In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.

The accurate detection and segmentation of concrete cracks are crucial for maintaining the integrity and safety of infrastructure. Traditional manual inspection methods are often constrained by background complexity and environmental noise, while deep learning models face challenges related to data dependency and poor generalization in complex scenarios. To address these issues, this paper proposes an enhanced transFissNet model that integrates a ResNet101 backbone with Transformer modules for self-attention and multi-scale feature extraction. The model improves the robustness of crack detection under varying lighting conditions and irregular crack morphologies. Experimental results on multiple benchmark datasets demonstrate that transFissNet achieves an accuracy of 96.8%, outperforming existing mainstream methods. The proposed approach provides a reliable and scalable solution for automated crack segmentation and contributes to the advancement of intelligent structural health monitoring.

Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network (LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%, which can greatly enhances the tram driving safety.

In recent years, with the continuous promotion of blockchain technology, the application of smart contracts has shown an explosive growth trend, and smart contract vulnerabilities seriously threaten the ecological security of blockchain. Aiming at the inefficiency of existing smart contract Symbolic Execution vulnerability detection technology, this paper proposes an effective smart contract vulnerability detection method at the source code level. Firstly, we define the critical path. As attackers typically aim to steal or freeze funds, we define the path related to fund transfer as the critical path, and its related instructions are the critical instructions. Then, we constructed a smart contract control flowchart based on Ethereum bytecode and used a constraint solver to solve path constraints and corresponding vulnerability constraints. Detect common smart contract vulnerabilities such as reentrancy, access control, arithmetic vulnerabilities, unchecked low calls, and denial of service. The experimental results show that the proposed scheme has good detection performance, and vulnerability detection was performed on 55 smart contracts containing vulnerabilities in the dataset. Compared with the pre optimized scheme, the precision rate of this scheme has been improved by 7.52%, and the total execution time has been reduced by 34.92%.