Tangshan College

With the continuous increase in the speed of high-speed trains (HSTs), traditional friction braking no longer meets the requirement for rapid stopping over short distances. Wind brake devices (WBDs) serve as a crucial supplementary mechanism to enhance braking performance. Therefore, investigating methods to improve their aerodynamic braking force under multifactor influences holds significant importance. This study presents a hybrid surrogate model (HSM) with dynamic weighting based on local error evaluation, which integrates the modeling characteristics of multiple classical surrogate models. The proposed model fulfills the fitting accuracy and adaptability requirements of surrogate models in the optimization of WBDs. Initial sampling points are generated using Optimal Latin Hypercube Sampling (OLHS), and their corresponding geometries are created through the Morph mesh deformation method. The responses of these sampling points are obtained using computational fluid dynamics (CFD) simulations. The accuracy of the numerical calculation method is validated through scaled wind tunnel experiments. Then, the NSGA-II algorithm is employed for optimization. The results indicate that the drag of the head car equipped with WBDs increases by 11.2%, while the lift decreases by 21%. Analyses of the flow field and pressure distribution further demonstrate that the optimized WBDs attenuate the large windward vortex, expand and intensify the leeward low-pressure region, and modify vortex morphology to raise flow separation and reattachment, collectively enhancing braking force and reducing lift. The application of the HSM considerably improves computational accuracy and efficiency.

This paper proposes a fully virtual driving teaching system for high-speed EMUs based on a modular architecture. With virtual driving software as its core, the system integrates five standardized modules—train model, control logic, environmental scenario, training management, and data processing—enabling multi-train model switching, multi-route simulation, and multi-scenario dynamic simulation. It represents a specific application of intelligent transportation technology in the field of professional training. By integrating advanced technologies such as computer simulation, virtual reality, and artificial intelligence, the system can conduct practical training on equipment operation, train operation control, and fault handling. It features a highly immersive visual scene and a scientific assessment mechanism, demonstrating the in-depth integration of computer technology in modern education. This system provides an efficient, safe, and intelligent teaching solution for the cultivation of rail transit talents, and also offers a practical path for the coordinated development of intelligent transportation and vocational education.

Underwater images typically exhibit a bluish-green tone due to the absorption and scattering effects of water, especially in deep-water environments, where red wavelengths are easily absorbed, leading to the loss of red channel information. This, in turn, affects the image's color and detail. To address this issue, this paper proposes a GAN-based model enhanced by the red channel. The model utilizes a U-Net autoencoder to extract features from degraded underwater images. Additionally, considering the unique characteristics of the underwater environment, a red channel feature enhancement network is designed. This network takes the red channel of the degraded image as input and, through an attention module, focuses on severely degraded regions where the red channel prior cannot be satisfied, thereby assisting the autoencoder in image reconstruction. Experimental results demonstrate that, when tested on the UIEB dataset, the proposed algorithm outperforms the compared models in terms of PSNR and SSIM, achieving 25.26 dB and 0.90, respectively. The model effectively removes the bluish-green background from degraded images and restores their color. Further, the model's practical feasibility is verified through generalization testing, feature point matching, and edge detection application analysis.