A railway foreign object detection algorithm for complex weather conditions based on an improved RT-DETR, named FRP-DETR, was proposed. A feature complementary mapping module (FCM) and Pzconv units were introduced, which built complementary paths between shallow spatial details and deep semantic information to compensate for the limitations of single-scale feature representation, with the two components working collaboratively to enhance the perception capabilities of small and edge targets. A railway perception modulation fusion module (RMFM) was introduced, which was based on adaptive channel attention and spatial modulation mechanisms to enhance the model’s response to key semantic information in railway scenes. The original downsampling module was replaced with pinwheel-shaped convolution (PSConv), which enhanced edge texture extraction through multi-directional asymmetric padding and separable convolution. A railway foreign object detection dataset containing four weather conditions was constructed based on the SaMam style transfer method, by transferring sunny railway foreign object images to rainy, foggy, and snowy scenes. Experimental results showed that compared to the original RT-DETR-R18 model, this method achieved improvements of 1.65 percentage points and 3.4 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, with a 63.5% reduction in parameter count and an inference speed of 88 frames per second. The results verified that FRP-DETR achieved high accuracy, lightweight design, and real-time performance in railway foreign object detection under complex weather conditions.

To address the issue of inconsistent thermal stress between the upper and lower devices of the same bridge arm in converters under conditions of uneven aging or layout deviations, a novel thermal balancing method for upper and lower bridge arms based on dead-time shifting adjustment was proposed. By monitoring the on-state voltage of insulated gate bipolar transistor (IGBT) online, the real-time junction temperature of IGBTs was deduced based on the mapping relationship between the on-state voltage and junction temperature of IGBTs. Then, the temperature difference between the upper and lower devices of the same bridge arm was determined. When the thermal stress difference between the upper and lower bridge arms exceeded the threshold, the effective conduction time of the upper and lower bridge arms was adjusted by fine-tuning the position of the dead time, thereby actively regulating the power loss distribution of the upper and lower bridge arms. This achieved an increase in the loss of the bridge arm with lower temperature and a decrease in the loss of the bridge arm with higher temperature without affecting the total system loss, thus improving the thermal stress consistency of power devices in the converter and enhancing system reliability. Theoretical analysis and experimental results showed that the proposed method did not affect system efficiency and had no significant negative impact on output performance. Under typical operating conditions, the average temperature difference between the upper and lower devices was reduced by approximately 14% after adjustment, effectively improving the thermal stress consistency of the upper and lower bridge arms.

A detection model named dynamic kernel perception YOLO (DKP-YOLO) was constructed to address the problems of contextual information loss, multi-scale distress coexistence, and insufficient detail restoration in pavement distress detection from a UAV perspective. A lightweight crack context module was proposed to mitigate the variability of crack morphology and the loss of contextual information in complex scenes. This module enhanced feature discriminability through heterogeneous parallel depthwise separable convolutions and a dynamic feature fusion mechanism. In order to tackle the challenges of complex backgrounds and multi-scale distress, a pavement distress perception module was designed, which improved the multi-scale distress perception capability using parallel multi-scale convolutions and a dual-attention mechanism. A large-kernel perception feature fusion module was introduced to overcome the limitations of traditional convolutions in modeling long-range dependencies and responding to small targets. This module captured global contextual relationships by integrating extra-large receptive field convolution with channel-spatial attention. Finally, a lightweight dynamic upsampling operator was incorporated into the neck network to reduce detail loss and semantic ambiguity during upsampling. Experiments on the UAV-PDD2023 dataset showed that the proposed model significantly improved detection accuracy while effectively reducing model complexity. Compared with the baseline model, DKP-YOLO achieved a better balance between being lightweight and maintaining high detection performance, making it more suitable for UAV-based pavement distress detection.

The development of underwater manipulators was reviewed from the perspective of actuation methods. The key roles of dynamic modeling, motion control, and autonomous intelligence in the operations of underwater manipulators were discussed, and the trend of end-effectors evolving from rigid to flexible structures was analyzed. Problems of existing underwater manipulators in aspects such as structural design, dynamic modeling, and autonomous intelligent control were summarized. The aim of the dynamic modeling, motion control, and autonomous intelligence, which are the key technologies for realizing the operations of underwater manipulators, is to deal with the complexity and uncertainty of underwater operational environments. The intelligent underwater manipulators with the capabilities of autonomous operation and precise motion control have broad application prospects in marine engineering, deep-sea exploration, and ocean resource development.

The advanced vector control scheme was analyzed in order to realize the high-performance control of hub motor for electric drive armored vehicle. The torque control mode was adopted and an electromagnetic torque estimation method based on full-order sliding mode observer was proposed in order to obtain good dynamic performance of torque and speed. The maximum torque per ampere (MTPA) control strategy was used below the base speed in the driving system. When the speed is above the base speed, a novel current compensation flux-weakening control scheme combining constant torque feedforward with voltage feedback was proposed according to the control requirements of hub motor. The experiment was conducted based on a 90 kW interior permanent magnet synchronous motor (IPMSM). The experimental results showed that the designed torque observer ensured high observation accuracy and the error was controlled within 5 N·m. The motor’s torque following performance was good and the error was kept within 5%. The motor can smoothly transit from MTPA to flux-weakening mode, and strictly run according to the planned flux-weakening route. The motor has good torque following and fast speed response during operation, which can meet the control requirements of electric drive vehicles.

The Electric_Data inspection dataset was constructed, and a PDD-SPCIE algorithm based on context information enhancement and multi-target semantic perception was proposed within the RT-DETR framework, in order to improve the detection accuracy of multi-category power defects in complex substation inspection scenarios. The LDDM-ResNet module was designed to extract high- and low-frequency detailed features through multi-kernel parallel convolution. The DA-Encoder module was constructed to enhance the representation of multi-scale and irregularly shaped targets by incorporating deformable attention and dynamic sampling. The LGCA module was introduced to fuse global and local features using atrous convolution and spatial depth transformation convolution. The original framework was improved from the aspects of feature representation, context modeling, and feature fusion, thus enhancing the unified detection ability for multiple defect categories. Experimental results showed that the proposed model achieved a precision of 0.819, a recall of 0.681, and an mAP₅₀ of 0.704 on the self-built dataset, and a precision of 0.719, a recall of 0.642, and an mAP₅₀ of 0.712 on the VOC dataset.

The Kalina cycle (KC) and the organic Rankine cycle (ORC) were used and modeled as the bottom cycles, to economically and efficiently recover the waste heat of the supercritical carbon dioxide Brayton cycle (SCBC). Parametric analysis was conducted and the NSGA-II multi-objective genetic algorithm was performed for these combined systems to optimize the parameters. The optimization results were compared with the SCBC system performance to display the benefits of combined cycles. Parametric analysis results showed that there was an optimal pressure ratio for the thermodynamic performances of both integrated cycle schemes. The thermodynamic performances of the two schemes were improved by raising the turbo expansion ratio of bottom cycles, and the exergoeconomic performances of the systems were promoted by upping the inlet temperature of the bottom cycle turbine. Comparison results showed that the optimized SCBC/KC system’s thermal efficiency and exergy efficiency increased by 9.27% and 8.69% respectively compared with that of the pre-optimized SCBC system, and its exergoeconomic cost increased by 0.92%. The thermal efficiency and exergy efficiency of SCBC/ORC system increased by 10.73% and 10.08% respectively, and the exergoeconomic cost increased by 1.87%. Comparative analysis showed that the SCBC/KC system was more exergy economical, while SCBC/ORC system was more energy efficient.

Existing knowledge graph update approaches often suffer from high computational cost, long update latency, and difficulties in maintaining consistency, which limit their applicability in large-scale dynamic environments. To address these issues, an incremental update method based on core entities and local subgraphs was proposed. A weighted centrality metric was constructed by integrating entity degree centrality with business access frequency, and the top-K entities with the highest scores were selected as core entities. In this way, the update process was focused on the entities that were most influential to downstream applications. Local subgraphs associated with the identified core entities were extracted through k-hop neighborhood expansion and edge-weight filtering, so that the update scope was restricted to semantically relevant structures and redundant computation was reduced. During model optimization, a local objective function was established for fine-tuning pre-trained knowledge graph embeddings. A neighborhood stability regularization term was introduced to suppress embedding drift caused by incremental updates. In addition, Schema constraints and multi-source confidence fusion were incorporated to ensure the consistency and reliability of the updated graph. Experiments were conducted on large-scale knowledge graph datasets. The results showed that, compared with full graph reconstruction and conventional fine-tuning methods, the proposed method reduced update latency by approximately 60%, improved triple insertion accuracy by approximately 8 percentage points, and decreased the consistency conflict rate by approximately 12 percentage points. These results indicate that the proposed method can effectively improve update efficiency while maintaining the structural and semantic quality of the knowledge graph.

A spatiotemporal joint lane-change intention prediction model based on Transformer and graph attention neural network (GATv2) was proposed in order to accurately predict lane-changing intention of surrounding vehicles and improve the safety and reliability of autonomous vehicles. Symmetric exponential average filtering was applied to smooth the raw NGSIM data. Then velocity and acceleration were recalculated by using the difference method, and outliers were removed. The data of the target vehicle and its surrounding vehicles were unified by establishing an eight-neighborhood for each vehicle, providing a foundation for model training and validation. Transformer multi-head attention was used to mine temporal dependency, while GATv2 was employed to quantify the interaction relationship between vehicles at each time step. Then a Transformer-GATv2 lane-changing prediction model based on spatiotemporal feature and multi-vehicle interaction was constructed. The experimental results showed that the proposed model significantly outperformed other baseline models in accuracy, recall, F₁-score, and other metrics. A prediction accuracy of 98.04% was achieved, which demonstrated superior lane-changing intention prediction performance and strong early prediction capability. The model was deployed on a six-degree-of-freedom driving simulator integrated with the CARLA co-simulation platform for model-in-the-loop testing, verifying the usability of the model in real-world scenario.

A new composite bent cap consisting of a shell made of steel plate and ultra-high-performance concrete (UHPC) and cast-in-place core normal concrete (NC) was proposed in order to realize the assembly and rapid construction of ultra-large-scale bent cap for urban viaducts or highway reconstruction and expansion projects. Parametric analysis of different UHPC and steel plate thickness was conducted in order to analyze the influence of the thickness of UHPC and steel mold plate on its stress performance. Results showed that the stiffness of the shell was affected by the thickness of UHPC and steel plate and their ratio together under the action of self-weight. The thicker the UHPC and steel plate are, the better the stress performance of the shell is, but the economy will be reduced when tensioning prestress and casting concrete. It is recommended to use UHPC thickness of 70 mm and steel plate thickness of 6 mm. A piece of 1∶2.5 scaled-down model was designed and static loading test was conducted in order to verify the feasibility and safety of this scheme. Results show that the new UHPC-NC composite bent cap has good force performance and high safety reserve, which can provide reference for the assembly construction of bent cap.

Existing scheduling models for Internet data center (IDC) commonly have two key shortcomings: insufficient consideration of the voltage fluctuations in distribution networks and the impact of IDC storage data constraints on the spatiotemporal transfer of the workloads. An optimized operation method for flexible distribution networks was proposed considering the flexible IDC scheduling. Uncertainty scenarios for renewable generation and load demand were generated using Monte Carlo simulation and the K-means clustering algorithm. Differentiated scheduling strategy was established to distinguish between sensitive and tolerant loads. An IDC power consumption model along with a data storage constraint model were constructed. A robust optimization model was established with the objective of minimizing both distribution network operating costs and IDC electricity purchasing costs. The dynamic regulation capability of soft open point (SOP) and voltage fluctuation constraints were also incorporated into the model. Simulation results demonstrated that the proposed method significantly enhanced voltage quality, markedly increased the renewable energy accommodation rate, and reduced the electricity procurement cost for IDC by collaboratively optimizing the spatiotemporal load shifting of IDC workload along with the power regulation and reactive power support capabilities of SOP. It also validated the impact of storage capacity constraints on the efficiency of spatiotemporal workload shifting.

An encirclement decision-making method based on adaptive curriculum learning and multi-agent proximal policy optimization (MAPPO) algorithm was proposed to address the problem of encirclement and game confrontation for unmanned surface vehicle (USV) swarms in complex marine environments. For maritime encirclement and defense tasks, a combat simulation environment including dynamic targets and multiple reefs was constructed, and the judgement criteria for successful encirclement and task termination were defined. To improve the decision-making efficiency in complex adversarial environments, a normalized state space representing the motion states of both friendly and hostile parties, multi-scale reward functions, and a continuous action space were designed. An adaptive curriculum scheduler was introduced into the centralized training and decentralized execution framework to dynamically adjust the environmental complexity and the noise intensity of action exploration. The results of multiple sets of adversarial simulation experiments indicate that, compared to the baseline methods (i.e., no curriculum learning and traditional curriculum learning methods), the proposed method effectively improves the encirclement success rate, shortens the average task completion time and the average encirclement path length, reduces the number of collisions, and exhibits good generalization ability and adversarial adaptability.

To address the core issue of efficiency degradation in concentrator photovoltaics (CPV) caused by high heat flux, microchannel cooling technology has emerged as a critical research direction. Advances in this field were systematically reviewed, with a focus on three mainstream configurations: single-layer, manifold, and jet-impingement structures. The effects of factors such as channel geometry, working fluid, and flow regime on pressure drop, temperature rise, and cooling efficiency were analyzed. Furthermore, the strengths and weaknesses of various flow channel designs, including serpentine, pin-fin, and fractal configurations, were compared. Studies have shown that single-layer straight channels are prone to high pressure drop and temperature non-uniformity. Derived serpentine and fractal channel designs can enhance heat transfer by perturbing the flow. The manifold configuration effectively reduces flow resistance and improves temperature uniformity, often serving as a foundational platform for integrating other cooling technologies. The hybrid jet-impingement and microchannel approach demonstrates potential in addressing localized ultra-high heat flux, though its system complexity and energy consumption still require optimization. Looking ahead, future research on this technology should progressively advance from the millimeter scale to the micro-scale, enabling the development of lightweight and efficient portable concentrator photovoltaic devices. The focus should shift from pursuing “cooling intensity” to emphasizing “intelligent heat transfer architecture,” deeply integrating bionics and artificial intelligence to design adaptive microchannels. Furthermore, the research perspective should transition from optimizing individual components toward a systemic, co-design approach that couples photonic, thermal, electrical, and mechanical multi-physics fields.

The shared control strategy, as the main control mode of teleoperation robots based on telepresence, can make full use of the operator's perception, judgment and decision-making ability, and utilize to the robot's own unique advantages. The telerobotic telepresence technology was introduced. The development of teleoperation shared control strategy was summarized. The principles of each control strategy were introduced mainly based on tactile feedback guidance, kinematic constraint avoidance and sharing factor assignment. The bottlenecks and shortcomings in the development of telerobotic shared control strategy were analyzed, such as the singleness or rigidity of shared factors, time delay and limited autonomous judgment ability of robots. The future research trends were proposed from three aspects in view of the limitations of the current study, namely, improving the intervention level, strengthening robot intention prediction, and combining machine learning, which have a certain guiding significance.

A novel parameter optimization method for model predictive control was proposed to address the problems of insufficient tracking accuracy and poor real-time performance of model predictive controllers in trajectory tracking of autonomous vehicles. An improved non-dominated sorting whale optimization algorithm (NSWOA) based on the improved Sinusoidal mapping and a Lévy flight strategy was proposed to solve the problem of relatively concentrated and out-of-bound optimal solutions obtained by the NSWOA. The parameter optimization problem of model predictive controllers was formulated as a multi-objective optimization problem. The predictive horizon, control horizon, and sampling time were used as optimization variables. The sum of squared lateral trajectory errors and total computation time were used as optimization objectives. The improved NSWOA was employed to solve the multi-objective optimization problem and obtain the Pareto optimal solution set. The optimal controller parameter combination was determined by using the expert scoring method, the continuous ordered weighted averaging operator method, the game theory-based combined weighting method, and the technique for order preference by similarity to an ideal solution. The tracking accuracy of the proposed method was improved by an average of 56.27%, and the computation time was reduced by an average of 21.54%. This method provides a new idea that balances high tracking accuracy and high real-time performance for the tuning strategy of model predictive control parameters.

A full-coverage 3D path planning method for mountainous orchard plant protection UAVs was proposed to address the challenges of manual control and the lack of 3D path planning for plant protection drones operating in hilly orchards. 3D coordinates of the operation area obtained from a real scene 3D model of the area were utilized. Comprehensive 3D path planning for plant protection UAVs was carried out based on the reciprocating cattle farming method and the real scene 3D model of the hilly orchard. An energy consumption model for the UAV was constructed, considering its movement status and load changes. The operating heading angle (ranging from 1° to 180°) was optimized to determine the path with minimal energy consumption. Results of field experiments showed that the path with the minimal energy consumption (heading angle of 91°) reduced the total energy consumption by 20.88% and the time required to complete the plant protection operation by 16.31%, compared to the path with the maximum energy consumption (heading angle of 147°). The fluctuation in canopy droplet deposition at each sampling point within the operation area was minimal. This method not only optimizes the energy consumption and improves the operational efficiency, but also ensures full coverage of plant protection within the working area.

Direct numerical simulations were performed to investigate the interaction between a horseshoe vortex and a free surface at various Weber numbers. The incompressible Navier-Stokes equations were solved using the volume-of-fluid method coupled with the piecewise linear interface construction scheme. The continuum surface force model was incorporated to account for surface tension effects during interface deformation. The simulations covered a range of Weber numbers from 0.24 to 0.84. By quantitatively analyzing the evolution of total kinetic energy, dissipation rate, total helicity, and surface energy, the influence of Weber number on the vortex-interface interaction was elucidated. At low Weber numbers, surface tension dominated, maintaining the interface as a coherent ring-like structure and suppressing deformation and topological changes, while the vorticity field remained stable with negligible variations in helicity. Conversely, at high Weber numbers, inertial forces dominated over capillary effects, leading to significant interface deformation, breakup, and reconnection. This process resulted in vortex breakdown, accelerated kinetic energy decay, distinct peaks in energy dissipation, and a continuous decrease in helicity. These findings underscored the critical role of the Weber number in energy transfer and dissipation mechanisms, providing theoretical support for multiphase flow dynamics and guidelines for engineering applications such as the design of surface and trans-media vehicles.

A new method for small-target water-floating garbage detection was proposed to address the issue of missed and false detections due to the limited information of small-target water-floating garbage that is prone to losing detailed features in feature extraction. After analyzing the limitations of small-target feature extraction, an edge-enhanced feature extraction network which synergistically utilized the spatial features and high-frequency features was proposed to effectively enhance the complex edges, details, and high-frequency information of small targets. A triple-branch cross-scale feature adaptive fusion module was designed to enhance the network’s ability to represent multi-dimensional small-target features by analyzing small-target features through local detail perception, global context modeling, and the large receptive field. An adaptive sparse attention-based intra-scale feature interaction module was constructed, which dynamically adjusted the interactive features by leveraging sparsity to enhance the discriminability between small targets and backgrounds. Experimental results show that, compared with the baseline model RT-DETR, the proposed method achieves improvements of 4.93, 2.46, and 3.18 percentage points in mAP, mmAP, and recall rate (R) respectively on the water-floating garbage dataset from the Lanzhou section of the Yellow River, and achieves improvements of 3.39, 1.45, and 2.23 percentage points on the FloW-Img dataset. These results indicate that the proposed method effectively enhances the detection performance for small-target water-floating garbage, thereby facilitating the efficient monitoring and management of water-floating garbage.

A free-force control method of the flexible robot joint system without sensors on link side was proposed aiming at the application of robot direct teaching. The dynamic LuGre model was introduced to estimate the joint friction. Two-segment quartic polynomial was adopted to describe the stiffness of the flexible robot joint system. The joint torque was observed based on the generalized momenta. The torsional displacement of the harmonic drive was estimated through the inverse stiffness model and the joint torque among the algorithm. The gravity and angle on link side were figured out by combining torsional angle with motion transmission characteristics. The contact torque was obtained by utilizing the kinetic equation on link side. Then a desired motor driving torque was constructed comprising the gravity, joint friction and the contact torque. The free-force control was realized by tracking this desired motor torque. The experiments were conducted on the laboratory setup of the flexible robot joint system. The experimental results show that the contact torque is about 1.8 N·m when accomplishing the same drag teaching process. The free-force control method based on the compensation of gravity and friction requires a contact torque of about 3.4 N·m. The contact torque required for the power stage off teaching is approximately 14 N·m. The experimental results verify that the proposed method has a practical effect.

A lightweight traffic police gesture recognition method for autonomous driving was proposed to address the challenge of maintaining high recognition accuracy while achieving lightweight deployment, particularly given the subtle variations in gestures and complex application scenarios. An efficient adaptive weight downsampling module was designed on the basis of the YOLOv8n algorithm. This module replaced the standard convolutions in the backbone network to capture spatial differences, reducing both parameter count and computational complexity. Additionally, a C2f-G module was introduced to better leverage local and contextual features, improving the recognition accuracy in complex backgrounds. A triplet attention mechanism was incorporated into the neck network to comprehensively capture fine-grained feature details. Experimental validation on the Chinese traffic police gesture dataset showed that compared to the baseline model, the proposed method reduced the parameter count by 44.0%, increased the mean average precision by 3 percentage points, and achieved a detection speed of 294 frames/s. The proposed method effectively addresses the challenge of dynamic traffic police gesture recognition in complex environments, achieves fast and high-precision recognition, realizes a lightweight architecture, and significantly reduces the deployment complexity.