Subway Obstacle Perception and Identification Method Based on Cloud Edge Collaboration

The traditional analysis method of train obstacle uses isomorphic sensors to obtain the state information and completes detection and identification analysis at the remote end of a network. A single data sample and more processing links will reduce the accuracy and speed analysis for subway encountering obstacles. To solve this problem, this paper proposes a subway obstacle perception and identification method based on cloud edge cooperation. The subway monitoring cloud platform realizes the training and construction of a detection model, and the network edge side completes the situation awareness of track state and real-time action when the train encounters obstacles. Firstly, the railroad track position is detected by cameras, and subway running track is identified by Mask RCNN algorithm to determine the detection area of obstacles in the process of subway train running. At the edge of network, the feature-level fusion of data collected by sensor cluster is carried out to provide reliable data support for detection work. Then, based on the DeepSort and YOLOv3 network models, the subway obstacle detection model is constructed on the subway monitoring cloud platform. Moreover, a trained model is distributed to the network edge side, so as to realize the fast and efficient perception and action of obstacles. Finally, the simulation verification is implemented based on actual collected datasets. Experimental results show that the proposed method has good detection accuracy and efficiency, which maintains 98.9% and 1.43 s for obstacle detection accuracy and recognition time in complex scenes.

Microgrid Group Control Method Based on Deep Learning under Cloud Edge Collaboration

Aiming at the economic benefits, load fluctuations, and carbon emissions of the microgrid (MG) group control, a method for controlling the MG group of power distribution Internet of Things (IoT) based on deep learning is proposed. Firstly, based on the cloud edge collaborative power distribution IoT architecture, combined with distributed generation, electric vehicles (EV), and load characteristics, the MG system model in the power distribution IoT is established. Then, a deep learning algorithm is used to train the features of the data model on the edge side. Finally, the group control strategy is adopted in the power distribution cloud platform to reasonably regulate the coordinated output of multiple energy sources, adjust the load state, and realize the economic operation of the power grid. Based on the MATLAB platform, a group model of MG is built and simulated. The results show the effectiveness of the proposed control method. Compared with other methods, the proposed control method has higher income and minimum carbon emission and realizes the economic and environmental protection system operation.

Falcon: A Blockchain-Based Edge Service Migration Framework in MEC

Driven by advanced 5G cellular systems, mobile edge computing (MEC) has emerged as a promising technology that can meet the energy efficiency and latency requirements of IoT applications. Edge service migration in the MEC environment plays an important role in ensuring user service quality and enhancing terminal computing capabilities. Application services on the edge side should be migrated from different edge servers to edge nodes closer to users, so that services follow users and ensure high-quality services. In addition, during the migration process, edge services face security challenges in an edge network environment without centralized management. To tackle this challenge, this paper innovatively proposes a blockchain-based security edge service migration framework, Falcon, which uses mobile agents different from VM and container as edge service carriers, making migration more flexible. Furthermore, we considered the dependencies between agents and designed a service migration algorithm to maximize the migration benefits and obtain better service quality. In order to ensure the migration of edge services in a safe and reliable environment, Falcon maintains an immutable alliance chain among multiple edge clouds. Finally, the experimental results show that “Falcon” has lower energy consumption and higher service quality.

Heat Transfer Characterization Inside a Rotating Rib Roughened Cooling Channel With Multiple Heated Walls

This work experimentally investigates the effects of walls heating conditions on the heat transfer coefficient distribution inside a rotating cooling channel. The model has a square cross-section with a hydraulic diameter of 50 mm and one ribbed side. The ribs are perpendicular to the main flow direction, the rib pitch-to-height ratio is 10, and the blockage ratio is 10%. Detailed heat transfer measurements were performed employing the liquid crystals thermography in steady-state approach under two thermal boundary conditions: only ribbed wall heated or equal heat fluxes set to ribbed wall and sidewalls. Inlet Reynolds number was 20000 and the tests were conducted in both static and rotating conditions up to a rotation number of 0.18, with the ribbed wall acting as trailing edge side. The results show that the wall heating conditions have a negligible effect on the heat transfer distribution for the stationary case, whereas, they lead to a modification of the heat transfer distribution under rotation with an overall enhancement when three walls are heated.

The structure, and electronic and magnetic properties of MX (M=GA, IN; X=S, SE, TE) nanoribbons

We used the first principle of density functional theory to perform detailed calculations regarding the structure, and the electronic and magnetic properties of MX (M[Formula: see text]=[Formula: see text]Ga, In; X[Formula: see text]=[Formula: see text]S, Se, Te) nanoribbons. The armchair nanoribbons (ARNs) are nonmagnetic semiconductors, which have even or odd oscillations of bandgaps. All small-sized zigzag nanoribbons (ZRNs) were found to break the six-membered ring structure and move to the center, thereby exhibiting nonmagnetic semiconductor behavior owing to the quantum confinement effect. However, among the large ZRNs, which are all metals, MTe ZRNs are nonmagnetic; this differs from the case of graphene, MoS2 and Ti2CO2 nanoribbons. MX (M[Formula: see text]=[Formula: see text]Ga, In; X[Formula: see text]=[Formula: see text]S, Se) ZRNs exhibited ferromagnetism owing to the presence of the unpaired electrons on the metal-edge side and the magnetic moment of each pair of molecules, which was controlled by the size of the nanoribbons. The results provided a theoretical reference that can be used in the future to produce MX materials for application in low-dimensional semiconductor devices, spin electron transport devices and new magnetoresistance devices.