Train control Center (TCC) is the hub of the information interaction of the column control system. It is the core system that ensures the safety of railway driving and ensure transportation efficiency. The traditional...
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ISBN:
(数字)9798331506797
ISBN:
(纸本)9798331506803
Train control Center (TCC) is the hub of the information interaction of the column control system. It is the core system that ensures the safety of railway driving and ensure transportation efficiency. The traditional manual test method has many problems, such as high test cost, long time-consuming and low efficiency, and great influence of human subjective factors. This paper uses computer simulation technology to build a simulation test system. By analyzing the information interaction between the column control center and other devices, the computer simulation technology is used to build the simulation test system simulation control system operation. This system can intuitively reflect the performance of the column control system, which is convenient to optimize and adjust the design scheme, make theoretical guidance for the establishment of the real control system. According to the functional test requirements of TCC, this paper uses Colored Petri Net (CPN) to establish a hierarchical model for the simulation test system of the train control center. Taking the temporary speed limit scene test as an example, the formal modeling of the simulation test system through CPN can avoid the ambiguity caused by the specification description, improve the timeliness and safety of the system, help to find the problems that may occur in the design specification, manufacturing and operation of the system, and provide some reference for the design of the real train control system.
Asymptotic phase and amplitudes are fundamental concepts in the analysis of limit-cycle oscillators. In this paper, we briefly review the definition of these quantities, particularly a generalization to stochastic osc...
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Physics-Informed Neural Networks (PINNs) have emerged as powerful tools for integrating physics-based models with data by minimizing both data and physics losses. However, this multi-objective optimization problem is ...
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This paper investigates the safe platoon formation tracking and merging control problem of connected and automated vehicles (CAVs) on curved multi-lane roads. The first novelty is the separation of the control designs...
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Best-performing speech models are trained on large amounts of data in the language they are meant to work for. However, most languages have sparse data, making training models challenging. This shortage of data is eve...
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This paper investigates the optimal control problem for an unknown linear time-invariant (LTI) system. To solve this problem, a novel composite policy iteration (CPI) algorithm based on adaptive dynamic programming is...
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Oblique photogrammetry using unmanned aerial vehicles (UAV) is crucial to 3D scene reconstruction. Nevertheless, oblique images are generally acquired with large overlaps, and sometimes with multiple views. Although t...
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Oblique photogrammetry using unmanned aerial vehicles (UAV) is crucial to 3D scene reconstruction. Nevertheless, oblique images are generally acquired with large overlaps, and sometimes with multiple views. Although this increases the level of data completeness, it introduces additional and sometimes redundant computations in tie-point matching, creating overly dense camera connections in the pose graph for bundle adjustment (BA). This study optimizes the pose graph of oblique UAV images by removing redundant image connections to guide tie-point matching. Assuming a five-camera system for oblique image collection, a pose graph called a topologically connected camera network (TCN) was initially constructed using position and orientation system (POS) data to determine the spatial connectivity among oblique images. Second, five geometric meta-parameters of overlapping images were constructed, and their influence on tie-point matching was analyzed using a data-driven approach to generate a weighted pose graph. Third, the weighted pose graph was simplified to a degree-bounded skeletal camera network (D-SCN) using the proposed two-stage multi-objective graph optimization approach. Finally, the D-SCN was embedded into a structure from motion (SfM) pipeline to produce a novel D-SCN–SfM method to reduce the required computations for tie-point matching. The proposed D-SCN-SfM method was tested using data from three large sites, each containing over 5,000 images. In addition, the D-SCN-SfM method was compared with three state-of-the-art methods. The experimental results indicate that our method can significantly reduce the required computations for tie-point matching to 1/14–1/20 as compared to a method that uses only topological constraints, that is, TCN, saving 89–92% of the time expenditure. Furthermore, the accuracy and completeness of the 3D geometry produced by the proposed method were comparable to those produced by standard SfM methods. The source code of our approach
In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property ...
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The development of new products and services allows railway transport to constantly increase the fleet of specialised wagons, which, in comparison with the universal rolling stock, have the best technical and economic...
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Practical stabilization of input-affine systems in the presence of measurement errors and input constraints is considered in this brief note. Assuming that a Lyapunov function and a stabilizing control exist for an in...
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