The power supply from urban power network (UPN) ensures the proper operation of the urban metro network (MN). However, due to the close mutual connections, the negative impact of a power outage can propagate to the MN through a traction power network (TPN). Therefore, it is pressing to assess the resilience of such coupled networks. This paper initially proposes a multi-layer power-metro interdependent network (PMIN) model by integrating physical characteristics of UPN, TPN and MN into their topology networks. In the PMIN, the interdependence of UPN, TPN and MN is established in a hierarchical manner, where the community-based TPN model is practically elaborated to bridge fault propagation from the disabled UPN the to MN. Next, the traditional MN is equivalently converted into a mission-oriented network to simulate passenger rerouting processes, enabling the exploration of rerouting-incurred cascading failure with three proposed failed load redistribution disciplines. Finally, the resilience curves and assessment metrics are proposed to evaluate the resilience of the PMIN, with consideration of coupling effects among the UPN, TPN, and MN. The proposed method is applied to a quasi-authentic PMIN to reveal the fault propagation mechanisms and vulnerability characteristics of critical components within the PMIN, ultimately contributing to resilience enhancement.

Abstract Cyber-physical systems (CPSs) are used in many safety-critical areas, making it crucial to ensure their safety. However, with CPSs increasingly dynamically deployed and reconfigured during runtime, their safety analysis becomes challenging. For one thing, reconfigurable CPSs usually consist of multiple agents dynamically connected during runtime. Their highly dynamic system topologies are too intricate for traditional modeling languages, which, in turn, hinders formal analysis. For another, due to the growing size and uncertainty of reconfigurable CPSs, their system models can be huge and even unavailable at design time. This calls for runtime analysis approaches with better scalability and efficiency. To address these challenges, we propose a scenario-based hierarchical modeling language for reconfigurable CPS. It provides template models for agent inherent features, together with an instantiation mechanism to activate single agent’s runtime behavior, communication configurations for multiple agents’ connected behaviors, and scenario task configurations for their dynamic topologies. We also present a path-oriented falsification approach to falsify system requirements. It employs classification-model-based optimization to explore search space effectively and cut unnecessary system simulations and robustness calculations for efficiency. Our modeling and falsification are implemented in a tool called . Experiments have shown that it can largely reduce modeling time and improve modeling accuracy, and perform scalable CPS falsification with high success rates in seconds.

Control-CPSs are usually safety or mission critical, hence they demand thorough debugging. As nowadays control-CPSs reaching millions of lines of source code, traditional human-flesh debugging is no longer sufficient. We need automated software fault localization (SFL) to assist the debugging. In automated SFL, automatically generated test cases are fed to the control-CPS (or the simulator of the control-CPS), to generate thousands of cyber-subsystem code traces and physical-subsystem trajectories. Next, another automated program, aka oracle, is needed to label the correctness of these physical-subsystem trajectories (and hence cyber-subsystem code traces), even without knowing if there is a bug in the cyber-subsystem. Control-CPS oracle design is a known hard problem. To our best knowledge, AR-SI oracle (denoted as AO in the following) is the most widely adopted control-CPS oracle so far. On the other hand, recently, transformer emerges as a major game changer in the domain of time series prediction. As AO is also time series prediction based, people naturally wonder if transformers can also be used as control-CPS oracles; and if so, can it outperform AO. In this paper, we answer this question by comparing AO with an intuitive design of transformer control-CPS oracle (simplified as TO in the following). Our comparison results show that in terms of SFL accuracy and latency, the TO does not significantly outperform the AO; in terms of false positive rate, the AO performs significantly better; and in terms of false negative rate, the TO performs significantly better.

As the ground objects become increasingly complex, the classification results obtained by single source remote sensing data can hardly meet the application requirements. In order to tackle this limitation, we propose a simple yet effective attention fusion model based on Disentangled Non-local (DNL) network for hyperspectral and LiDAR data joint classification task. In this model, according to the spectral and spatial characteristics of HSI and LiDAR, a multiscale module and a convolutional neural network (CNN) are used to capture the spectral and spatial characteristics respectively. In addition, the extracted HSI and LiDAR features are fused through some operations to obtain the feature information more in line with the real situation. Finally, the above three data are fed into different branches of the DNL module, respectively. Extensive experiments on Houston dataset show that the proposed network is superior and more effective compared to several of the most advanced baselines in HSI and LiDAR joint classification missions.

Automatic demand side response is well applied and popularized with the development of active power distribution network technology. In this paper, with the specific condition of urban commercial centers considered, the generalized demand side resources co-optimization is presented here including load side resources and energy storage resources. Firstly, for the normal operation of power systems, this paper considers the commercial users' requirements for temperature and response convenience. In addition, for the fault status of power systems, the coordinated operation of energy storage and incentive-based demand side response is conducted to lower the impact of power grid failure for important load, improving the line load rate determined by safety limit. The tabu search-particle swarm optimization is primarily used for the optimization calculation. Finally, the improved IEEE-33 distribution system is adopted as study case for the simulation. What's more, the impact on BESS considering the demand side resources allocation and PV permeability and the influence of resource allocation in demand side on limit line road rate is also analyzed.