International Journal of Software Engineering and Knowledge Engineering

Train delay is a serious issue that can spread rapidly in the railway network leading to further delay of other trains and detention of passengers in stations. However, the current practice in the event of the trail delay usually depends on train dispatcher’s experience, which cannot manage train operation effectively and may have safety risks. The application of intelligent railway monitor and control system can improve train operation management while increasing railway safety. This paper presents a methodology in which train timetabling, platforming and routing models are combined by studying the real-time adjustment and optimization of high-speed railway in the case of the train delay in order to produce a cooperative adjustment algorithm so that the train operation adjustment plan can be obtained. MATLAB computer programs have been developed based on the proposed methodology and adjustment criteria have been established from knowledge data bases in order to calculate optimized solutions. A case study is used to demonstrate the proposed methodology. The results show that the proposed method can quickly adjust the train operation plan in the case of the train delay, restore the normal train operation order, and reduce the impact of train delay on railway network effectively and efficiently.

Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed to predict the recovery time of train primary-delay based on the real records from High-Speed Railway (HSR). In Stage 1, two models are built to study the influence of feature space and model framework on the prediction accuracy of buffer time in each section or station. It is found that explicitly inputting the attribute features of stations and sections to the model, instead of implicit simulation, will improve the prediction accuracy effectively. For validation purpose, the proposed model has been compared with several alternative models, namely, Logistic Regression (LR), Artificial Neutral Network (ANN), Support Vector Machine (SVM) and Gradient Boosting Tree (GBT). The results show that its remarkable performance is better than other schemes. Specifically, when the error is extended to 3min, the proposed model can achieve up to the accuracy of 94.63%. It proves that our method has high value in practical engineering application. Considering the delay propagation of trains is a complex process, our future study will focus on building delay propagation knowledge base and dispatcher experience knowledge base.

The catenary support device inspection is of crucial importance for ensuring safety and reliability of railway systems. At present, visual detection tasks of catenary support devices defect are performed by trained personnel based on the images taken periodically by industrial cameras installed on inspection vehicle in a limited period of time at midnight. However, the inspection mean is inappropriate for low efficiency and high cost. This paper presents a novel network based on unmanned aerial vehicle (UAV) images for catenary support device inspection and focuses on small object detection and the imbalanced dataset. With regards to the first aspect, based on a pyramid network structure, the improved Faster R-CNN consists of a top-down-top feature pyramid fusion structure, which heavily fuses high-level semantic information and low-level detail information. The feature map fusions of three different pooling scales are employed for improving detection accuracy of predicted bounding boxes. With regards to the second, we copy and paste the small proportion objects of dataset for avoiding category imbalance. Finally, quantitative and qualitative evaluations illustrate that the improved Faster-RCNN achieves better performance over the classic methods, yet remains convenient and efficient.

In China, the turnout abnormality very easily causes traffic accident or affects the efficiency due to the operating environment of railway transportation. The existing monitoring means are relatively backward, and mature automatic diagnosis method is lacking. In this study, a method based on semi-supervised learning algorithm for abnormal state diagnosis of turnout action curve is proposed, which is used to analyze and extract the electrical characteristics of the turnout by using the turnout action curve and the static and dynamic properties collected by the railway centralized monitoring system. The support vector machine model is used to construct the initial classifier with a small number of labeled samples, and the labeled samples are expanded from a large number of unlabeled samples. The switch curve is analyzed and diagnosed by using unlabeled data with a small amount of labeled data. The experimental results show that the method can automatically diagnose turnout electrical characteristics with high accuracy. While the cost of this method is relatively low compared with supervised learning, it can achieve higher accuracy and improve the practicability of fault diagnosis of turnout.

The deployment change of SaaS (Software as a Service) software will influence its response time, which is an important performance metric. Therefore, studying the impact of deployment change on the response time of SaaS software could contribute to performance improvement of the software. However, there are few performance analysis methods which can directly analyze the relationship between deployment change and response time of SaaS software. In this paper, we propose an approach which provides the impact analysis of specific deployment change operations on response time of SaaS software explicitly. Specifically, we present an evaluation method for the response time of SaaS software in specific deployment scheme by leveraging queueing theory. With mathematical derivation based on the proposed evaluation method, we qualitatively analyze the variation trend of response time with respect to deployment change. Furthermore, we study the relationship between two specific types of deployment change operations and response time variation of SaaS software, which is used to propose a response time improvement method based on deployment change. Finally, the effectiveness of the analysis conclusions and the proposed method in this paper is validated by practical cases, which indicates that adjusting deployment scheme according to the conclusions obtained in this paper can be helpful in improving the response time of SaaS software.

Trace links between requirements and software artifacts provide available traceability information and in-depth insights for different stakeholders. Unfortunately, establishing requirements trace links is a tedious, labor-intensive and fallible task. To alleviate this problem, Information Retrieval (IR) methods, such as Vector Space Model (VSM), Latent Semantic Indexing (LSI), and their variants, have been widely used to establish trace links automatically. But with the widespread use of agile development methodology, artifacts that can be used to generate automatic tracing links are getting shorter and shorter, which decreases the effects of traditional IR-based trace link generation methods. In this paper, Biterm Topic Model–Genetic Algorithm (BTM–GA), which is effective in managing short-text artifacts and configuring initial parameters, is introduced. A hybrid method VSM$+$BTM–GA is proposed to generate requirements trace links. Empirical experiments conducted on five real and frequently-used datasets indicate that (1) the hybrid method VSM+BTM$-$GA outperforms the others, and its results can achieve the “Good” level, where recall and precision are no less than 70% and 30%, respectively; (2) the performance of the hybrid method is stable and (3) BTM–GA can provide a number of “hard-to-find” trace links that complement the candidate trace links of VSM.