A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask...A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper. The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process. For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics. By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%.展开更多
As global warming caused by greenhouse gases grows (GHGs) into a global environmental threat, carbon dioxide emissions are drawing increasing attention in these years. Among all emission sources, transportation is a m...As global warming caused by greenhouse gases grows (GHGs) into a global environmental threat, carbon dioxide emissions are drawing increasing attention in these years. Among all emission sources, transportation is a major contributor to climate change because of its high dependence on fossil fuels. The International Maritime Organization (IMO) has therefore been promoting the reduction of fuel usage and carbon emissions for container ships by such measures as improving shipping route selection, shipping speed optimization, and constructing clean energy propulsion systems. In this paper, a review of the impact of carbon dioxide emissions on climate change is presented;the current situations of carbon dioxide emissions, decarbonizing methods, IMO regulations, and possible future directions of decarbonizing in the maritime transportation industry are also discussed. Based on the result, it is found that in the case that non intelligent ships still occupy the vast majority of operating ships, the use of new energy as the main propulsion fuel has the defects of high renewal cost and long effective period. It is more likely to achieve energy conservation and emission reduction in the shipping industry in a short period of time by using intelligent means and artificial intelligence to assist ship operation. .展开更多
Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and...Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.展开更多
文摘A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper. Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper. The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process. For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics. By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%.
文摘As global warming caused by greenhouse gases grows (GHGs) into a global environmental threat, carbon dioxide emissions are drawing increasing attention in these years. Among all emission sources, transportation is a major contributor to climate change because of its high dependence on fossil fuels. The International Maritime Organization (IMO) has therefore been promoting the reduction of fuel usage and carbon emissions for container ships by such measures as improving shipping route selection, shipping speed optimization, and constructing clean energy propulsion systems. In this paper, a review of the impact of carbon dioxide emissions on climate change is presented;the current situations of carbon dioxide emissions, decarbonizing methods, IMO regulations, and possible future directions of decarbonizing in the maritime transportation industry are also discussed. Based on the result, it is found that in the case that non intelligent ships still occupy the vast majority of operating ships, the use of new energy as the main propulsion fuel has the defects of high renewal cost and long effective period. It is more likely to achieve energy conservation and emission reduction in the shipping industry in a short period of time by using intelligent means and artificial intelligence to assist ship operation. .
文摘Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.
