Systems Theoretical Accident Model and Process (STAMP), which considers system safety as an emergent property of the system, is a more effective accident/loss causality model for modern complex systems. Based on STAMP, System Theoretical Process Analysis (STPA) has attracted increasing attention as a new approach to hazard analysis, and relevant international standards are being developed. However, STPA is mainly performed manually, leading to inefficiencies, and constructs models in non-standard language, hindering the integration with existing systems engineering. STPA-SN (STPA based on SysML/MARTE and NuSMV) is proposed to build model in SysML, describing the timing with MARTE (Modeling and Analysis of Real-Time and Embedded Systems), transform SysML model into NuSMV model and output loss scenarios automatically with model checker. An application example of STPA-SN is provided to demonstrate potentials for higher efficiency of analysis and for collaboration with SysML-based systems engineering.

This paper proposes an accurate short-term prediction model of bike-sharing demand with the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a low-carbon, green and healthy way of transportation. However, the explosive growth and free-form development of bike-sharing has also brought about a series of problems in the area of urban governance, creating a new opportunity and challenge in the use of a large amount of historical data for regional bike-sharing traffic flow predictions. In this study, we built an accurate short-term prediction model of bike-sharing demand with the bike-sharing dataset from 2015 to 2017 in London. First, we conducted a multidimensional bike-sharing travel characteristics analysis based on explanatory variables such as weather, temperature, and humidity. This will help us to understand the travel characteristics of local people, will facilitate traffic management and, to a certain extent, improve traffic congestion. Then, the explanatory variables that help predict the demand for bike-sharing were obtained using the Granger causality with the entropy theory-based MIC method to verify each other. The Multivariate Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model were integrated to build the prediction model, and this is abbreviated as the TCN-GRU model. The fitted coefficient of determination R2 and explainable variance score (EVar) of the dataset reached 98.42% and 98.49%, respectively. Meanwhile, the mean absolute error (MAE) and root mean square error (RMSE) were at least 1.98% and 2.4% lower than those in other models. The results show that the TCN-GRU model has strong efficiency and robustness. The model can be used to make short-term accurate predictions of bike-sharing demand in the region, so as to provide decision support for intelligent dispatching and urban traffic safety improvement, which will help to promote the development of green and low-carbon mobility in the future.

This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as the voltage of the generator, angle of attack, and position of the rudder surface. A UAV is a typical complex system, and its flight data are typical high-dimensional large sample data sets. In practical applications such as UAV fault detection, the fault data only appear in a small part of the data sets. In this study, representation learning is used to extract the normal features of the flight data and reduce the dimensions of the data. The normal data are used for the training of the Auto-Encoder, and the reconstruction loss is used as the criterion for fault detection. An Improved Auto-Encoder suitable for UAV Flight Data Sets is proposed in this paper. In the Auto-Encoder, we use wavelet analysis to extract the low-frequency signals with different frequencies from the flight data. The Auto-Encoder is used for the feature extraction and reconstruction of the low-frequency signals with different frequencies. To improve the effectiveness of the fault localization at inference, we develop a new fault factor location model, which is based on the reconstruction loss of the Auto-Encoder and edge detection operator. The UAV Flight Data Sets are used for hard-landing detection, and an average accuracy of 91.01% is obtained. Compared with other models, the results suggest that the developed Self-supervised Fault Detection Model for UAVs has better accuracy. Concluding this study, an explanation is provided concerning the proposed model’s good results.

The paper aims to build a traffic prediction model for online car-hailing demand with Improved LSTM and Transformer. There are many factors that affect demand, such as temporal features, spatial features, high signal-to-noise ratio, and so on. In this study, LSTM and Transformer are used to extract the temporal and spatial features of data. The temporal and spatial features are used for bagging ensemble learning to predict the online car-hailing orders. An improved LSTM suitable for Traffic data sets is proposed in this paper. In LSTM, we use wavelet decomposition and reconstruction to extract the small-amplitude high-frequency signal from data. The extracted signal is translated and superimposed into the results of the model. The average MAE of 21.24 is obtained on Online Car-hailing Information Data Sets. Compared with other methods, the results suggest the proposed traffic demand prediction model has better accuracy.