020.3010316&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routeshybrid 40 citations 40 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!more_vert IEEE Sensors Journal arrow_drop_down HELDA - Digital Repository of the University of HelsinkiArticle . 2020 . Peer-reviewedData sources: HELDA - Digital Repository of the University of Helsinkiadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2020.3010316&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2016Publisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | ComBoNDTEC| ComBoNDTM. Salvato; Saverio De Vito; Elena Esposito; Ettore Massera; M. L. Miglietta; Grazia Fattoruso; Girolamo Di Francia;Artificial olfaction is an emerging application field for machine learning practitioners. In this paper, we propose a holistic approach to pattern classification in electronic noses applications. In particular, we show how classification results based on a complete measurement cycle can be combined with an assessment provided by real-time classifiers acting on the single instantaneous measurement sample. A running classification confidence measure allows for obtaining fast and reliable outcomes. A safety critical scenario has been selected for the testing of the proposed pattern analysis strategy involving the identification and discrimination of surface contaminants on composite panels in pre-bonding nondestructive tests during lightweight aircraft assembly. A reject option has been introduced to refuse low classification confidence panels improving both FP and FN rates. Results show how this strategy can efficiently exploit two different views of the electronic nose olfactive fingerprinting process that is currently seen as alternative.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2015.2513818&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu7 citations 7 popularity Average influence Top 10% impulse Top 10% Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jsen.2015.2513818&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu