Identifying influential nodes in complex networks has developed into a prominent research focus. Classical centrality measures such as degree centrality (DC), betweenness centrality (BC), and K-shell (KS) are increasingly being supplemented or replaced by more effective gravity-based alternatives such as gravity centrality (GC), KS based on GC (KSGC), and GGC. However, existing gravity-based methods further enhance node quality based on single attributes, often neglecting the crucial role of edges as communication channels for information dissemination in unweighted networks. A neighborhood potential-edge-weight gravity centrality (NPGC) considering both the degree of node and the information interaction capability of its neighbors for identifying key nodes is proposed. The potential edge weights, which represent the strength of information interaction between nodes, are defined to capture neighborhood information. The simulation of SIR model is employed on nine real-world unweighted and undirected datasets to assess the efficiency of NPGC. Experimental results show that, compared with eight existing methods, the proposed method has high accuracy and good differentiation.

For three-dimensional (3D) imaging based on fringe projection profilometry (FPP), maximum fringe frequency selection and fringe frequencies allocation have a significant impact on the accuracy and robustness of 3D imaging. In this paper, we conduct a detailed analysis of the wrapped phase error, and analyze the phase unwrapping reliability in the three-frequency temporal phase unwrapping (TPU). Since different measurement systems and scenes having different maximum sampling frequencies, we introduce a maximum frequency selection approach in this work. In order to ensure the overall phase unwrapping reliability, we introduce an optimal frequencies allocation approach. Experimental results show the valid of the proposed approach. The research in this paper will help to improve the accuracy and robustness of FPP in practical 3D measurement.

Abstract High‐precision relative position and attitude measurement technology has a wide range of applications in aerospace and industrial production. Currently, the commonly used method for measurement is based on visual cooperative signs. However, its accuracy significantly decreases as the distance increases. Therefore, a relative positioning system is designed based on light field spatial coding and visual recognition. The projector emits spatially encoded structured light within the coverage of the light field, while the receiving end captures, identifies, measures the code, and calculates its pose in the light field coordinate system. Compared with the traditional measurement method, the measurement accuracy of this system does not decrease greatly with the increase in distance, the measurement distance can be adjusted in real‐time and does not depend on an external light source. By changing the projection pattern with different resolutions without changing hardware systems, it can adjust effective measurement distance accordingly. Theoretical and experimental results show that the proposed method can maintain measurement accuracy with the increase in distance.

Currently, the diagnosis of single fault marked by distinct characteristics has received mass concern, and the related research achievements are remarkable. However, the diagnosis of early multiple intermittent (EMI) faults commonly existing in industrial systems is still an intractable problem, owing to: 1) the scarcity of fault data and the faintness of features and 2) the mutual interaction of components and the coupling of fault characteristics. In order to address the problem, this article proposes an improved sparse autoencoder and multilevel denoising strategy (MDS-ISAE) for diagnosing EMI faults. First, a relational constraint term is constructed to mitigate the effect of data correlation. Second, a multilevel denoising strategy is designed to enhance the robustness of AE feature learning with strong background noise. Third, a resizable resampling strategy is planned to tackle the skew distribution of diagnosis data. Based on the above strategies, an MDS-ISAE-based model is constructed to achieve EMI faults diagnosis. Further, the evaluation criteria of diagnosis performance are proposed and the applicability of the method is tested. Finally, the effectiveness and practicability of the proposed method are verified by artificial damage and real fault experiments, respectively.