This work proposes an auto-segment control system with multiple transmitters and dual receivers for dynamic inductive power transfer (DIPT) charging. The current of the transmitting coil precisely coupled with the receiver can be enhanced automatically, while the transmitting current will be suppressed when the transmitter is uncoupled with the receiver. Therefore, the electromagnetic interference (EMI) and power loss generated by uncoupled transmitters can be reduced without position detection and switching control. Each receiver includes a power coil and an impedance coil. When the vehicle moves along the road, the impedance coils are utilized to adjust the resonant state of the transmitter, and the power coils operate alternately to maintain a high spatially average power. The relative position and size between transmitters and receivers directly impact the power transfer performance. Therefore, several critical parameters of the magnetic coupler are analyzed and designed to obtain a considerable output power. An around 300W experimental prototype is established to verify the feasibility of the proposed magnetic coupler and design method. Compared to the auto-segment control system with a single receiver, the spatially average power of the proposed system is improved by twice, and the maximum efficiency is 84.9% in a moving period.

Bidirectional leader discharges initiated from aircraft determine the lightning strike points on aircraft. Understanding the physics underlying the bidirectional leader discharges is fundamental to designing lightning protection systems for aircraft. However, the physical processes of bidirectional leader discharges are still unclear. To deepen the understanding of bidirectional leader discharges, the bidirectional leader discharges initiated from the floating conductors are produced in a laboratory. The development processes of the bidirectional leader are recorded by a high-speed camera with up to 504,000 frames per second. The high-speed camera frames and the applied voltage are synchronized in time. Then the initial thresholds of key processes in the bidirectional leader discharges are obtained. The influence of background electric field rise rate and electrode curvature radius on the bidirectional leader discharges are further studied. It is found that the higher the background electric field rise rate, the easier it is for the bipolar discharge to initiate, and the inception condition of positive leader is independent of the electrode curvature radius (diameter in the range of 20-50mm), as is the case for the negative streamer. Finally, a criterion for determining the initiation condition of the bipolar discharge is proposed, and this criterion is validated based on previous instrument aircraft measurement results. This work can serve as a physical basis to support the numerical model for aircraft zoning.

Purpose: The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.Design/methodology/approach: In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.Findings: Based on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.Originality/value: With the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.

Accurate regional lightning-occurrence prediction is important for operational weather-risk management, but its development is challenged by the severe class imbalance of grid-hour lightning samples. This study proposes a repeated random undersampling (RUS) stacking ensemble that combines heterogeneous machine-learning models and produces probabilistic lightning-occurrence forecasts using atmospheric variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5), together with spatial and temporal predictors. The effects of the undersampling ratio, ensemble size, and positive-class weighting were systematically evaluated, and a configuration with a RUS ratio of 1:15, 20 ensemble members, and a positive-class weight of 2 was selected. Using an operational threshold of 0.591 selected exclusively on the 2024 validation set, the final model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.945, an area under the precision–recall curve (PR-AUC) of 0.235, a probability of detection (POD) of 0.521, and an F1-score of 0.302 on the independent 2025 test set. Physical-variable-group ablation and aggregated TreeSHAP (tree-based Shapley additive explanations) analyses were further conducted to interpret the predictions. Removing the spatiotemporal predictors produced the largest reduction in performance, followed by removing cloud and microphysical variables. Both the Light Gradient Boosting Machine (LightGBM) and categorical boosting (CatBoost) models consistently identified longitude, latitude, total-column cloud ice water, and convective available potential energy as the leading predictors, while higher cloud-ice-water content and stronger convective-instability indices generally shifted model outputs towards lightning occurrence. Direct transfer of the ERA5-trained ensemble to corresponding ECMWF forecast fields without retraining retained useful predictive skill and substantially outperformed the operational ECMWF lightning product over the collocated evaluation samples, although performance degradation indicated a cross-dataset distribution shift. These results demonstrate the value of combining imbalance-aware ensemble learning with physically interpretable predictors for regional lightning-risk forecasting, while the strong influence of geographic variables indicates that external validation and local recalibration or retraining are required before application to other regions.

Lightning poses localized risks to signaling, communication, and traction-power systems along high-speed railways, yet most nowcasting products are generated on regular grids rather than operational line segments. This study developed a grid-to-segment machine-learning framework for next-hour lightning warning along the Jinan West–Tai’an corridor. Ground-based lightning observations, hourly ERA5 fields, temporal variables, and engineered historical lightning features and spatial-neighborhood features were organized on a 0.25° grid. Data from 2014 to 2018 were used for training, 2019 for validation, and 2020 for independent testing. Grid probabilities were converted into warnings for six railway segments using 10 km buffers and maximum-probability aggregation. In 2020, the full extreme gradient-boosting (XGBoost) model, a tree-based ensemble-learning algorithm, achieved grid-level probability of detection (POD), false-alarm ratio (FAR), and critical success index (CSI) values of 0.55, 0.50, and 0.36; segment-level verification yielded 0.60, 0.39, and 0.43. To examine transfer to forecast-driven application, the trained model and threshold were fixed, and ERA5 meteorological inputs were replaced by short-lead ECMWF HRES forecasts for July 2025. POD decreased from 0.85 to 0.80 and CSI from 0.60 to 0.57, while FAR remained nearly unchanged. Under a predefined non-zero rule, HRES litota1 achieved 0.62, 0.71, and 0.25. ML-HRES therefore showed higher CSI and lower FAR than the direct litota1 baseline.

In wide input range and high step-down application, conventional Sigma converter suffers from the problem of small duty cycle and low efficiency of the internal Buck converter. A stacked half-bridge (SHB)-based Sigma-type topology has been proposed by integrating an LLC resonant converter and a Buck converter with an SHB structure, which inherently halves the input voltage for the LLC and Buck converter. Compared with the conventional Sigma topology, the proposed topology achieves higher efficiency under the same gain and power distribution conditions, due to the optimized duty cycle range of the Buck converter. Besides, all switches in the SHB achieve ZVS through phase-shift control, effectively reducing switching losses. An experimental prototype with 42-55V input and 5V/80W output is built, demonstrating a peak efficiency of 95.1%.