信息工程最新文献

This paper presents a geometric solution framework for a target defense problem, formulated as a variant of the classical Game of Two Cars. The setting considers a Dubins defender that is faster and more maneuverable and aims to intercept a Dubins attacker attempting to reach a convex target set. To address the computational complexity of solving the associated Hamilton-Jacobi-Isaacs (HJI) equations, a geometric approach based on the concept of the Attacker Dominance Region (ADR) is developed. The ADR is constructed piecewise from the boundaries of the players’ reachable sets. The complete solution consists of two components: a Game of Kind, which determines the outcome based on the spatial relationship between the ADR and the target set, and a Game of Degree, which derives optimal strategies that achieve equilibrium. Simulation results demonstrate the effectiveness of the proposed method under realistic motion constraints and indicate its potential applicability to practical target defense scenarios.

The aircraft final assembly is a complex system, encompassing various aspects and multidimensional production factors. These numerous factors are interconnected, significantly impacting the efficiency of the final assembly process. To investigate the interrelationships among various production factors, this study introduces a specialized fine-tuning large language model for aircraft final assembly, termed Aircraft Final Assembly ChatGLM (AFA-ChatGLM). This model is designed to automatically extract essential information regarding key production factors from process documentation. Furthermore, the FP-Growth algorithm is employed to uncover association rules between these production factors and the various stages of the final assembly. Experimental results indicate that our method demonstrates outstanding performance in the aircraft final assembly domain. Specifically, for the assembly process documents of the C919 large passenger aircraft, our proposed model achieved a Precision of 82.7%, Recall of 89.1%, and F1 score of 85.4%, representing a substantial improvement over traditional word segmentation methods. leveraging the superior performance of the model, we utilized association rule mining techniques to construct 44,851 high-confidence association rules for the final assembly line of the C919, laying a foundation for subsequent optimization of the production line.

With the rapid development of logistics and manufacturing industries, traditional handling robots can no longer meet practical needs. In response to this, for the rapid handling of diversified products, research first combines deep learning technology to improve the Double Actors Regularized Critics (DARC) algorithm and design a robot path planning method; Then, a Reachability Analysis-based Time Optimal Trajectory Planning (RA-TOP) algorithm is designed to generate the time optimal trajectory from the interpolated robot path, thereby efficiently achieving the task of rapid handling of diversified products by robots. The findings demonstrate that the enhanced DARC algorithm offers notable benefits in terms of path planning, resulting in shorter paths, reduced curvature, enhanced smoothness, a minimum path length of less than 20 meters, and fewer convergence times, surpassing the performance of alternative algorithms. The time trajectory generation algorithm has a shorter motion time, taking about 1.75 seconds under the same displacement, which is better than the comparison algorithm and can effectively avoid robot motion shaking. Compared with the comparative method, the obstacle avoidance trajectory of the research method is closer to the expected value, with an average deviation of about 0.5 m from the expected trajectory. The application results of the example show that under the research method, the success rate of the handling robot task is 94% or above. The above results indicate that robots can stably and dynamically avoid obstacles, generate optimal trajectories, meet the real-time path planning and efficient handling needs of enterprises, and improve production efficiency under the research method.

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With the growing penetration of renewable energy, the impact of renewable uncertainties on power system secure operation is of increasing concern. Based on a recently developed linear power flow model, we formulate a chance-constrained optimal power flow (CC-OPF) in transmission networks that provides a concise way to regulate the security regarding both power and voltage behaviors under renewable uncertainties, the latter of which fails to be captured by the conventional DC power flow model. The formulated CC-OPF finds an optimal operating point for the forecasted scenario and the corresponding generation participation scheme for balancing power fluctuations such that the expectation of generation cost is minimized and the probabilities of line overloading and voltage violations are sufficiently low. The problem under the Gaussian distribution of renewable fluctuations is reformulated into a deterministic problem in the form of second-order cone programming, which can be solved efficiently. The proposed approach is also extended to the non-Gaussian uncertainty case by making use of the linear additivity of probability terms in the Gaussian mixture model. The obtained results are verified via numerical experiments on several IEEE test systems.

Vision Transformers (ViTs) have achieved state-of-the-art performance on various computer vision tasks. However these models are memory-consuming and computation-intensive, making their deployment and efficient inference on edge devices challenging. Model quantization is a promising approach to reduce model complexity. Prior works have explored tailored quantization algorithms for ViTs but unfortunately retained floating-point (FP) scaling factors, which not only yield non-negligible re-quantization overhead, but also hinder the quantized models to perform efficient integer-only inference. In this paper, we propose H-ViT, a dedicated post-training quantization scheme (e.g., symmetric uniform quantization and layer-wise quantization for both weights and part of activations) to effectively quantize ViTs with fewer Power-of-Two (PoT) scaling factors, thus minimizing the re-quantization overhead and memory consumption. In addition, observing serious inter-channel variation in LayerNorm inputs and outputs, we propose Power-of-Two quantization (PTQ), a systematic method to reducing the performance degradation without hyper-parameters. Extensive experiments are conducted on multiple vision tasks with different model variants, proving that H-ViT offers comparable(or even slightly higher) INT8 quantization performance with PoT scaling factors when compared to the counterpart with floating-point scaling factors. For instance, we reach 78.43 top-1 accuracy with DeiT-S on ImageNet, 51.6 box AP and 44.8 mask AP with Cascade Mask R-CNN (Swin-B) on COCO.

Significant progress has been made in distributed unmanned aerial vehicle (UAV) swarm exploration. In complex scenarios, existing methods typically rely on shared trajectory information for collision avoidance, but communication timeliness issues may result in outdated trajectories being referenced when making collision avoidance decisions, preventing timely responses to the motion changes of other UAVs, thus elevating the collision risk. To address this issue, this paper proposes a new distributed UAV swarm exploration framework. First, we introduce an improved global exploration strategy that combines the exploration task requirements with the surrounding obstacle distribution to plan an efficient and safe coverage path. Secondly, we design a collision risk prediction method based on relative distance and relative velocity, which effectively assists UAVs in making timely collision avoidance decisions. Lastly, we propose a multi-objective local trajectory optimization function that considers the positions of UAVs and static obstacles, thereby planning safe flight trajectories. Extensive simulations and real-world experiments demonstrate that this framework enables safe and efficient exploration in complex environments.

Object detection serves as a challenging yet crucial task in computer vision. Despite significant advancements, modern detectors remain struggling with task alignment between localization and classification. In this paper, Global Collaborative Learning (GCL) is introduced to address these challenges from often-overlooked perspectives. First, the essence of GCL is reflected in the label assignment of the detector. Adjusting the loss function to transform samples with strong localization yet weak classification into high-quality samples in both tasks, provides more effective training signals, enabling the model to capture key consistent features. Second, the spirit of GCL is embodied in the head design. By enabling global feature interaction within the decoupled head, the approach ensures that final predictions are made more comprehensively and robustly, thereby preventing the two independent branches from converging into suboptimal solutions for their respective tasks. Extensive experiments on the challenging MS COCO and CrowdHuman datasets demonstrate that the proposed GCL method substantially enhances performance and generalization capabilities.