Symmetry

Marine vertical centrifugal pump vibration severely impacts equipment reliability and ship structural integrity, with low-frequency vibration being a key challenge for traditional passive isolation systems. To address this, this study aims to optimize the pump base’s vibration isolation performance by integrating symmetrically distributed acoustic black holes (ABHs) and locally resonant metamaterials. A combined numerical and experimental approach was adopted: an H-shaped ABH-coupling plate dynamic model was established and validated, followed by parametric evaluation of base structures, ABH parameters (length, l_ABH), damping layer configurations, and metamaterial arrays. Experimental tests were conducted using simulated pump excitation on the optimal prototype. The results show the optimal configuration—symmetrical ABH (l_ABH= 100 mm) with a full damping layer and 3 × 3 metamaterial array—achieves 11.97 dB low-frequency and 22.01 dB high-frequency vibration suppression, forming a 24.8–27.6 Hz bandgap and 7.43 dB isolation at characteristic frequencies, with an overall 13% performance improvement. This work verifies the feasibility of the symmetrical ABH–metamaterial hybrid system, providing a novel technical solution for high-performance vibration-noise reduction in marine power equipment.

In autonomous driving systems, fog decreases visibility and contrast, blurs the boundary of objects, and increases scale variations, which results in missed detections. To solve these problems, we introduce Fog-YOLO, a lightweight real-time detector based on the YOLOv12 framework. First, we design the A2C2f-FSA module, which enhances the representation of fog-affected areas and low-contrast objects by modeling long-range dependencies in the frequency domain. This module effectively suppresses the interference of fog and background noise while maintaining low computational overhead. Second, we propose a bidirectional feature fusion module (BFFM) that uses decoupled attention paths to fuse deep semantic features and shallow texture details. This approach enhances robustness across multiple scales, ensuring the capture of fine-grained texture information and the preservation of global contextual information in foggy environments. Third, we introduce GSConv, which reduces parameters and computational cost by balancing spatial correlation modeling and computational complexity, optimizing the feature extraction process. Finally, we design the F-WIoU v3 loss function, which optimizes bounding box regression through dynamic focusing and difficulty re-weighting strategies, thereby reducing the influence of low-quality samples while improving the model’s localization robustness in foggy conditions. Experiments on the RTTS real-world fog dataset and the VOC-FOG synthetic dataset show that Fog-YOLO outperforms the baseline by 5.2% and 7.3% in mAP@0.5 with real-time inference speed. Overall, Fog-YOLO outperforms mainstream lightweight detectors, demonstrating its practical usefulness for autonomous driving in foggy environments.

In adverse weather and rapidly changing scenes, fog severely reduces image contrast and obscures target textures, causing small-object detection to suffer from feature weakening and background interference. Many existing detectors, meanwhile, rely on computationally intensive feature modeling, making it difficult to achieve real-time inference while effectively mitigating fog-induced degradation. To address these challenges, we propose HS-MambaDet, a frequency-compensated hidden-state state-space detection network for accurate and efficient small-object detection in foggy environments. Specifically, we embed a lightweight SSD-based state-space modeling module with frequency-domain window attention (FWS-SSD) into the backbone, preserving long-range dependency modeling with low computational overhead while emphasizing informative high-frequency details and attenuating low-frequency haze interference. This study highlights a symmetry-inspired balance between global context modeling and local detail restoration. In the neck network, a multi-scale frequency-spatial fusion (MFSF) module further strengthens fine-grained object representations and cross-scale contextual interactions. In addition, we introduce a fog-aware detection loss to better supervise low-contrast and detail-deficient regions, improving detection robustness in foggy scenes. Extensive experiments on RTTS and Cityscapes demonstrate clear and consistent gains: HS-MambaDet outperforms representative one-stage, two-stage, and state-space-based detectors by up to 4.3% in mAP@0.5 and 6.5% in mAP@0.5:0.95, while maintaining competitive inference efficiency, thereby achieving a favorable accuracy-efficiency trade-off for foggy small-object detection.