In recent years, while the rapid development of global electric vehicles (EVs) has been driving energy transition, their large-scale integration into power grids has posed significant challenges to the stability of power systems and the reliability of electricity supply. Vehicle-to-Grid (V2G) technology demonstrates potential in balancing grid peak-valley differences and facilitating renewable energy integration, thereby effectively mitigating issues caused by disorderly grid integration of massive EVs. This paper proposes a “Vehicle-to-Station-to-Grid” (VSG) aggregated system based on V2G technology. In this system, the multi-state stochastic power output and demand of EVs are considere... More >

As engineering systems grow in scale and complexity, the classical consecutive k-out-of-n model has evolved significantly to address the challenges of component dependence and dynamic maintenance. This paper presents a comprehensive survey of these advancements, categorized into model extensions and assessment methodologies. We synthesize key variants adapted for practical scenarios, including consecutive k-within-m-out-of-n systems, m-consecutive-k-out-of-n systems, (n, f, k) and ⟨n, f, k⟩ systems, sparse $d$ systems, and complex models accounting for component dependence and repair mechanisms. Regarding reliability assessment, this paper evaluates the transition from analytical methods... More >

To meet the efficiency and economy requirements of unmanned aerial vehicle (UAV) inspection for large watershed, long-distance and multi-measurement-point reservoirs, this paper focuses on the optimal allocation problem of heterogeneous UAV swarms for reservoir inspection with task decomposition. Firstly, the four decomposable dimensions of reservoir inspection tasks (spatial decomposition, time decomposition, measurement point decomposition, and data accuracy decomposition) are clarified, and the performance metrics and constraints of heterogeneous UAV swarms are defined. Secondly, inspection success models and inspection cost models are constructed to form a dual-objective optimization mod... More >

To improve the prediction accuracy of slope stability and prevent slope failure accidents, this study proposes a slope stability prediction model based on an improved pelican optimization algorithm optimized random forest (Improved Pelican Optimization Algorithm optimized Random Forest, IPOA-RF). First, according to 431 slope cases, the slope height, slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio were selected as the main predictive features. Second, due to the issue of excessive hyperparameters in the traditional random forest (RF) model, the IPOA algorithm was employed to optimize the RF parameters using an optimal-guidance strategy, mutation op... More >

The rapid expansion of the low-altitude economy is reshaping urban transportation systems, while large-scale operations of dynamic and high-density low-altitude unmanned aerial vehicles pose significant challenges to performance and robustness of airspace infrastructure. Traditional free-flight and point-to-point paradigms have revealed inherent limitations in conflict resolution and congestion mitigation, making the construction of adaptive, structured, and dynamic low-altitude air route networks a critical pathway to achieve both route controllability and efficient operation. However, existing design approaches may struggle to rapidly adapt to dynamic flight requirements while ensuring hig... More >