Search Results (69)

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem. Full article

Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article

Atrial fibrillation remains the most frequent sustained arrhythmia, particularly in the elderly population, and is associated with increased risks of stroke, heart failure, and reduced quality of life. While catheter ablation is widely used for rhythm control, its efficacy is limited in persistent and long-standing atrial fibrillation. Over the past two decades, minimally invasive surgical strategies have emerged as effective alternatives, aiming to replicate the success of the Cox-Maze procedure while reducing surgical trauma. This overview critically summarizes the current minimally invasive techniques available for atrial fibrillation treatment, including mini-thoracotomy ablation, thoracoscopic ablation, and hybrid procedures such as the convergent approach. These methods offer the potential for durable sinus rhythm restoration by enabling direct visualization, transmural lesion creation, and left atrial appendage exclusion, with lower perioperative morbidity compared to traditional open surgery. The choice of energy source plays a key role in lesion efficacy and safety. Particular attention is given to the technical steps of each procedure, patient selection criteria, and the role of left atrial appendage closure in stroke prevention. Hybrid strategies, which combine epicardial surgical ablation with endocardial catheter-based procedures, have shown encouraging outcomes in patients with refractory or long-standing atrial fibrillation. Despite the steep learning curve, minimally invasive techniques provide significant benefits in terms of recovery time, reduced hospital stay, and fewer complications. As evidence continues to evolve, these approaches represent a key advancement in the surgical management of atrial fibrillation, deserving integration into contemporary treatment algorithms and multidisciplinary heart team planning. Full article

To enhance pulsar candidate recognition performance and improve model generalization, this paper proposes the cross-feature hybrid associative prior network (CFHAPNet). CFHAPNet incorporates a novel architecture and strategies to integrate multi-class heterogeneous feature subimages from each candidate into multi-channel data processing. By implementing cross-attention mechanisms and other enhancements for multi-view feature interactions, the model significantly strengthens its ability to capture fine-grained image texture details and weak prior semantic information. Through comparative analysis of feature weight similarity between subimages and average fusion weights, CFHAPNet efficiently identifies and filters genuine pulsar signals from candidate images collected across astronomical observatories. Additionally, refinements to the original loss function enhance convergence, further improving recognition accuracy and stability. To validate CFHAPNet’s efficacy, we compare its performance against several state-of-the-art methods on diverse datasets. The results demonstrate that under similar data scales, our approach achieves superior recognition performance. Notably, on the FAST dataset, the accuracy, recall, and F1-score reach 97.5%, 98.4%, and 98.0%, respectively. Ablation studies further reveal that the proposed enhancements improve overall recognition performance by approximately 5.6% compared to the original architecture, achieving an optimal balance between recognition precision and computational efficiency. These improvements make CFHAPNet a strong candidate for future large-scale pulsar surveys using new sensor systems. Full article

Economic dispatch (ED) is one of the most important problems in terms of energy planning, management, and operation in power systems. This study presents a snow ablation optimizer (SAO) algorithm developed with the fuzzy-based fitness–distance balance (FFDB) method for solving ED problems in small-, medium- and large-scale electric power systems and determining the optimal operating values of fossil fuel thermal generation units. The FFDB-based SAO algorithm (FFDBSAO) controls early convergence problems through balancing exploration–exploitation and improves the solving of high-dimensional optimization problems. In the light of extensive experimental studies conducted on CEC2020, CEC2022, and classical benchmark test functions, the FFDBSAO2 algorithm has shown superior performance against its competitors. Wilcoxon and Friedman’s statistical analysis results confirm the performance and efficiency of the algorithm. Moreover, the proposed algorithm significantly reduces total fuel cost by optimizing fossil fuel thermal generation units. According to the results, the scalability and robustness of the algorithm make it a valuable tool for solving large-scale optimization problems in the planning of electric power systems. Full article

Matching constraints in an analog integrated circuit (IC) are critical to optimizing layout performance. To extract these matching constraints accurately and efficiently from the netlist, we propose the heterogeneous matching constraint extraction graph neural network (MCE-HGCN). First, the netlist is mapped into a heterogeneous attribute multi-graph, and based on the characteristics of analog IC matching constraints, a mixed-domain attention mechanism is developed to leverage both the topology information and node attributes in the graph to characterize node embeddings. A matching classifier, implemented using the support vector machine (SVM), is then employed to classify different types of matching constraints from the netlist. Additionally, a matching filter is introduced to remove interference terms. Experimental results demonstrate that the MCE-HGCN model converges effectively with small datasets. In the matching prediction process, the mean F1 score reached 0.917 across different netlist processes and circuit types while maintaining a shorter runtime compared to other methods. Ablation experiments also show that incorporating the mixed-domain attention mechanism and the matching filter individually leads to significant performance improvements. Overall, MCE-HGCN excels at extracting matching constraints from various analog circuits and processes, offering valuable insights for placement guidance and enhancing the efficiency of analog IC layout design. Full article

Search and rescue (SAR) operations in post-earthquake environments are hindered by unseen environment conditions and uncertain victim locations. While reinforcement learning (RL) has been used to enhance unmanned aerial vehicle (UAV) navigation in such scenarios, its limited generalisation to novel environments, such as post-disaster environments, remains a challenge. To deal with this issue, this paper proposes an RL-based framework that combines the principles of lifelong learning and eligibility traces. Here, the approach uses a shaping reward heuristic based on pre-training experiences obtained from similar environments to improve generalisation, and simultaneously, eligibility traces are used to accelerate convergence of the overall approach. The combined contributions allows the RL algorithm to adapt to new environments, whilst ensuring fast convergence, critical for rescue missions. Extensive simulation studies show that the proposed framework can improve the average reward return by 46% compared to baseline RL algorithms. Ablation studies are also conducted, which demonstrate a 23% improvement in the overall reward score in environments with different complexities and a 56% improvement in scenarios with varying numbers of trapped individuals. Full article

To address the issues of uneven initial distribution and limited search accuracy with the traditional divergent quantum-inspired differential search (DCS) algorithm, a hybrid multi-strategy variant, termed DQDCS, is proposed. This improved version overcomes these limitations by integrating the refined set strategy and clustering process for population initialization, along with the double Q-learning model to balance exploration and exploitation This enhanced version replaces the conventional pseudo-random initialization with a refined set generated through a clustering process, thereby significantly improving population diversity. A novel position update mechanism is introduced based on the original equation, enabling individuals to effectively escape from local optima during the iteration process. Additionally, the table reinforcement learning model (double Q-learning model) is integrated into the original algorithm to balance the probabilities between exploration and exploitation, thereby accelerating the convergence towards the global optimum. The effectiveness of each enhancement is validated through ablation studies, and the Wilcoxon rank-sum test is employed to assess the statistical significance of performance differences between DQDCS and other classical algorithms. Benchmark simulations are conducted using the CEC2019 and CEC2022 test functions, as well as two well-known constrained engineering design problems. The comparison includes both recent state-of-the-art algorithms and improved optimization methods. Simulation results demonstrate that the incorporation of the refined set and clustering process, along with the table reinforcement learning model (double Q-learning model) mechanism, leads to superior convergence speed and higher optimization precision. Full article

The gearbox, as an important transmission component in wind turbines, connects the blades to the generator and is responsible for converting wind energy into mechanical energy and transmitting it to the generator. Its ability to reduce vibrations directly affects the operational lifespan of the wind turbine. When designing the gearbox’s elastic support structure, it is essential to evaluate how the design parameters influence various performance metrics. Neural networks offer a powerful means of capturing and interpreting the intricate associations linking structural parameters with performance metrics. However, conventional neural networks are usually optimized for a single task, failing to fully account for task differences and shared information. This can lead to task conflicts or insufficient feature modeling, which in turn affects the learning efficiency of inter-task correlations. Furthermore, physical experiments are costly and provide limited training, making it difficult to meet the large-scale dataset requirements for neural network training. To address the high cost and limited scalability of traditional physical testing for gearbox rubber damping structures, in this study, we propose a low-cost performance prediction method that replaces expensive experiments with simulation-driven dataset generation. An optimal Latin hypercube sampling technique is employed to generate high-quality data at minimal cost. On this basis, a multi-task prediction model called multi-gate mixture-of-experts with LSTM (PLE-LSTM) is constructed. The adaptive gating mechanism, hierarchical nonlinear transformation, and effective capture of temporal dynamics in the LSTM significantly enhance the model’s ability to model complex nonlinear patterns. During training, a dynamic weighting strategy named GradNorm is utilized to counteract issues like the early stabilization in multi-task loss convergence and the uneven minimization of loss values. Finally, ablation experiments conducted on different datasets validate the effectiveness of this approach, with experimental results demonstrating its success. Full article

This study proposes an unmanned combat aerial vehicle (UCAV)-oriented hierarchical reinforcement learning framework to address the temporal abstraction challenge in autonomous within-visual-range air combat (WVRAC) for UCAVs. The incorporation of maximum-entropy objectives within the MEOL framework facilitates the optimization of both autonomous low-level tactical discovery and high-level option selection. At the low level, three tactical policies (angle, snapshot, and energy tactics) are designed with reward functions informed by expert knowledge, while the high-level policy dynamically terminates current tactics and selects new ones through sparse reward learning, thus overcoming the limitations of fixed-duration tactical execution. Furthermore, a novel automatic curriculum generation mechanism based on Wasserstein Generative Adversarial Networks (WGANs) is introduced to enhance training efficiency and adaptability to diverse initial combat conditions. Extensive experiments conducted in UCAV air combat simulations have shown that MEOL not only achieves significantly better win rates than other policies when training against rule-based opponents, but also that MEOC achieves superior results in tests against tactical intra-option policies as well as other option learning policies. The framework facilitates dynamic termination and switching of tactics, thereby addressing the limitations of fixed-duration hierarchical methods. Ablation studies confirm the effectiveness of WGAN-based curricula in accelerating policy convergence. Additionally, the visual analysis of UCAVs’ flight logs validates the learned hierarchical decision-making process, showcasing the interplay between tactical selection and manoeuvring execution. This research provides novel methodologies combining hierarchical reinforcement learning with tactical domain knowledge for the autonomous decision-making of UCAVs in complex air combat scenarios. Full article

Accurate prediction of the state of health (SOH) in lithium batteries (LiBs) is essential for ensuring operational safety, extending battery lifespan, and enabling effective second-life applications. However, achieving precise SOH prediction under small-sample conditions remains a significant challenge due to inherent variability among battery cells and capacity recovery phenomena, which result in irregular degradation patterns and hinder effective feature extraction. To overcome these challenges, this study introduces an advanced autoencoder-based method specifically designed for SOH prediction in small-sample scenarios. This method employs a multi-encoder structure—comprising token, positional, and temporal encoders—to comprehensively capture the multi-dimensional characteristics of SOH sequences. Furthermore, a BHTP module is integrated to facilitate feature fusion and enhance the model’s stability and interpretability. By utilizing a pre-training and fine-tuning strategy, the proposed method effectively reduces computational complexity and the number of model parameters while maintaining high prediction accuracy. The validation of the NASA 18650 lithium cobalt oxide battery dataset under various discharge strategies shows that the proposed method achieves fast convergence and outperforms traditional prediction methods. Compared with other models, our method reduces the RMSE by 0.004 and the MAE by 0.003 on average. In addition, ablation experiments show that the addition of the multi-encoder structure and the BHTP module improves the RMSE and MAE by 0.008 and 0.007 on average, respectively. These results highlight the robustness and utility of the proposed method in real battery management systems, especially under data-scarce conditions. Full article

With the rapid development of research on Wireless Radar Sensor Networks (WRSNs), security issues have become a major challenge. Recent studies have highlighted numerous security threats in WRSNs. Given their widespread application value, the operational security of WRSNs needs to be ensured. This study focuses on the problem of malware propagation in WRSNs. In this study, the complex characteristics of WRSNs are considered to construct the epidemic VCISQ model. The model incorporates necessary factors such as node density, Rayleigh fading channels, and time delay, which were often overlooked in previous studies. This model achieves a breakthrough in accurately describing real-world scenarios of malware propagation in WRSNs. To control malware spread, a hybrid control strategy combining quarantine and patching measures are introduced. In addition, the optimal control method is used to minimize control costs. Considering the robustness and adaptability of the control method, two model-free reinforcement learning (RL) strategies are proposed: Proximal Policy Optimization (PPO) and Multi-Agent Proximal Policy Optimization (MAPPO). These strategies reformulate the original optimal control problem as a Markov decision process. To demonstrate the superiority of our approach, multi-dimensional ablation studies and numerical experiments are conducted. The results show that the hybrid control strategy outperforms single strategies in suppressing malware propagation and reducing costs. Furthermore, the experiments reveal the significant impact of time delays on the dynamics of the VCISQ model and control effectiveness. Finally, the PPO and MAPPO algorithms demonstrate superior performance in control costs and convergence compared to traditional RL algorithms. This highlights their effectiveness in addressing malware propagation in WRSNs. Full article

The case of randomly scattered maize seeds presents the problem of complex background and high density, making detection difficult. To address this challenge, this paper proposes an improved YOLOv8n model (OBW-YOLO) for detecting damaged maize seeds. The introduction of the C2f-ODConv module enhances the model’s ability to extract damaged features, especially in complex scenarios, allowing for better capture of local information. The improved BIMFPN module optimizes the fusion of shallow and deep features, reduces detail loss, and improves detection accuracy. To accelerate model convergence and improve detection precision, the traditional bounding box loss function has been replaced by WIoU, significantly enhancing both accuracy and convergence speed. Experimental results show that the OBW-YOLO model achieves a detection accuracy of 93.6%, with mAP@0.5 and mAP@0.5:0.95 reaching 97.6% and 84.8%, respectively, which represents an improvement of 2.5%, 1%, and 1.2% compared to previous models. Additionally, the number of parameters and model sizes have been reduced by 33% and 22.5%, respectively. Compared to other YOLO models, OBW-YOLO demonstrates significant advantages in both accuracy and mAP. Ablation experiments further validate the effectiveness of the improvements, and heatmap analysis shows that the improved model is more precise in capturing the damaged features of maize seeds. These improvements enable the OBW-YOLO model to achieve significant performance gains in maize seed damage detection, providing an effective solution for the automated quality inspection of maize seeds in the agricultural field. Full article

Policy space response oracles (PSRO) is an important algorithmic framework for approximating Nash equilibria in two-player zero-sum games. Enhancing policy diversity has been shown to improve the performance of PSRO in this approximation process significantly. However, existing diversity metrics are often prone to redundancy, which can hinder optimal strategy convergence. In this paper, we introduce the policy similarity measure (PSM), a novel approach that combines Gaussian and cosine similarity measures to assess policy similarity. We further incorporate the PSM into the PSRO framework as a regularization term, effectively fostering a more diverse policy population. We demonstrate the effectiveness of our method in two distinct game environments: a non-transitive mixture model and Leduc poker. The experimental results show that the PSM-augmented PSRO outperforms baseline methods in reducing exploitability by approximately 7% and exhibits greater policy diversity in visual analysis. Ablation studies further validate the benefits of combining Gaussian and cosine similarities in cultivating more diverse policy sets. This work provides a valuable method for measuring and improving the policy diversity in two-player zero-sum games. Full article