The development of engineering technology such as inspection robots (IR) for transmission lines and wireless sensor networks (WSN) are widely used in the field of smart grid monitoring. However, how to integrate inspection robots into wireless sensor networks is still a great challenge to form an efficient dynamic monitoring network for transmission lines. To address this problem, a dynamic barrier coverage (DBC) method combining inspection robot and wireless sensor network (WSN) is proposed to realize a low-cost, energy-saving and dynamic smart grid-oriented sensing system based on mobile wireless sensor network. To establish an effective smart grid monitoring system, this research focuses on the design of an effective and safe dynamic network coverage and network nodes deployment method. Multiple simulation scenarios are implemented to explore the variation of network performance with different parameters. In addition, the dynamic barrier coverage method for the actual scene of smart grid monitoring considers the balance between network performance and financial costs.
With the development of engineering technology, the distributed design-based Branch-Trimming Robot (BTR) has been used to ensure the power supply security of transmission lines. However, it remains difficult to combine distributed BTRs with a wireless sensor network to build an efficient multi-robot system. To achieve this combination, a dynamic network topology control method was proposed, combining the motion characteristics of robots with the structure of a distributed wireless sensor network. In addition, a topology-updating mechanism based on node signal strength was adopted as well. To achieve efficient data transmission for distributed multi-robot systems, the present study focused on the design of a distributed network model and a dynamic network topology control strategy. Several simulation and test scenarios were implemented, and the changes of network performance under different parameters were studied. Furthermore, the real scene-based dynamic topology control method considers the relationship between network performance and antenna layout.
Elman neural network was one of the dynamic recurrent neural networks. In this paper, a modified Elman network was introduced first. Then we proposed a PID Elman neural network and its learning algorithms are discussed in detail. Simulation results based on ideal mathematical model and hydraulic unit model show that the PID Elman network is prior to the modified Elman network in identifying nonlinear dynamic system.
This paper proposes an optimization paradigm for structure design of curved-tube nozzle based on genetic algorithm. First, the mathematical model is established to reveal the functional relationship between outlet power and the nozzle structure parameters. Second, genetic algorithms transform the optimization process of curved-tube nozzle into natural evolution and selection. It is found that curved-tube nozzle with bending angle of 10.8°, nozzle diameter of 0.5 mm, and curvature radius of 8 mm yields maximum outlet power. Finally, we compare the optimal result with simulations and experiments of the rotating spinning. It is found that optimized curved-tube nozzle can improve flow field distribution and reduce the jet instability, which is critical to obtain high-quality nanofibers.
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