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China’s rail transit system is developing rapidly, but achieving seamless high-precision localization of trains throughout the entire route in closed environments such as tunnels and culverts still faces significant challenges. Traditional localization technologies cannot meet current demands, and the present paper proposes an autonomous localization method for trains based on pulse observation in a tunnel environment. First, the Letts criterion is used to eliminate abnormal gyro data, the CEEMDAN method is employed for signal decomposition, and the decomposed signals are classified using the continuous mean square error and norm method. Noise reduction is performed using forward linear filtering and dynamic threshold filtering, respectively, maximizing the retention of its effective signal components. A SINS/OD integrated localization model is established, and an observation equation is constructed based on velocity matching, resulting in an 18-dimensional complex state space model. Finally, the EM algorithm is used to address Non-Line-Of-Sight and multipath effect errors. The optimized model is then applied in the Kalman filter to better adapt to the system’s observation conditions. By dynamically adjusting the noise covariance, the localization system can continue to maintain continuous high-precision position information output in a tunnel environment.

As a control structure, the magnetic repulsion device is applied in the high-speed switch hydraulic operating mechanism. It must not only move quickly but also stop precisely. The repulsion disk is subjected to high impact loads, resulting in the phenomenon of fracture and damage. In this paper, the magnetic repulsion value of the engineering prototype was obtained through simulation. A super-elastic material was selected as the buffer, and impact dynamics simulation was carried out. A double-repulsion-disk structure was designed, which reduced the structural impact stress and satisfied the operation time of less than 2 milliseconds. This realized redundant design and improved the reliability of the high-speed switch hydraulic operating mechanism, which is of great significance for the safe operation of high-speed switches.

The strong wind environment causes the additional conductor of the overhead contact system (OCS) of the Lanzhou–Xinjiang high-speed railway to gallop, significantly impacting the safe operation of the train. This paper presents the design of an online monitoring system for the galloping of additional conductors in the OCS, utilizing video monitoring for accurate and real-time assessment. Initially, the dynamics of the OCS additional conductor and its operational environment are examined, leading to the selection of suitable data transmission and power supply methods to finalize the camera configuration. Secondly, a preprocessing method for enhancing images of galloping in OCS additional conductors is developed, effectively reducing noise in edge detection through a region chain code clustering analysis. The video monitoring system effectively extracts wire edges, addressing the issues of splitting, breakage, and edge overlap in edge detection, while accurately identifying wire targets in video images. In conclusion, a galloping monitoring test platform is established to extract galloping data from additional conductors through video monitoring. The analysis of the galloping frequency and amplitude facilitates the comprehensive monitoring and assessment of the galloping status of OCS additional conductors. The video monitoring system effectively extracts and analyzes galloping data of the OCS additional conductor, fulfilling the fundamental requirements for the online monitoring of additional conductor galloping, and possesses significant engineering application value.

Benefiting from their good mechanical and electrical properties, epoxy resin materials are widely utilized in the field of high-voltage electrical insulation devices. However, with the increase in voltage levels of equipment, the epoxy resin materials used for insulating pull rods in high-voltage electrical equipment are facing increasingly severe challenges. This study enhanced the mechanical and insulating properties of epoxy resin materials by molecular structure regulation, composite incorporation and formula optimization. The tensile strength, bending strength and impact strength of the epoxy resin materials with molecular structure regulation increased by 20.6%, 8.5% and 42.1%. The breakdown strength successfully increased from 27.6 kV/mm to 29.9 kV/mm. After combining with the modified Al2O3 nanofillers, the breakdown strength, surface resistivity and volumetric resistivity of the composite further improved to 35.8 kV/mm, 2.7 × 1016 Ω and 5.8 × 1017 Ω·cm. The insulating pull rod prepared by this method achieved a flashover voltage of 18.5 kV, meeting the requirements for both insulating and mechanical performance of a prototype of 200 kV high-voltage direct current floor tank-type high-speed mechanical switch. This study can provide important support for the optimization of epoxy resin material formulation design and the development of epoxy-resin-insulating pull rods.

The Lanzhou-Xinjiang (Lan-Xin) high-speed railway is one of the principal sections of the railway network in western China, and signal equipment is of great importance in ensuring the safe and efficient operation of the high-speed railway. Over a long period, in the railway operation and maintenance process, the railway signaling and communications department has recorded a large amount of unstructured text information about equipment faults in the form of natural language. However, due to irregularities in the recording methods of these data, it is difficult to use directly. In this paper, a method based on natural language processing (NLP) was adopted to analyze and classify this information. First, the Latent Dirichlet Allocation (LDA) topic model was used to extract the semantic features of the text, which were then expressed in the corresponding topic feature space. Next, the Support Vector Machine (SVM) algorithm was used to construct a signal equipment fault diagnostic model that reduced the impact of sample data imbalance on the classification accuracy. This was compared and analyzed with the traditional Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms. This study used signal equipment failure text data from the Lan-Xin high-speed railway to conduct experimental analysis and verify the effectiveness of the proposed method. Experiments showed that the accuracy of the SVM classification algorithm could reach 0.84 after being combined with the LDA topic model, which verifies that the natural language processing method can effectively realize the fault diagnosis of signal equipment and has certain guiding significance for the maintenance of field signal equipment.

This paper studies the distributed adaptive cooperative control of multiple urban rail trains with position output constraints and uncertain parameters. Based on an ordered set of trains running on the route, a dynamic multiple trains movement model is constructed to capture the dynamic evolution of the trains in actual operation. Aiming at the position constraints and uncertainties in the system, different distributed adaptive control algorithms are designed for all trains by using the local information about the position, speed and acceleration of the train operation, so that each train can dynamically adjust its speed through communicating with its neighboring trains. This control algorithm for each train is designed to track the desired position and speed curve, and the headway distance between any two neighboring trains is stable within a preset safety range, which guarantee the safety of tracking operation of multiple urban rail trains. Finally, the effectiveness of the designed scheme is verified by numerical examples.

The occurrence of a partial arc can affect insulation properties of the insulator by different types of flashover. In order to investigate the influence of a partial arc on electric field distribution along the catenary insulator string, a three-dimensional model of the cap-pin insulator string with partial arc was established in this paper. The electric field distribution along the insulator string when the arc extended on the insulator surface and bridged sheds was investigated based on the electric field analysis using the finite element method. The results showed that the occurrence of a partial arc caused obvious distortion of the electric field, which was a two-dimensional axis symmetrical field before arcing to a three-dimensional field. In the case of arc extension, the sudden rise of field intensity was mostly at the rib and the shed edge, which had the local maximum field intensity. The rib and the shed edge played a certain hindrance role in the extension of the arc. The main reason for promoting the development of the arc can be attributed to thermal ionization. In the case of arc bridge sheds, the highest field intensity appeared at the edge of the last bridged shed. As the number of sheds arc-bridged increased, the maximum field intensity also increased. As the arc length increased, the electric field intensity of the arc head also increased, which resulted in an accelerated arc development. The main factor to promote the development of the arc can be attributed to electrical breakdown. The measures to hinder the rapid development of partial arcs were proposed.

Railway signals’ fault text data contain a substantial amount of expert maintenance experience. Extracting valuable information from these fault text data can enhance the efficiency of fault diagnosis for signal equipment, thereby contributing to the advancement of intelligent railway operations and maintenance technology. Considering that the characteristics of different signal equipment in actual operation can easily lead to a lack of fault data, a fault diagnosis method for railway signal equipment based on data augmentation and an improved attention mechanism (DEIAM) is proposed in this paper. Firstly, the original fault dataset is preprocessed based on data augmentation technology and retained noun and verb operations. Then, the neural network is constructed by integrating a bidirectional long short-term memory (BiLSTM) model with an attention mechanism and a convolutional neural network (CNN) model enhanced with a channel attention mechanism. The DEIAM method can more effectively capture the important text features and sequence features in fault text data, thereby facilitating the diagnosis and classification of such data. Consequently, it enhances onsite fault maintenance experience by providing more precise insights. An empirical study was conducted on a 10-year fault dataset of signal equipment produced by a railway bureau. The experimental results demonstrate that in comparison with the benchmark model, the DEIAM model exhibits enhanced performance in terms of accuracy, precision, recall, and F1.

High parking accuracy, comfort and stability, and fast response speed are important indicators to measure the control performance of a fully automatic operation system. In this paper, aiming at the problem of low accuracy of the fully automatic operation control of urban rail trains, a radial basis function neural network position output-constrained robust adaptive control algorithm based on train operation curve tracking is proposed. Firstly, on the basis of the mechanism of motion mechanics, the nonlinear dynamic model of train motion is established. Then, RBFNN is used to adaptively approximate and compensate for the additional resistance and unknown interference of the train model, and the basic resistance parameter adaptive mechanism is introduced to enhance the anti-interference ability and adaptability of the control system. Lastly, on the basis of the RBFNN position output-constrained robust adaptive control technology, the train can track the desired operation curve, thereby achieving the smooth operation between stations and accurate stopping. The simulation results show that the position output-constrained robust adaptive control algorithm based on RBFNN has good robustness and adaptability. In the case of system parameter uncertainty and external disturbance, the control system can ensure high-precision control and improve the ride comfort.

Genome editing has revolutionized modern biotechnology, enabling precise modifications to DNA sequences with far‐reaching applications in medicine, agriculture, and synthetic biology. Recent advancements in artificial intelligence (AI) have significantly enhanced genome editing by improving target selection, minimizing off‐target effects, and optimizing CRISPR‐associated systems. AI‐driven models, such as deep learning‐based predictors and protein language models, enable more accurate sgRNA design, novel Cas protein discovery, and enhanced gene regulatory network analysis. Additionally, AI‐powered tools facilitate large‐scale data integration, accelerating functional genomics and therapeutic genome editing. This review explores the intersection of AI and genome editing, highlighting key innovations, challenges, and future prospects. Despite its transformative potential, AI‐driven genome editing raises ethical concerns regarding data bias, algorithmic transparency, and unintended genetic modifications. Addressing these challenges requires interdisciplinary collaboration between AI researchers, molecular biologists, and policymakers. As AI continues to evolve, its integration with genome editing will pave the way for groundbreaking advancements in precision medicine, genetic disease treatment, and sustainable agriculture. This review explores AI‐driven advancements in genome editing, focusing on CRISPR optimization, enhanced targeting precision, reduced off‐target effects, and novel tool design. It addresses ethical challenges, data biases, and highlights future prospects in precision medicine, agriculture, and synthetic biology.