To improve the stability of rolling bearing fault signal denoising under different working conditions, this study proposes a multi-condition rolling bearing fault denoising method based on RIME-VMD. According to the characteristics of rolling bearing fault signal, RIME (frost and ice algorithm) is utilized to obtain adaptive optimization of the modal number and penalty factors in VMD (variational mode decomposition) algorithm, and then the optimized core parameters are input into VMD to decompose the rolling bearing fault signal. The fitness function is established by introducing the fusion of power spectrum entropy and kurtosis value; these intrinsic modal functions (IMFs) with high correlation based on original rolling bearing fault signal are selected to reconstruct the rolling bearing fault denoising signal. The experimental results show that the RIME-VMD method can effectively remove most of the noise in the rolling bearing fault signal, and the performance evaluation indexes of this method are better than other existing optimization algorithms. The research achievement of this study can provide effective data support for the fault diagnosis of bearing equipment.

This paper addresses critical challenges in Unmanned Aerial Vehicle (UAV) target detection and 3D positioning, specifically inaccuracies in localization and lack of robustness in complex environments. The objective of this research is to improve UAV detection and positioning accuracy by proposing an enhanced Deformable DETR (Detection Transformer) model integrated with multi-view geometry theory. To achieve this, the study first preprocesses UAV-collected data, then optimizes the convolutional layers of the original DETR model to better handle object occlusion and scale variations. Furthermore, the research incorporates multi-view geometric modeling and multimodal fusion strategies to enhance detection accuracy during the target recognition process. Experimental results demonstrate that the proposed approach achieves over 70% detection accuracy, significantly outperforming traditional methods. The findings underscore the effectiveness of combining the improved Deformable DETR model with multi-view geometry for high-precision detection and 3D localization in complex environments. This research has significant implications for UAV-based applications, such as autonomous navigation, surveillance, and search-and-rescue missions, where precise target detection and 3D positioning are critical for successful operation.

With the rapid development of modern technology, due to the light-weight, small size, and good concealment, unmanned aerial vehicle (UAV) has received much attention from the society and has been vigorously promoted. Photoelectric tracking detection system is an important means in the field of modern detection. The combination of a UAV and photoelectric detection system can effectively play an important role in reconnaissance and exploration, target positioning, communication, and navigation. At present, the relevant personnel have higher and higher requirements for the accuracy of the photoelectric detection function of UAV, and the original POS data relied on by the existing photoelectric detection devices of UAV has systematic errors, which leads to the low accuracy of photoelectric detection control. Therefore, in order to achieve the purpose of improving the high-precision control of the photoelectric detection device of UAV, this paper designed an optimization method for the high-precision control device of photoelectric detection of UAV based on POS data. Firstly, the improved PID control algorithm is applied to the optimization of the UAV control device, and secondly the error correction model is established by analyzing the error source, and the original POS data is corrected by the model. This paper used the designed high-precision control device optimization algorithm and the traditional algorithm to compare the stability control experiments of the UAV platform, respectively. The experimental results showed that the application of the improved UAV photoelectric detection control device optimization method could effectively improve the control device optimization accuracy of UAV photoelectric detection by 8.23, which was conducive to the efficient implementation of the project.

Addressing the issue of uncertainty in calculating the damage effectiveness of aerial maneuvering targets due to the random intersection with warhead fragment clusters formed by projectile proximity explosions, this paper puts forward a numerical calculation method of the aerial maneuvering target damage probability under the intersection between the warhead fragment group and the target with random dwell time sequence (RDTS) characteristics. Leveraging the random scattering characteristics of warhead fragment clusters and incorporating the vulnerability characteristics of the aerial maneuvering target, a model for calculating the probability of hitting and damaging an aerial maneuvering target with a warhead fragment cluster from a single projectile’s explosive is established. Factors related to the random dwell time of the target possibly falling into the effective damage area of the warhead fragment cluster from the projectile’s proximity explosion are introduced, resulting in the development of a model for calculating the damage probability of an aerial maneuvering target under RDTS. In addition, we provide a mathematical description of the statistical characteristics of the probability of damage. Through simulation and example calculation and analysis, the results demonstrate that this method can accurately reflect the actual damage effectiveness of warhead fragment clusters formed by projectile proximity explosion on the aerial maneuvering target in a random scattering state, offering a novel theoretical approach for calculating the damage effectiveness of highly maneuverable targets in the future.

The Dempster–Shafer (D–S) evidence theory, while powerful for uncertainty reasoning, suffers from mathematical limitations in high–conflict scenarios where its combination rule produces counterintuitive results. This paper introduces a reformulated D–S framework grounded in optimization theory and information geometry. We rigorously construct a dynamic weight allocation mechanism derived from minimizing systemic Jensen–Shannon divergence and propose a conflict–adaptive fusion rule with theoretical guarantees. We formally prove that our framework possesses the Conflict Attenuation Property and Robustness to Outlier Evidence. Extensive Monte Carlo simulations in multi–UAV explosion point localization demonstrate the framework’s superiority, reducing localization error by 75.6% in high–conflict scenarios compared to classical D–S. This work provides not only a robust application solution but also a theoretically sound and generalizable mathematical framework for multi–source data fusion under uncertainty.