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Unmanned aerial vehicle light detection and ranging (UAV‐lidar) and unmanned aerial vehicle (UAV) photogrammetry are currently commonly used surface monitoring technologies. Previous studies have used the two technologies interchangeably and ignored their correlation, or only compared them on a single product. However, there are few quantitative assessments of the differences between these two techniques in monitoring surface deformation and prediction of their application prospects. Therefore, the paper compared the differences between the digital elevation model (DEM) and subsidence basins obtained by the two techniques using Gaussian analysis. The results indicate that the surface DEMs obtained by both the techniques exhibit a high degree of similarity. The statistical analysis of the difference values in the z direction between the two DEMs follows a Gaussian distribution with a standard deviation of less than 0.36 m. When comparing the surface subsidence values monitored by the two techniques, it was found that UAV‐lidar was more sensitive to small‐scale deformation, with a difference range of 0.23–0.44 m compared to photogrammetry. The conclusion provides valuable information regarding the utilisation of multisource monitoring data. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low detection accuracy caused by insufficient target feature extraction in substation 3D scene, a voxelization-based 3D object detection model AugSecond for substation scene is proposed, which is designed based on the Second network structure. It introduces a triple attention mechanism in the voxel feature encoding stage, which focuses on multi-dimensional attention to enhance the key information of the target and reduce the interference of irrelevant feature information. It designes asymmetric sparse convolutional networks, uses asymmetric convolution to improve convolutional kernel representation capabilities and fuses multi-scale features to enrich target geometry information. Meanwhile, the position regression loss is optimized, and CIoU Loss is used to further consider the geometric correlation between bounding boxes to speed up the network convergence. Experiments on self-built power scene data sets and public data sets show that compared with the benchmark model, AugSecond model significantly improves recognition accuracy and has real-time reasoning speed, which proves the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Conversion of heat into electrical energy using a thermoelectric generator (TEG) will be beneficial for self‐powered wearable electronic systems. However, the output power of previously reported flexible TE generator (f‐TEG) is too low to meet the requirements of wearable electronic devices. Herein, we report an f‐TEG integrated with inorganic TE materials and exhibiting high output power. The proposed TE device has 60 pairs of p‐type Bi0.5Sb1.5Te3 and n‐type Bi2Te2.7Se0.3 TE legs, and has an extremely low internal resistance (1.09 Ω). This f‐TEG possesses good flexibility (with a bending radius of ∼1 cm) and high output‐power density (up to 4.49 mW/cm2 with a temperature difference of 30 K). To convenience use, a step‐up boost voltage converter is integrated with the TEG to convert the low voltage into a high voltage for micro‐device to work, it provides a new candidate scheme for self‐powered wearable electronic devices. Experiments showed that a LED can be lit up by the f‐TEG with a boost circuit. There results demonstrate that the proposed f‐TEG provides ultrahigh output power density and integrated self‐powered devices, can be used to powering low‐power electronics and sensors through heat harvesting. [ABSTRACT FROM AUTHOR]