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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]

The multi-process manufacturing of steel rolling products requires the cooperation of complicated and variable rolling conditions. Such conditions pose challenges to the fault diagnosis of the key equipment of the rolling mill. The development of transfer learning has alleviated the problem of fault diagnosis under variable working conditions to a certain extent. However, existing diagnosis methods based on transfer learning only consider the distribution alignment from a single representation, which may only transfer part of the state knowledge and generate fuzzy decision boundaries. Therefore, this paper proposes a multi-representation domain adaptation network with duplex adversarial learning for hot rolling mill fault diagnosis. First, a multi-representation network structure is designed to extract rolling mill equipment status information from multiple perspectives. Then, the domain adversarial strategy is adopted to match the source and target domains of each pair of representations for learning domain-invariant features from multiple representation networks. In addition, the maximum classifier discrepancy adversarial algorithm is adopted to generate target features that are close to the source support, thereby forming a robust decision boundary. Finally, the average value of the predicted probabilities of the two classifiers is used as the final diagnostic result. Extensive experiments are conducted on an experimental platform of a four-high hot rolling mill to collect the fault state data of the reduction gearbox and roll bearing. The experimental results reveal that the method can effectively realize the fault diagnosis of rolling mill equipment under variable working conditions and can achieve average diagnostic rates of up to 99.15% and 99.40% on the data sets of the rolling mill gearbox and bearing, which are respectively 2.19% and 1.93% higher than the rates achieved by the most competitive method. [ABSTRACT FROM AUTHOR]

In order to solve the problems of small cracks easily lost and isolated noise difficult to eliminate in current industrial crack segmentation, an industrial crack segmentation network based on reversible pyramid and balanced attention (RPBAN) is proposed. Firstly, a reversible pyramid module is proposed, which introduces a feature pyramid and an improved inverted feature pyramid between encoder and decoder. By deepening the fusion of global features and detailed features, the performance of small crack detection is improved. Secondly, the balanced attention module is introduced in the decoding stage, and the balanced feature is used as the guide information to effectively remove isolated noise points. Finally, in the learning stage, the Focal Loss is selected as the loss function in training to control the weights of positive and negative samples, so that the model pays more attention to the crack samples. By verification and testing on the self-built InsulatorCrack dataset and three challenging public crack datasets CFD, CrackTree200 and AEL, the experimental results show that compared with other benchmark methods, RPBAN can achieve higher semantic segmentation accuracy, better detection effect on small cracks, and more effective elimination of isolated noise points. On the four data sets, IoU reaches 61.42%, 58.36%, 64.45% and 53.44% respectively, indicating the effectiveness and versatility of RPBAN. [ABSTRACT FROM AUTHOR]

As the existing object detection algorithms have the problem of low detection accuracy of power components and inspection defects in complex inspection scenarios of high-voltage power, defect detection in transmission line based on scale- invariant feature pyramid networks is proposed. Firstly, by applying current baseline detection methods to the problem in this article, the RepPoints v2 networks has the highest accuracy. Secondly, as FPN structure cannot effectively extract cross- level semantic information and ignoring the scale normalization in the corner point verification process of RepPoints v2, the scale- invariant feature pyramid networks (SI- FPN) structure is proposed by combining the efficient channel attention (ECA)mechanism and the scale-equalizing pyramid convolution (SEPC). In SI-FPN, the ECA attention module enhances the features of FPN at the channel level, and then SEPC extracts scale-invariant features from FPN and fuses cross-level pyramid features. Through training and testing on the self-built data set of six objects including insulator, shock hammer, suspension clamp, insulator self explosion, shock hammer falling off and bird' s nest, the proposed method improves 1.9 percentage points on the baseline of RepPoints v2, and the mAP reaches 96.3%. The detection accuracy is far beyond the current baseline detection models. Moreover, the SI-FPN module designed in this paper can be used as an independent structure to improve other detection models, which has certain universality. [ABSTRACT FROM AUTHOR]