Due to increasing environmental concerns regarding urban transit systems, the specific operating characteristics of metro trains and the rules of regenerative braking energy recycling were studied in this paper to relieve environmental stress. Based on integrated research on trajectory and operation time, we considered complex operation routes mixed with ramps and detours, which also caused a complicated situation of overlap time, to better fit the actual and more efficient situation. A complex new model combined with a matrix control algorithm was proposed in this study. This model overcomes the lack of matching opportunity for overlap time as well as the precocity and instability of the genetic algorithm. In addition, the search ability of the model in the solution space is more comprehensive. In the interest of achieving minimum energy consumption, the objective function was set to minimize the total energy. Taking the Chongqing Metro Line 1 as a numerical example, the energy consumption results show that the energy saving method is effective and has a practical advantage. Then, a comparison of different models using the index of renewable utilization ratio as the indicator shows that the proposed model has a superior potential for energy conservation.

Bearing plays a significant role in the transmission of traction forces and safe operation of train. Affected by the actual operating conditions of the train, it is of great significance to ensure the accurate diagnosis and classification of train bearing faults under strong noise background. An intelligent bearing fault diagnosis method based on the improved sooty tern optimization algorithm to optimize the variational mode decomposition (ISTOA-VMD) and the Squeeze-and-Excitation deep convolutional neural network with wide first-layer kernels (SE-WDCNN) is proposed. Firstly, an improved sooty tern optimization (ISTOA) is proposed by introducing the nonlinear convergence strategy and dynamic weight strategy, and the parameters of VMD are optimized by ISTOA. Furthermore, the VMD combined with sample entropy is used to reconstruct and denoise the signal. Finally, SE-WDCNN is proposed by fusing Squeeze-and-Excitation block, and the reconstructed signal is input into SE-WDCNN for automatic feature extraction and fault recognition. The experimental results show that the proposed method has significant effects on fault diagnosis tasks in different noise environments.

At present, the use of underwater robots to replace underwater manual work is a future development direction. The complex and changeable underwater environment brings great difficulties to the operation of robots. In order to improve the problem of color distortion and degradation of sea cucumber images and to increase the accuracy and stability of sea cucumber detection, this letter proposes a data-driven multi-scale convolutional hybrid attention residual enhancement network (MCRNet). Firstly, a gain factor that considers the differences of each color channel is used to neutralize the color bias of underwater images. Secondly, a multi-scale convolutional fusion module is proposed to extract shallow feature information at different scales. Multiple residual blocks and hybrid attention module are constructed to focus on extracting deep features in the image. The overall residual connectivity of the network helps to retain the shallow information. The experimental results show that compared with the unprocessed four types of sea cucumber images, the method proposed in this letter improves 63.96% and 44.50% in the image quality evaluation metrics UCIQE and UIQM, respectively, and the average detection accuracy of sea cucumbers is improved by 4.7%.

As an efficient mode of public transportation, urban rail transit promotes sustainable development by reducing energy consumption and improving service quality. Numerous studies have been conducted on this subject. However, many existing methods struggle to adapt to environmental changes due to the complexity of actual line conditions. Therefore, based on deep deterministic policy gradient (DDPG) and genetic algorithm (GA), a new optimization algorithm GA-DDPG is proposed in this paper. This algorithm utilizes DDPG for continuous action control of trains and employs GA to optimize the noise generation hyperparameter of DDPG, thereby enhancing the learning and adaptive capabilities of the algorithm. Firstly, a reference system is established based on expert knowledge to ensure safe train operation and guide the training of the algorithm model. Then, under the intervention of reference system, the GA-DDPG is used to optimize and adjust the train operation control strategy to achieve a balance in punctuality, energy efficiency, and comfort. Finally, simulation experiment shows that GA-DDPG achieves at least 5.2% energy savings and 51.2% passenger comfort compared to DDPG. More experiments further demonstrate that GA-DDPG possesses better adaptability and robustness than DDPG when the environment changes.