Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust
Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing...
Ausführliche Beschreibung
Autor*in: |
Zhenduo Zhang [verfasserIn] Huijie Wang [verfasserIn] Kai Cao [verfasserIn] Ying Li [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
In: Remote Sensing - MDPI AG, 2009, 15(2023), 11, p 2721 |
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Übergeordnetes Werk: |
volume:15 ; year:2023 ; number:11, p 2721 |
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DOI / URN: |
10.3390/rs15112721 |
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Katalog-ID: |
DOAJ094245894 |
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10.3390/rs15112721 doi (DE-627)DOAJ094245894 (DE-599)DOAJ306d8ca1a641467f8621b10061a879a2 DE-627 ger DE-627 rakwb eng Zhenduo Zhang verfasserin aut Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO<sub<2</sub< content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO<sub<2</sub< concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO<sub<2</sub< content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO<sub<2</sub< content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO<sub<2</sub< content and the radiation characteristics for each channel, which it used to predict the CO<sub<2</sub< content in the ship exhaust. The results demonstrated that the predicted and true CO<sub<2</sub< contents had a root mean square error of <0.2, mean absolute error of <0.15, and mean absolute percentage error of <3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO<sub<2</sub< content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions. ship emissions exhaust plume CO<sub<2</sub< concentration convolutional neural network imaging detection Science Q Huijie Wang verfasserin aut Kai Cao verfasserin aut Ying Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 11, p 2721 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:11, p 2721 https://doi.org/10.3390/rs15112721 kostenfrei https://doaj.org/article/306d8ca1a641467f8621b10061a879a2 kostenfrei https://www.mdpi.com/2072-4292/15/11/2721 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 11, p 2721 |
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10.3390/rs15112721 doi (DE-627)DOAJ094245894 (DE-599)DOAJ306d8ca1a641467f8621b10061a879a2 DE-627 ger DE-627 rakwb eng Zhenduo Zhang verfasserin aut Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO<sub<2</sub< content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO<sub<2</sub< concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO<sub<2</sub< content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO<sub<2</sub< content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO<sub<2</sub< content and the radiation characteristics for each channel, which it used to predict the CO<sub<2</sub< content in the ship exhaust. The results demonstrated that the predicted and true CO<sub<2</sub< contents had a root mean square error of <0.2, mean absolute error of <0.15, and mean absolute percentage error of <3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO<sub<2</sub< content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions. ship emissions exhaust plume CO<sub<2</sub< concentration convolutional neural network imaging detection Science Q Huijie Wang verfasserin aut Kai Cao verfasserin aut Ying Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 11, p 2721 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:11, p 2721 https://doi.org/10.3390/rs15112721 kostenfrei https://doaj.org/article/306d8ca1a641467f8621b10061a879a2 kostenfrei https://www.mdpi.com/2072-4292/15/11/2721 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 11, p 2721 |
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10.3390/rs15112721 doi (DE-627)DOAJ094245894 (DE-599)DOAJ306d8ca1a641467f8621b10061a879a2 DE-627 ger DE-627 rakwb eng Zhenduo Zhang verfasserin aut Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO<sub<2</sub< content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO<sub<2</sub< concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO<sub<2</sub< content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO<sub<2</sub< content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO<sub<2</sub< content and the radiation characteristics for each channel, which it used to predict the CO<sub<2</sub< content in the ship exhaust. The results demonstrated that the predicted and true CO<sub<2</sub< contents had a root mean square error of <0.2, mean absolute error of <0.15, and mean absolute percentage error of <3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO<sub<2</sub< content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions. ship emissions exhaust plume CO<sub<2</sub< concentration convolutional neural network imaging detection Science Q Huijie Wang verfasserin aut Kai Cao verfasserin aut Ying Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 11, p 2721 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:11, p 2721 https://doi.org/10.3390/rs15112721 kostenfrei https://doaj.org/article/306d8ca1a641467f8621b10061a879a2 kostenfrei https://www.mdpi.com/2072-4292/15/11/2721 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 11, p 2721 |
allfieldsGer |
10.3390/rs15112721 doi (DE-627)DOAJ094245894 (DE-599)DOAJ306d8ca1a641467f8621b10061a879a2 DE-627 ger DE-627 rakwb eng Zhenduo Zhang verfasserin aut Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO<sub<2</sub< content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO<sub<2</sub< concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO<sub<2</sub< content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO<sub<2</sub< content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO<sub<2</sub< content and the radiation characteristics for each channel, which it used to predict the CO<sub<2</sub< content in the ship exhaust. The results demonstrated that the predicted and true CO<sub<2</sub< contents had a root mean square error of <0.2, mean absolute error of <0.15, and mean absolute percentage error of <3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO<sub<2</sub< content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions. ship emissions exhaust plume CO<sub<2</sub< concentration convolutional neural network imaging detection Science Q Huijie Wang verfasserin aut Kai Cao verfasserin aut Ying Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 11, p 2721 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:11, p 2721 https://doi.org/10.3390/rs15112721 kostenfrei https://doaj.org/article/306d8ca1a641467f8621b10061a879a2 kostenfrei https://www.mdpi.com/2072-4292/15/11/2721 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 11, p 2721 |
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10.3390/rs15112721 doi (DE-627)DOAJ094245894 (DE-599)DOAJ306d8ca1a641467f8621b10061a879a2 DE-627 ger DE-627 rakwb eng Zhenduo Zhang verfasserin aut Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO<sub<2</sub< content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO<sub<2</sub< concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO<sub<2</sub< content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO<sub<2</sub< content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO<sub<2</sub< content and the radiation characteristics for each channel, which it used to predict the CO<sub<2</sub< content in the ship exhaust. The results demonstrated that the predicted and true CO<sub<2</sub< contents had a root mean square error of <0.2, mean absolute error of <0.15, and mean absolute percentage error of <3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO<sub<2</sub< content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions. ship emissions exhaust plume CO<sub<2</sub< concentration convolutional neural network imaging detection Science Q Huijie Wang verfasserin aut Kai Cao verfasserin aut Ying Li verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 11, p 2721 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:11, p 2721 https://doi.org/10.3390/rs15112721 kostenfrei https://doaj.org/article/306d8ca1a641467f8621b10061a879a2 kostenfrei https://www.mdpi.com/2072-4292/15/11/2721 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 11, p 2721 |
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Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust |
abstract |
Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO<sub<2</sub< content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO<sub<2</sub< concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO<sub<2</sub< content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO<sub<2</sub< content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO<sub<2</sub< content and the radiation characteristics for each channel, which it used to predict the CO<sub<2</sub< content in the ship exhaust. The results demonstrated that the predicted and true CO<sub<2</sub< contents had a root mean square error of <0.2, mean absolute error of <0.15, and mean absolute percentage error of <3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO<sub<2</sub< content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions. |
abstractGer |
Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO<sub<2</sub< content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO<sub<2</sub< concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO<sub<2</sub< content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO<sub<2</sub< content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO<sub<2</sub< content and the radiation characteristics for each channel, which it used to predict the CO<sub<2</sub< content in the ship exhaust. The results demonstrated that the predicted and true CO<sub<2</sub< contents had a root mean square error of <0.2, mean absolute error of <0.15, and mean absolute percentage error of <3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO<sub<2</sub< content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions. |
abstract_unstemmed |
Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO<sub<2</sub< content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO<sub<2</sub< concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO<sub<2</sub< content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO<sub<2</sub< content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO<sub<2</sub< content and the radiation characteristics for each channel, which it used to predict the CO<sub<2</sub< content in the ship exhaust. The results demonstrated that the predicted and true CO<sub<2</sub< contents had a root mean square error of <0.2, mean absolute error of <0.15, and mean absolute percentage error of <3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO<sub<2</sub< content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions. |
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Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust |
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https://doi.org/10.3390/rs15112721 https://doaj.org/article/306d8ca1a641467f8621b10061a879a2 https://www.mdpi.com/2072-4292/15/11/2721 https://doaj.org/toc/2072-4292 |
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