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Using a stereo camera system, a new diagnostic for the safety factor of the core plasma based on the pellet ablation trail is applied on the Experimental Advanced Superconducting Tokamak (EAST). In EAST discharge No. 128 874, a shattered pellet injection system is applied to inject a shattered neon pellet into the EAST. Since the strong magnetic field in tokamaks binds the ablated pellet material, the orientation of the pellet ablation trail is the same as the local magnetic field direction. Thus, from the three-dimensional reconstruction result of the pellet ablation trail, the local safety factor q can be obtained. The motional Stark effect (MSE) diagnostic is applied to determine the safety factor q profile in this shot. The determined safety factor q results for this new diagnostic are in quantitative agreement with those from the MSE diagnostic with the mean relative difference of only 6.8%, confirming the effectiveness of this new diagnostic of the safety factor. [ABSTRACT FROM AUTHOR]

The single individual capability and insufficient endurance of unmanned aerial vehicles (UAVs) have always been problems in power inspection operations. This article is based on the new development needs of UAV inspection and proposes a new model of multi-UAVs collaborative inspection. On this basis, this article studies the decoupling and intelligent matching technology between airports and UAVs, task merging and splitting technology in complex multi-task situations, and proposes a safety inspection strategy for multi-UAVs collaborative operations to ensure the safety of operations in the multi-UAVs multi-nests collaborative inspection mode. Finally, a complete multi-UAV, multi-nests collaborative inspection strategy is constructed. [ABSTRACT FROM AUTHOR]

We consider typical environmental conditions, such as different shooting backgrounds, lighting, and shooting distances, and study adaptive shooting algorithms for unmanned aerial vehicle (UAV) on-site environments under typical working conditions to achieve high-quality acquisition of UAV inspection images and reduce the technical difficulty of artificial intelligence recognition of inspection images. Based on the collected high-quality images and the characteristics of unmanned aerial vehicle inspection images, a deep learning technology framework is adopted to study the intelligent identification method of hidden dangers in transmission line equipment and channel environment, achieving the intelligent recognition of pin level fine particle defects. [ABSTRACT FROM AUTHOR]