Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
In sensor design, electromagnetic field numerical simulation techniques are widely used to investigate the working principles of sensors. These analyses help designers understand how sensors detect and respond to external signals during operation. One popular method for electromagnetic field computation is the meshless radial point interpolation method (RPIM), where the number and distribution of nodes are critical to ensuring both accuracy and efficiency. However, traditional RPIM methods often face challenges in achieving stable and precise results, particularly in complex electromagnetic environments. In order to enhance the stability and accuracy of electromagnetic numerical calculations, a node generation and adaptive refinement algorithm for the meshless RPIM is proposed. The proposed approach includes an initial node-generation method designed to optimize the balance between computational accuracy and efficiency, as well as a dynamic error threshold and hybrid node refinement method to precisely identify and adaptively refine areas requiring additional nodes, ensuring high precision in critical regions. The proposed method was validated through its application to electrostatic fields and multi-media magnetic fields, demonstrating significant improvements in both stability and accuracy compared with conventional RPIM approaches. These findings highlight the potential of the proposed algorithm to enhance the reliability and precision of electromagnetic field simulations in sensor design and related applications....
The dispersion effect of seawater can cause the envelop distortion of underwater eLoran signals, which causes the envelope-to-cycle difference (ECD) to exceed the standard range. Furthermore, it results in incorrect cycle identification and significant positioning errors. However, few studies have focused on the distortion caused by the dispersion effect. In this study, we propose an accurate underwater eLoran ECD compensation method based on a variable step size least mean square (VSS-LMS) algorithm. First, a systematic modeling approach was employed to investigate the impact of dispersion effects on Loran signals. Second, the VSS-LMS algorithm was introduced to update the filter weight vector in response to discrepancies in the input signal. Finally, the input signal was subjected to an adaptive transversal filtering process, resulting in an output signal that adhered to the specifications of the ECD standard. The efficacy and superiority of the proposed algorithm were demonstrated by experimentation and simulation. When the depth of seawater exceeds 2 m, the ECD value of the original eLoran signal exceeds the standard range, precluding the possibility of cycle identification. However, when the depth of seawater reaches 4 m, the ECD of the signal compensated by the proposed algorithm adaptively compensates for the normal range, thereby enabling the accurate recognition of cycles....
Ultrasonic imaging methods show significant advantages in detecting internal defects of composite crystalline materials. For polymer-bonded explosives (PBXs) with highly filled crystalline particles, the strong acoustic aenuation caused by their heterogeneous crystalline structure leads to low signal-to-noise ratios (SNRs) in the full matrix capture (FMC) signals and strong background noise in reconstructed images. To realize the high-SNR imaging of defects in PBXs, this paper is the first to schematically reorganize the nonlinear post-process algorithms which have the potential to realize high-SNR imaging of defects in crystalline particle-filled explosives. Six kinds of beamforming algorithms (DAS, F-DMAS, BB-DMAS, DMAS3, L-DMAS, and DS-DMAS) were applied to the same FMC data to reconstruct the images of prefabricated side-drilled holes (SDHs) in PBXs. The image quality in terms of SNR, lateral and axial resolution, and calculation efficiency was compared and evaluated quantitatively. The experimental results show that the nonlinear beamforming algorithms showed significant improvements in SNR and resolution. In particular, L-DMAS and DS-DMAS exhibited excellent imaging capability in SDH defect detection compared to the other algorithms, with effective suppression of crystalline structural noise....
Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. Environmental factors often lead to insulator defects, highlighting the need for accurate detection methods. This paper proposes an enhanced defect detection approach based on a lightweight neural network derived from the YOLOv11n architecture. Key innovations include a redesigned C3k2 module that incorporates multidimensional dynamic convolutions (ODConv) for improved feature extraction, the introduction of Slimneck to reduce model complexity and computational cost, and the application of the WIoU loss function to optimize anchor box handling and to accelerate convergence. Experimental results demonstrate that the proposed method outperforms existing models like YOLOv8 and YOLOv10 in precision, recall, and mean average precision (mAP), while maintaining low computational complexity. This approach provides a promising solution for real-time, high-accuracy insulator defect detection, enhancing the safety and reliability of power transmission systems....
With the explosive growth of information on the internet, personalized recommendation technology has become an important tool for helping users efficiently acquire information. However, existing spreading-based recommendation algorithms only consider user choices and fail to fully leverage the potential relationships between users and items. Additionally, the incomplete utilization of user and item information limits their application potential and applicable scenarios, resulting in suboptimal recommendation performance in practical applications. To address this issue, we propose a Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading (LGCNHS). This algorithm first optimizes the embeddings of users and items using their respective feature matrix, then learns the latent embedding representations of users and items through a lightweight graph convolutional network. Finally, the latent embedding representations are incorporated as key parameters into the hybrid spreading recommendation algorithm to generate recommendations. Comparative experiments on two publicly available datasets, MovieLens and Douban, demonstrate that LGCNHS achieves improved accuracy and diversity in recommendations compared to related methods. The algorithm code is available on github....
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