Search Results (2)

Energy-absorbing components should be effective and stable in engineering protective structure designs to reduce collision impacts. However, conventional energy-absorbing structures have considerable potential for optimization for energy dissipation and structural stability. Like other invertebrates, the centipede’s folding mode when moving forward is compatible with the hierarchical folding process when the energy-absorbing structure is impacted; however, this rule has not been thoroughly examined and proven. Based on this gap, this study built a unique biomimetic aluminum foam-filled bidirectional pyramid energy-absorbing structure, analyzed its geometric parameters on crashworthiness, and developed high-performance energy-absorbing components. Experiments and simulations were conducted on a bidirectional pyramid construction with three schemes for filling aluminum foam inspired by the centipede body section and profile. The construction with foam aluminum filling the gap has optimum specific energy absorption and load stability. Additionally, optimizing structural performance is most effective in certain ranges (78° ≤ θ ≤ 87°, t ≤ 0.1 mm, 34 mm ≤ d ≤ 44 mm). With Kriging and NSGA-III multi-objective optimization, the optimized peak crushing force decreases by 11.17% and specific energy absorption increases by 11.67%. The study and optimization process offers a theoretical reference for future high-performance energy-absorbing structures and has significant engineering application potential. Full article

Insulator defect detection is important in ensuring power systems’ safety and stable operation. To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extraction from high-entropy details to low-entropy semantics, an improved YOLOv8 target detection network for insulator defects based on bidirectional weighted feature fusion was proposed. A C2f_DSC feature extraction module was designed to identify more insulator tube features, an EMA (encoder–modulator–attention) mechanism and a BiFPN (bidirectional weighted feature pyramid network) fusion layer in the backbone network were introduced to extract different features in complex environments, and EIOU (efficient intersection over union) as the model’s loss function was used to accelerate model convergence. The CPLID (China Power Line Insulator Dataset) was tested to verify the effectiveness of the proposed algorithm. The results show its model size is only 6.40 M, and the mean accuracy on the CPLID dataset reaches 98.6%, 0.8% higher than that of the YOLOv8n. Compared with other lightweight models, such as YOLOv8s, YOLOv6, YOLOv5s, and YOLOv3Tiny, not only is the model size reduced, but also the accuracy is effectively improved with the proposed algorithm, demonstrating excellent practicality and feasibility for edge devices. Full article