Underwater images often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network’s capability to extract key features, improvements were made based on the symmetrically structured blind super-resolution generative adversarial network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrated that the proposed method achieved significant improvements in peak signal-to-noise ratio (PSNR) and underwater image quality measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

The positioning error of the GPS method at close distances is relatively large, rendering it incapable of accurately guiding unmanned surface vehicles (USVs) back to the mother ship. Therefore, this study proposes a near-distance recovery method for USVs based on monocular vision. By deploying a monocular camera on the USV to identify artificial markers on the mother ship and subsequently leveraging the geometric relationships among these markers, precise distance and angle information can be extracted. This enables effective guidance for the USVs to return to the mother ship. The experimental results validate the effectiveness of this approach, with positioning distance errors of less than 40 mm within a 10 m range and positioning angle errors of less than 5 degrees within a range of ±60 degrees.

Micro-sandblasting pretreatment was applied to AlTiSiN-coated WC–Co tools to enhance cutting performance in 316 L stainless steel milling. An L₉(3³) Taguchi orthogonal array varied passivation pressure (0.1, 0.2, and 0.3 MPa), gun traverse speed (60, 80, and 100 m/min), and tool rotation speed (20, 30, and 40 r/min). Coating thickness varied only from 0.93 to 1.19 μm, and surface roughness remained within 0.044–0.077 μm, confirming negligible thickness and roughness effects. Under optimized conditions, coating adhesion strength and nano-hardness both exhibited significant improvements. A weighted-scoring method balancing these two responses identified the optimal pretreatment parameters as 0.1 MPa, 80 m/min, and 20 r/min. Milling tests at 85 m/min—using flank wear VBₘₐₓ = 0.1 mm as the failure criterion—demonstrated a cutting distance increase from 4.25 m (untreated) to 12.75 m (pretreated), a 200% improvement. Wear progressed through three stages: rapid initial wear, extended steady wear due to Al₂O₃ protective-film formation and Si-induced oxygen-diffusion suppression, and accelerated wear. Micro-sandblasting further prolonged the steady-wear phase by removing residual cobalt binder, exposing WC grains, and offsetting tensile residual stresses. These findings establish a practical, cost-effective micro-sandblasting pretreatment strategy that significantly enhances coating adhesion, hardness, and tool life, providing actionable guidance for improving the durability and machining performance of coated carbide tools in difficult-to-cut applications.

To understand the vibration characteristics of rubber track system in traveling, this research studied the small harvester installed with rubber track system and the dynamic model reflecting vibration characteristics of rubber track system on the ground was constructed. Comparing analysis results with measured experimental data obtained from vehicle test, it is proved that the dynamic model established by theoretical analysis can correctly and effectively predict actual movement condition and vibration characteristics of rubber track system, especially at low test vehicle speeds. The relative difference between measured data of vibration acceleration obtained from real vehicle tests and the theoretical value was in the range of –1.2 %-+18.2 %. The vibration prediction and analysis method of rubber tracked vehicle was discussed in this study, and important basic data were provided for the research of comfort evaluation of working posture and lightweight design of rubber tracked mechanism.

Addressing the issues of prolonged training times and low recognition rates in large model applications, this paper proposes a weight training method based on entropy gain for weight initialization and dynamic adjustment of the learning rate using the multilayer perceptron (MLP) model as an example. Initially, entropy gain was used to replace random initial values for weight initialization. Subsequently, an incremental learning rate strategy was employed for weight updates. The model was trained and validated using the MNIST handwritten digit dataset. The experimental results showed that, compared to random initialization, the proposed initialization method improves training effectiveness by 39.8% and increases the maximum recognition accuracy by 8.9%, demonstrating the feasibility of this method in large model applications.

Infrared–visible image fusion aims to integrate structural details, natural appearance, and thermal target information from two source modalities, thereby improving visual perception in complex scenes. However, under challenging conditions such as low illumination, noise, low contrast, and overexposure, existing methods often struggle to stably preserve cross-modal shared features (CMSF) while effectively highlighting single-modal specific features (SMSF). In addition, the absence of real fusion labels limits effective supervised learning. To address these issues, this paper proposes a unified semantic-guided fusion network, termed USF-Net, which jointly models the shared and specific features of infrared and visible images under a unified semantic representation and dynamically adjusts the fusion strategy according to imaging contexts. Specifically, the Shared Feature Alignment and Enhancement (SFAE) module is designed to strengthen consistent modeling of common features across modalities, while the Specific Feature Reweighting Fusion (SFRF) module selectively enhances modality-specific features to achieve stable and controllable fusion. Moreover, the constructed real fusion labels are incorporated into the loss function for collaborative training. Experimental results on multiple public datasets demonstrate that USF-Net achieves superior fusion performance under diverse complex imaging conditions.