High-speed railway catenaries are vital components in railway traction power supply systems. To ensure stable contact between the pantograph and the catenary, droppers are positioned between the messenger wire and contact line. The failure of one or more droppers will affect the power supply of the catenary and the operation of the railway. In this paper, we modify the You Only Look Once version five (YOLOv5) model in several ways and propose a method for improving the identification of dropper status and the detection of small defects. Firstly, to focus on small target features, the selective kernel attention module is added to the backbone. Secondly, the feature graphs of different scales extracted from the backbone network are fed into the bidirectional feature pyramid network for multiscale feature fusion. Thirdly, the YOLO head is replaced by a decoupled head to improve the convergence speed and detection accuracy of the model. The experimental results show that the proposed model achieves a mean average precision of 92.9% on the dropper dataset, an increase of 3.8% over the results using YOLOv5s. The detection accuracy of small dropper defects reaches 79.2%, representing an increase of 10.8% compared with YOLOv5s and demonstrating that our model is better at detecting small defects.

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system.

Tracking control is a very challenging problem in the Networked Control System ( NCS ), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-based design approach for the Networked Tracking Control System ( NTCS ). The method utilizes the input-output data of the controlled process to establish a predictive model with the help of Fuzzy Cluster Modelling ( FCM ) technology. Then, the deduced error and error change in the future are treated as inputs of a Fuzzy Sliding Mode Controller ( FSMC ) to obtain a string of future control actions. These candidate control actions in the controller side are delivered to the plant side. Thus, the network induced time delays are compensated by selecting appropriate control action. Simulation outputs prove the validity of the proposed method.

Purpose: The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.Design/methodology/approach: A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.Findings: The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.Originality/value: MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

Terrain recognition technology plays a key role in enhancing autonomous mobility for Quadruped robot in off-road environments. However, feature extraction and classification algorithm are the key to accuracy and efficiency of the terrain recognition. Regarding the characteristics of different terrain surface properties and structures, it gets low-dimensional and high dimensional characteristics by texture features and wavelet transform and uses them as training features of classifier. Then its efficiency is not high and convergence speed is slow for traditional learning algorithm, which is difficult to meet the requirements. So the extreme learning machine is used to classify the terrain pictures collected by robot in real time. Experimental results show that the accuracy of extreme learning machine terrain classification is higher than the traditional neural network algorithm and the support vector machine, and algorithm efficiency is raised more than several times for the sample size of 6000, which meets the requirements for accuracy, especially for real time.