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Context 1
... interface availability is checked before switching from the initial state towards "ATO available" state of Figure 4, triggering Transition 3 of the table. The sequence diagram of the "sub use-case" of the use case "performing running ATO" (see Figure 2) can be consulted on Figure 11. Then a connection based on telecommunication means shall be established and a functional protocol (with respect to Subset 126) shall be initialized before firing Transition 4 switching towards the state "ATO Ready". ...
Context 2
... figure shows message exchanges triggering the state changes of the ATO on Board system (see Figure 10). Comparing Figure 9 and Figure 11, it appears that "setboardforAutomatic" corresponds to a sequence of messages: "setOn, powerOn, init, connect, SetReady, SetEngaged". In the same time, "SetTrackfor" Automatic is implemented at Level 2 by "setRBCMAnager" and "SetATOTrackOn". ...
Context 3
... order to illustrate the global approach, the corresponding Event-B generation is transformed from the previous SysML models. Integrating the two sequence diagrams corresponding to Figures 11 and 12 into a single one, the following model is built (see Figure 13). In a second step, the Event-B components whose modular design respects the architecture of Figure 5, can be generated (see Listing 1). ...
Context 4
... interface availability is checked before switching from the initial state towards "ATO available" state of Figure 4, triggering Transition 3 of the table. The sequence diagram of the "sub use-case" of the use case "performing running ATO" (see Figure 2) can be consulted on Figure 11. Then a connection based on telecommunication means shall be established and a functional protocol (with respect to Subset 126) shall be initialized before firing Transition 4 switching towards the state "ATO Ready". ...
Context 5
... figure shows message exchanges triggering the state changes of the ATO on Board system (see Figure 10). Comparing Figure 9 and Figure 11, it appears that "setboardforAutomatic" corresponds to a sequence of messages: "setOn, powerOn, init, connect, SetReady, SetEngaged". In the same time, "SetTrackfor" Automatic is implemented at Level 2 by "setRBCMAnager" and "SetATOTrackOn". ...
Context 6
... order to illustrate the global approach, the corresponding Event-B generation is transformed from the previous SysML models. Integrating the two sequence diagrams corresponding to Figures 11 and 12 into a single one, the following model is built (see Figure 13). In a second step, the Event-B components whose modular design respects the architecture of Figure 5, can be generated (see Listing 1). ...
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Citations
... Noise data are generated by incorrect operation when drivers are nervous. This information has a greater impact on a small number of data sets [24], so this part only focuses on a small number of data sets. Considering the classification performance and computational cost, the redundant and noisy data are filtered by the KNN-based method. ...
In a high-speed rail system, the driver repeatedly adjusts the train’s speed and traction while driving, causing a high level of energy consumption. This also leads to the instability of the train’s operation, affecting passengers’ experiences and the operational efficiency of the system. To solve this problem, we propose a variational graph auto-encoder (VGAE) model using a neural network to learn the posterior distribution. This model can effectively capture the correlation between the components of a high-speed rail system and simulate drivers’ operating state accurately. The specific traction control is divided into two parts. The first part employs an algorithm based on the K-Nearest Neighbors (KNN) algorithm and undersampling to address the negative impact of imbalanced quantities in the training dataset. The second part utilizes a variational graph autoencoder to derive the initial traction control of drivers, thereby predicting the energy performance of the drivers’ operation. An 83,786 m long high-speed train driving section is used as an example for verification. By using a confusion matrix for our comparative analysis, it was concluded that the energy consumption is approximately 18.78% less than that of manual traction control. This shows the potential and effect of the variational graph autoencoder model for optimizing energy consumption in high-speed rail systems.
