This work provided the first example of selective hydrodeoxygenation of 5-hydroxymethylfurfural (HMF) to 2,5-dimethylfuran (DMF) over heterogeneous Fe catalysts. A catalyst prepared by the pyrolysis of an Fe-phenanthroline complex on activated carbon at 800 °C was demonstrated to be the most active heterogeneous Fe catalyst. Under the optimal reaction conditions, complete conversion of HMF was achieved with 86.2 % selectivity to DMF. The reaction pathway was investigated thoroughly, and the hydrogenation of the C=O bond in HMF was demonstrated to be the rate-determining step during the hydrodeoxygenation, which could be accelerated greatly by using alcohol solvents as additional H-donors. The excellent stability of the Fe catalyst, which was probably a result of the well-preserved active species and the pore structure of the Fe catalyst in the presence of H , was demonstrated in batch and continuous flow fixed-bed reactors.
It is very challenging to detect traffic signs using a high-precision real-time approach in realistic scenes with respect to driver-assistance systems for driving vehicles and autonomous driving. To address this challenge, in this paper, a new detection scheme (named MSA_YOLOv3) is proposed to accurately achieve real-time localization and classification of small traffic signs. First, data augmentation is achieved using image mixup technology. Second, a multi-scale spatial pyramid pooling block is introduced into the Darknet53 network to enable the network to learn object features more comprehensively. Finally, a bottomup augmented path is designed to enhance the feature pyramid in YOLOv3, and the result is to achieve accurate localization of objects by utilizing fine-grained features effectively in the lower layers. According to the tests on the TT100K dataset (which is a dataset for traffic sign detection), the performance of the proposed MSA_YOLOv3 is better than that of YOLOv3 in detecting small traffic signs. The detection speed of MSA_YOLOv3 is 23.81 FPS, and the mAP (mean Average Precision) reaches up to 86%.
Video monitoring is an important means to ensure production safety in coal mine. However, the currently intelligent video surveillance is difficult to respond in real-time due to the latency of cloud computing. In this paper, a cloud-edge cooperation framework is proposed, which integrates cloud computing and edge computing in a coordinated manner. The cloud computing is used to process non-realtime and global tasks, while the edge computing is responsible for handling local monitoring video in realtime. In order to realize cloud-edge data interaction and online optimization for edge models, the heterogeneous converged network is built. In addition, an object detection model FL-YOLO composed of depthwise separable convolution and down-sampling inverted residual block is proposed, which realizes real-time video analysis at the edge. Finally, this paper discusses the complexity of FL-YOLO by its computational cost and model size. The experiment results show that the model size of FL-YOLO is 16.1MB, which is very light, and it achieves 36.7 FPS on NVIDIA Jetson TX1 and an AP of 76.7% on Multi-scene pedestrian dataset. Comparing with mainstream object detection models, FL-YOLO completes faster detection speed and higher accuracy, and it has lower calculation complexity and smaller model scale. Furthermore, the AP on Single-scene pedestrian dataset of FL-YOLO is improved to 80.7% by cloud-edge cooperation. K-Fold method is also used to further compared the performance of FL-YOLO and other models. Moreover, system test is implemented on coal mine, which validates the actual engineering effect of the proposed cloud-edge cooperation framework.INDEX TERMS Edge computing, YOLO, cloud-edge cooperation, real-time analysis.
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