Ultra-high voltage gas-insulated switchgear (GIS) is a critical core component for ensuring the safe operation of UHV power grids and rapidly isolating system faults. However, operational statistics reveal that discharge faults induced by internal metal particles account for up to 54.5

Autonomous vehicles must enforce safety constraints even when their state estimates are corrupted by sensor faults and disturbances. This paper develops a separation-based robust safety-control framework that couples a fault-tolerant observer with a control barrier function (CBF) safety filter through an explicit estimation-error envelope. First, a uniformly ultimately bounded observer-error estimate is derived. This bound is then injected into an estimated-state robust CBF condition, yielding safety margins that account for both observation error and bounded disturbances. The construction is further extended to time-varying safe sets induced by moving obstacles. For implementation, the resulting condition is realized as a quadratic-program safety filter with high-order obstacle and lane constraints. Simulations on a nonlinear 3-DOF bicycle model evaluate bias faults, gust-like disturbances, dense traffic, and tightened stress tests. Compared with a standard CBF baseline and observer/safety-filter ablations, the proposed method preserves nonnegative safety margins while keeping slack activation negligible. Additional sensitivity experiments quantify the trade-off among safety margin, slack usage, observer accuracy, control conservatism, and QP computation time. The results support the proposed architecture as a practical bridge between bounded state estimation and fault-aware safety filtering.

Control-barrier-function-based safety filters are promising for autonomous driving, but most existing formulations treat obstacle perception as deterministic or account only for bounded ego state-estimation errors. This becomes limiting when obstacle existence, position, motion, and sensing quality vary online. We present a sensor-health- and belief-aware risk-adaptive high-order control barrier function (HOCBF) safety filter for dynamic obstacle avoidance. The method uses obstacle belief from a perception/tracking module, inflates residual obstacle uncertainty according to an object-wise sensor-health score, and converts upper-tail risk into adaptive HOCBF tightening through conditional value-at-risk (CVaR). Sensor health enters the controller through both covariance inflation and online CVaR confidence scheduling. The resulting quadratic program combines deterministic ego-error robustness with probabilistic perception uncertainty while minimally modifying the nominal control input. The zero-slack solution guarantees forward invariance of the risk-tightened safe set under the stated assumptions, whereas the slack-activated mode provides a quantified least-violation fallback rather than a strict safety guarantee. Simulations on a nonlinear 3-DOF bicycle model evaluate critical cut-in, sudden perception degradation, merge-bottleneck, fixed-CVaR, sensitivity, runtime-scaling, heterogeneous multi-obstacle, and heavy-tailed uncertainty cases.

This article addresses the challenge of detecting internal structural defects in ultra-high voltage GIS isolation switches. An innovative high penetration X-ray detection technology is applied, which is based on a 7.5 MeV electron cyclotron and can achieve visual diagnosis of internal equipment abnormalities. In practical engineering applications, this method successfully discovered the defect of partial detachment of the PTFE sealing strip at the overlapping shielding of the isolation switch’s moving contact seat. To evaluate the potential harm of the defect, the study further combined electric field simulation to analyze the electric field distribution of the strip under different detachment states. The simulation results show that in the partially detached state, the electric field distortion coefficient is low, the maximum field strength is much lower than the critical breakdown field strength, and the discharge risk is controllable. This technology route that combines advanced non-destructive testing technology with numerical simulation not only accurately locates hidden defects, but also scientifically evaluates their safety, providing important technical support and decision-making basis for on-site operation and maintenance of ultra-high voltage GIS equipment.

As ports become increasingly electrified, the operation of electric tugboats faces challenges such as limited battery capacity and varying service demands of ships. This paper investigates the scheduling problem of electric tugboats, with a particular focus on integrating flexible charging strategies. We propose a mixed-integer linear programming (MILP) model aimed at minimising the total system costs, which includes the fixed tugboat utilisation cost, task tardiness penalty cost, and tugboat recharging cost. To address the high complexity of the problem, an improved kernel search method that incorporates the kernel search with a two-phase matheuristic (KSM) is developed. Extensive numerical experiments on 80 instances show that the proposed KSM method yields feasible solutions for large-scale instances where CPLEX cannot, and demonstrates more robust scalability than the straightforward kernel search method. A case study verifies the effectiveness of the KSM in providing complete, exact, and quantitative tugboat scheduling plans. This research fills the gap in the literature regarding the scheduling of electric tugboats under flexible charging strategies, providing an effective decision-support tool for port management.

Different sensors may experience degeneracy in specific scenarios, which can lead to failures in multisensor fusion optimization. To address the challenges of localization and mapping under such degeneracy conditions, this article proposes a LiDAR-inertial-visual simultaneous localization and mapping (SLAM) system with integrated degeneracy awareness. In the front end, the system utilizes the error-state iterated Kalman filter (ESIKF) for tracking and incorporates a sensor degeneracy detection method to ensure that degraded sensor data do not affect the state error update. In the back end, we replace the conventional point cloud map with an adaptive voxel map to accelerate the retrieval of planar point cloud and merge LiDAR planar features within a sliding window via region-growing algorithm. Furthermore, the point-to-plane distance formula is optimized via the Rayleigh quotient theorem, which reduces the dimensionality of the optimization variables and simplifies the optimization process. Finally, we evaluate the proposed system on several challenging datasets and compare it with various state-of-the-art sensor fusion systems. The experimental results demonstrate that the proposed system achieves high localization accuracy in the majority of challenging scenarios.

As the electrification reform accelerates in ports worldwide, the application of electric tugboats is becoming more widely applied, posing a challenge in the balance between working arrangement and energy replenishment, especially when the shore energy replenishment facilities are limited. Aligning with the emerging trends of port electrification, unmanned operations, and intelligentization, this paper investigates unmanned electric tugboat scheduling considering battery-swapping operations that combine the assignment of tasks to the working periods of tugboats, the allocation of battery-swapping operations to the shore battery-swapping stations, and the sequencing of operations at each station. The problem is formulated into a mixed-integer linear programming to minimize the total completion time of the battery-swapping operations. A logic-based Benders decomposition method is proposed that decomposes the problem into a master problem and a subproblem. The master problem relaxes the sequencing constraints and solves the assignment of tasks to tugboats and the allocation of battery-swapping operations to stations. The SP, based on the solution to the master problem, determines the sequencing of battery-swapping operations at each station. Considering the interdependence of swapping operations of each tugboat that might be allocated to different stations, a dispatching heuristic is designed to efficiently obtain high-quality sequences for the stations. Numerical experiments are conducted based on 80 randomly-generated instances with up to 100 tasks, ten tugboats, and six battery-swapping stations. The results demonstrate that LBBD is capable of solving all 80 instances, whereas the commercial solver CPLEX fails to solve those with 80 or more tasks. Moreover, the average computational time of CPLEX on the instances it can solve is 241.32 s, nearly 32 times that of LBBD (7.57 s). This clearly indicates that LBBD significantly outperforms CPLEX in terms of both computational capacity and efficiency. Further analyses show that the increase in the number of tugboats will significantly shorten the makespan and make ETSBS easier to solve, while the increase in the number of battery-swapping stations makes the problem more challenging with longer computational time.

In gas-insulated switchgear (GIS), basin-type insulators are prone to partial discharge and surface breakdown (flashover) due to uneven electric field distribution, threatening the reliability of GIS devices and electric power system. While traditional geometric optimization methods exhibit limited effectiveness and structural complexity, functionally graded materials (FGM) offer a novel approach for electric field optimization. This paper proposes permittivity gradient design method for 550 kV GIS basin insulators with FGM structure, based on the topology optimization. The goal is to improve electric field uniformity of both convex and concave surface of the basin insulator. Results demonstrates significant improvements in FGM insulators: the maximum electric field strength on the convex surface decreases from $\mathbf{1 3. 2 4 ~ k V} / \mathbf{m m}$ to $11.50 \text{kV} / \text{mm}$ (13.14 % reduction), while the concave surface field strength drops from $15.42 \text{kV} / \text{mm}$ to $11.50 \text{kV} / \text{mm}$ (25.42 % reduction). Moreover, both surfaces meet the engineering limits of electric field strength on the surface of GIS (less than $12 \text{kV} / \text{mm}$). This study validates the effectiveness of topological optimization in functionally graded insulation design, providing an innovative solution for enhancing insulation performance in high-voltage power GIS equipment.

In addressing the complexities of time series analysis, two primary challenges emerge: high dimensionality and inherent non-linearity, which often obstruct effective data processing and analysis. Representation learning emerges as a pivotal solution, enabling the efficient transformation of complex, high-dimensional time series into formats conducive to deeper analysis and understanding. This paper proposes a dynamic weight-based granular representation method for time series and validates it in collective anomaly detection tasks. The proposed method innovatively incorporates a weight allocation mechanism within the framework of justifiable granularity, a principle that accentuates the central tendency and extreme features of the data. By representing the original time series through this enhanced principle, the proposed method generates three distinct sets of interval granules: central, maximum value, and baseline granules, each reflecting crucial characteristics of the original data. This granular combination provides a holistic representation of time series, facilitating a more comprehensive analysis and interpretation. Subsequently, the proposed method evaluates the similarity across all subsequences, pinpointing those with notably low similarity as potential anomalies. Through extensive validation, the proposed method has shown high effectiveness in capturing essential time series features such as central tendency and amplitude variations, outperforming existing methods in key metrics including Accuracy, Sensitivity, Specificity, and F1 Score. Furthermore, the Friedman test confirms the proposed method’s significant advantages in three of these indicators, showcasing its capability to address the complexities of time series analysis effectively.

This paper is concerned with the event-triggered consensus problem for leader-following multi-agent systems under asynchronous denial-of-service (DoS) attacks. Since the asynchronous attacks on different edges may enlarge the defense burden, the topology connectivity is employed to reveal the effectiveness of attacks and determine the unhealthy time periods. Relying on the internal system state, a dynamic event-triggered mechanism is built to regulate the information transmission between agents. In this mechanism, a variable threshold weight composed of the deviations of the relative neighboring errors and historical state errors is proposed to adaptively schedule the triggering thresholds with systems running stages. To guarantee secure consensus performance even during the triggering intervals, the local estimation of the neighboring information is integrated into the controller such that sufficient conditions to obtain the control parameters and the tolerable attack parameters are established without any Zeno behavior. The proposed method is validated by a simulation study.

When robot creates a map, dynamic objects can change the space and render the map unusable for navigation. Additionally, the vertical resolution of a VLP-16 LiDAR may be insufficient, making dynamic point removal challenging. To address these challenges, we propose a novel method for dynamic point detection and removal consisting of four components. Firstly, we introduce a multi-resolution heightmap to enhance the efficiency and precision of dynamic point recognition by segmenting ground points. Secondly, we address the issue of limited vertical resolution by fusing multiple scans to simulate additional scan lines and leveraging a multi-resolution range image for precise dynamic point elimination. Thirdly, we apply clustering and principal component analysis-based techniques to compute eigenvectors, facilitating the correction of misclassified static points. Lastly, we propose the utilization of a three-dimensional bounding box strategy to reinforce the monitoring of small static clusters with elevated probabilities of misclassification. These four components complement each other and are executed sequentially. We evaluated our method for both dynamic point removal and ground segmentation on the KITTI dataset and real-world environments. The results demonstrate that our method outperforms baseline methods and generates clean maps.

Pipe pile welding robot is a specialized machine designed for the automatic welding of Pre-stressed High-strength Concrete (PHC) pipe piles. In the application of the pipe pile welding robot, there is a problem that the motor response has an obvious time delay behind input. Considering this problem, this paper introduces time delay of motor speed into the motor model and proposes a fixed time delay sliding mode control combined with PI controller method for the system to achieve trajectory tracking. The existence condition of sliding mode surface and the controller's expression are proposed by Lyapunov-Krasovskii method. In simulation experiments, taking trajectory tracking control of the welding robot as an example, motor's trajectory tracking model with time delay is established. The constant velocity reaching law is selected to resist load interference in the design of sliding mode controller. The feasibility of the fixed delay sliding mode controller controlling the time-delay motor system for trajectory tracking is verified. Compared to the state feedback controller with Smith predictor, the simulation result shows that the proposed method has better capability on resisting the load interference.

This paper studies a secure iterative interval estimation approach for cyber-physical systems subject to stealthy deception attacks. Under the hypothesis that the system is accessed by a stealthy attack, an iteration scheme integrating the T-N-L observer framework is employed to reconstruct the system state. With the help of a structure separation method, a sufficient condition in terms of linear matrix inequality is provided to obtain convergent observation errors under deception attacks. Resorting to the reachability analysis, a secure state interval is built by means of the analyzed attack bounds and the observation error interval. Simulation studies verify the effectiveness of the proposed method for attack and attack-free cases.

The precise classification of seismic events is of paramount importance for earthquake early warning, risk management, and seismic cataloging. A convolutional neural network (CNN) model is proposed, integrating short-time Fourier transform, attention mechanism and spatial pyramid pooling. The aim is to efficiently classify seismic events by capturing core features and multi-scale information. The proposed model is subject to experimental evaluation through five-fold cross-validation using a dataset collected and labeled by the Jiangsu Seismic Network Center, encompassing natural earthquakes, artificial explosions, and collapses. Compared to other methods, the proposed model exhibits a significant improvement in accuracy, ranging from 90.20% to 92.90%, reaching 94.18%. The recognition accuracy for natural earthquakes, collapses, and explosions is reported as 93.52%, 94.34%, and 94.80%, respectively. Optimal results are achieved across various performance metrics, including sensitivity (94.10%), specificity (97.05%), precision (94.22%), F1 score (93.15%) and Matthews correlation coefficient (91.24%). Comprehensive experimental results indicate that this method has made a significant breakthrough in the task of seismic event classification, providing a more robust tool for seismic research.

During the process of map creation, the presence of dynamic objects can disrupt the environment, rendering the map unsuitable for navigation. Additionally, the limited vertical resolution of a VLP-16 LiDAR sensor can present challenges in accurately identifying and eliminating dynamic points. To tackle these issues, we propose a dynamic point cloud removal method for pedestrians, which involves three essential components. Firstly, we adopt a novel ground point segmentation method to reduce the probability of misclassification of point clouds in subsequent processing steps. Secondly, we employ the k-means++ method to cluster each frame of point clouds, obtaining all potential clusters that may contain pedestrian point clouds. We deliberately refrain from discarding anything even remotely resembling a human. Subsequently, the multi-dimensional slice features and intensity attributes of the clustering results are extracted, and these are combined with the classification outcomes of the Support Vector Machine (SVM) to identify instances of pedestrians within the frame. Our method undergoes comprehensive evaluation in real-world environments, and the results demonstrate its superior performance compared to baseline methods.

This article is to investigate the consensus control and initialization region optimization for leader‐following multi‐agent systems with time‐varying communication delay and nonidentical consecutive packet dropouts. By combining the Bernoulli distribution model of the data loss and the Hadamard product, the control protocols affected by both communication delay and packet dropouts are utilized to formulate the consensus problem into the mean‐square stability problem of the augmented error systems. Resorting to the Lyapunov function methodology and a structure separation method, a sufficient condition to ensure the consensus is built in the form of linear matrix inequality. Relying on the given Lyapunov function and set‐membership analysis, an optimization procedure is proposed to simultaneously ensure the consensus and enlarge the estimated ellipsoid of initial conditions. The validity of the provided method is illustrated through a comparative simulation.

Cracks are the most important type of pavement diseases,and the accurate crack segmentation is an important decision basis for national preventive maintenance management of roads.To address the problem of crack segmentation accuracy of existing models for pavement under complex background,an end-to-end crack segmentation model based on convolutional neural network was proposed,which used a layered structure of ConvNeXt encoder to extract multi-scale features.A pyramid pooling module was used to further obtain the global priori features by the top layer of features,and the feature fusion was performed through a pyramid structure with lateral connections and top-down.A weighted cross-entropy loss function was employed to enhance the detection performance of model for the crack and background imbalance problem.In addition,a crack dataset UCrack with 2 876 cracks covering multiple crack types and a wide range of backgrounds was created to provide rich features for model learning.Experiments show that,compared with other best-performing models,the model recall and F1 score on the UCrack test dataset are improved by 2.68%and 6.89%,respectively.The test on the CrackDataset dataset achieves recall of 85.68%and F1 score of 80.11%,which implies that the model has better generalization capability and can cope with pavement crack segmentation with complicated scenarios.

This paper is devoted to the iterative interval estimation for nonlinear discrete-time systems. To reconstruct the system state, a sequence of iterative observers is established based on the iterative disturbance estimation and measured output. By means of the Lipschitz condition and [Formula: see text] technique, sufficient conditions are built by the Lyapunov function method to make observation errors convergent. Resorting to the zonotope-based reachability analysis, the reachable set of nonlinear terms and observation errors are analyzed such that the state interval can be supplied. The presented approach is validated by a simulation comparison.

X-ray detection is a promising visualization method for internal defects' diagnosis within the power equipment, while its impact on partial discharge (PD) should be clarified due to the strong ionizing ability. In this work, the discharge dynamics under different X-ray irradiation doses in gas insulated switchgear (GIS) are numerically investigated by coupling the Monte Carlo N-Particle (MCNP) and 2-D particle-in-cell/Monte Carlo collision (PIC/MCC) model, taking into account the conversion from irradiation dose to preionization in space. It is observed that the increment in irradiation dose results in a faster propagating discharge with a significantly expanded volume, weakening the insulating performance of the gap against overvoltage. This is correlated with the insignificant difference in electron energy both in the channel and at the discharge front, which accelerates the synchronized development of electron avalanches and thus promotes diffuse discharge formation. The discrete seed electrons caused by X-ray irradiation can guide the discharge propagating path with a larger dose. The results in this work demonstrate the essential relationship between X-ray irradiation and discharge dynamics, providing a reference for X-ray detection applied in electrical equipment.

Waveforms of artificially induced explosions and collapse events recorded by the seismic network share similarities with natural earthquakes. Failure to identify and screen them in a timely manner can introduce confusion into the earthquake catalog established using these recordings, thereby impacting future seismological research. Therefore, the identification and separation of natural earthquakes from continuous seismic signals contribute to the monitoring and early warning of destructive tectonic earthquakes. A 1D convolutional neural network (CNN) is proposed for seismic event classification using an efficient channel attention mechanism and an improved light inception block. A total of 9937 seismic sample records are obtained after waveform interception, filtering, and normalization. The proposed model can obtain better classification performance than other major existing methods, exhibiting 96.79% overall classification accuracy and 96.73%, 94.85%, and 96.35% classification accuracy for natural seismic events, collapse events, and blasting events, respectively. Meanwhile, the proposed model is lighter than the 2D convolutional and common inception networks. We also apply the proposed model to the seismic data recorded at the University of Utah seismograph stations and compare its performance with that of the CNN-waveform model.