Search Results (8,359)

In underground coal mines, narrow and irregular tunnels, dust, and gas hazards pose significant challenges to robotic path planning, particularly for shotcrete operations. The traditional A* algorithm has the limitations of limited safety, excessive node expansion, and insufficient dynamic obstacle avoidance capabilities. To address these, a hybrid algorithm integrating adaptive A*, Q-learning, and the Dynamic Window Approach (DWA) is proposed. The A* component is enhanced through improvements to its evaluation function and node selection strategy, incorporating dynamically adjustable neighborhood sampling to improve search efficiency. Q-learning re-plans unsafe trajectories in complex environments using a redesigned reward mechanism and an adaptive exploration strategy. The DWA module facilitates real-time obstacle avoidance in dynamic scenarios by optimizing both the velocity space and the trajectory evaluation process. The simulation results indicate that the proposed algorithm reduces the number of path nodes by approximately 30%, reduces the computational time by approximately 20% on 200 × 200 grids, and increases the path length by only 10%. These results demonstrate that the proposed approach effectively balances global path optimality with local adaptability, significantly improving the safety and real-time performance in complex underground environments. Full article

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics. Full article

General large language models (LLMs) often suffer from semantic misinterpretation, information redundancy, and hallucinated content when applied to educational question-answering tasks. These issues hinder their effectiveness in supporting students’ specialized course learning and self-directed study. To address these challenges, this study proposes an intelligent tutoring model that integrates a knowledge graph with a large language model (KG-CQ). Focusing on the Data Structures (C Language) course, the model constructs a course-specific knowledge graph stored in a Neo4j graph database. It incorporates modules for knowledge retrieval, domain-specific question answering, and knowledge extraction, forming a closed-loop system designed to enhance semantic comprehension and domain adaptability. A total of 30 students majoring in Educational Technology at H University were randomly assigned to either an experimental group or a control group, with 15 students in each. The experimental group utilized the KG-CQ model during the answering process, while the control group relied on traditional learning methods. A total of 1515 data points were collected. Experimental results show that the KG-CQ model performs well in both answer accuracy and domain relevance, accompanied by high levels of student satisfaction. The model effectively promotes self-directed learning and provides a valuable reference for the development of knowledge-enhanced question-answering systems in educational settings. Full article

Fog computing is increasingly preferred over cloud computing for processing tasks from Internet of Things (IoT) devices with limited resources. However, placing tasks and allocating resources in distributed and dynamic fog environments remains a major challenge, especially when trying to meet strict Quality of Service (QoS) requirements. Deep reinforcement learning (DRL) has emerged as a promising solution to these challenges, offering adaptive, data-driven decision-making in real-time and uncertain conditions. While several surveys have explored DRL in fog computing, most focus on traditional centralized offloading approaches or emphasize reinforcement learning (RL) with limited integration of deep learning. To address this gap, this paper presents a comprehensive and focused survey on the full-scale application of DRL to the task offloading problem in fog computing environments involving multiple user devices and multiple fog nodes. We systematically analyze and classify the literature based on architecture, resource allocation methods, QoS objectives, offloading topology and control, optimization strategies, DRL techniques used, and application scenarios. We also introduce a taxonomy of DRL-based task offloading models and highlight key challenges, open issues, and future research directions. This survey serves as a valuable resource for researchers by identifying unexplored areas and suggesting new directions for advancing DRL-based solutions in fog computing. For practitioners, it provides insights into selecting suitable DRL techniques and system designs to implement scalable, efficient, and QoS-aware fog computing applications in real-world environments. Full article

The Capacitated Vehicle Routing Problem (CVRP) is a classic combinatorial optimization problem in logistics and distribution, with significant theoretical and practical importance. To address the limitations of traditional evolutionary algorithms—particularly their use of fixed operator selection and simplistic search strategies—this paper proposes a Q-learning-based evolutionary algorithm (QEA). By incorporating a reinforcement learning mechanism, the QEA adaptively selects among multiple neighborhood search operators, effectively balancing global exploration and local exploitation. In addition, a novel insertion-based crossover operator and a set of diverse neighborhood search strategies are designed to further enhance solution quality and search efficiency. Experimental results on a variety of standard CVRP benchmark instances show that the QEA demonstrates a superior performance and strong robustness, significantly outperforming several representative state-of-the-art algorithms for solving the CVRP. These results confirm the effectiveness and practical value of the proposed method. Full article

Multi-UAV cooperative missions demand millisecond-level coordination across three key resource dimensions—battery energy, wireless bandwidth, and onboard computing power—where traditional Min or linearly weighted schedulers struggle to balance safety with efficiency. We propose a prediction-enhanced two-stage T-norm–Choquet–OWA resource aggregator. First, an LSTM-EMA model forecasts resource trajectories 3 s ahead; next, a first-stage T-norm (min) pinpoints the bottleneck resource, and a second-stage Choquet–OWA, driven by an adaptive interaction measure $\varphi$, elastically compensates according to instantaneous power usage, achieving a “bottleneck-first, efficiency-recovery” coordination strategy. Theoretical analysis establishes monotonicity, tight bounds, bottleneck prioritization, and Lyapunov stability, with node-level complexity of only $O\left(1\right)$. In joint simulations involving 360 UAVs, the method holds the average round-trip time (RTT) at 55 ms, cutting latency by 5%, 10%, 15%, and 20% relative to Min, DRL-PPO, single-layer OWA, and WSM, respectively. Jitter remains within 11 ms, the packet-loss rate stays below 0.03%, and residual battery increases by about 12% over the best heuristic baseline. These results confirm the low-latency, high-stability benefits of the prediction-based peak-shaving plus two-stage fuzzy aggregation approach for large-scale UAV swarms. Full article

This study presents a comprehensive comparison between classical sampling-based motion planners from the Open Motion Planning Library (OMPL) and a learning-based planner based on Soft Actor–Critic (SAC) for motion planning in industrial robotic arms. Using a UR3e robot equipped with an RG2 gripper, we constructed a large-scale dataset of over 100,000 collision-free trajectories generated with MoveIt-integrated OMPL planners. These trajectories were used to train a DRL agent via curriculum learning and expert demonstrations. Both approaches were evaluated on key metrics such as planning time, success rate, and trajectory smoothness. Results show that the DRL-based planner achieves higher success rates and significantly lower planning times, producing more compact and deterministic trajectories. Time-optimal parameterization using TOPPRA ensured the dynamic feasibility of all trajectories. While classical planners retain advantages in zero-shot adaptability and environmental generality, our findings highlight the potential of DRL for real-time and high-throughput motion planning in industrial contexts. This work provides practical insights into the trade-offs between traditional and learning-based planning paradigms, paving the way for hybrid architectures that combine their strengths. Full article

Individual recognition of Hu sheep is a core requirement for precision livestock management, significantly improving breeding efficiency and fine management. However, traditional machine vision methods face challenges such as high annotation time costs, the inability to quickly annotate new sheep, and the need for manual intervention and retraining. To address these issues, this study proposes a solution that integrates automatic annotation and transfer learning, developing a sheep face recognition algorithm that adapts to complex farming environments and can quickly learn the characteristics of new Hu sheep individuals. First, through multi-view video collection and data augmentation, a dataset consisting of 82 Hu sheep and a total of 6055 images was created. Additionally, a sheep face detection and automatic annotation algorithm was designed, reducing the annotation time per image to 0.014 min compared to traditional manual annotation. Next, the YOLOv10n-CF-Lite model is proposed, which improved the recognition precision of Hu sheep faces to 92.3%, and the mAP@0.5 to 96.2%. To enhance the model’s adaptability and generalization ability for new sheep, transfer learning was applied to transfer the YOLOv10n-CF-Lite model trained on the source domain (82 Hu sheep) to the target domain (10 new Hu sheep). The recognition precision in the target domain increased from 91.2% to 94.9%, and the mAP@0.5 improved from 96.3% to 97%. Additionally, the model’s convergence speed was improved, reducing the number of training epochs required for fitting from 43 to 14. In summary, the Hu sheep face recognition algorithm proposed in this study improves annotation efficiency, recognition precision, and convergence speed through automatic annotation and transfer learning. It can quickly adapt to the characteristics of new sheep individuals, providing an efficient and reliable technical solution for the intelligent management of livestock. Full article

Urban heat exposure has become an increasingly critical environmental issue under the dual pressures of global climate warming and rapid urbanization, posing significant threats to public health and urban sustainability. However, conventional linear regression models often fail to capture the complex, nonlinear interactions among multiple environmental factors, and studies confined to single LCZ types lack a comprehensive understanding of urban thermal mechanisms. This study takes the central urban area of Zhengzhou as a case and proposes an integrated “Local Climate Zone (LCZ) framework + random forest-based multi-factor contribution analysis” approach. By incorporating multi-temporal Landsat imagery, this method effectively identifies nonlinear drivers of heat exposure across different urban morphological units. Compared to traditional approaches, the proposed model retains spatial heterogeneity while uncovering intricate regulatory pathways among contributing factors, demonstrating superior adaptability and explanatory power. Results indicate that (1) high-density built-up zones (LCZ1 and E) constitute the core of heat exposure, with land surface temperatures (LSTs) 6–12 °C higher than those of natural surfaces and LCZ3 reaching a peak LST of 49.15 °C during extreme heat events; (2) NDVI plays a dominant cooling role, contributing 50.5% to LST mitigation in LCZ3, with the expansion of low-NDVI areas significantly enhancing cooling potential (up to 185.39 °C·km²); (3) LCZ5 exhibits an anomalous spatial pattern with low-temperature patches embedded within high-temperature surroundings, reflecting the nonlinear impacts of urban form and anthropogenic heat sources. The findings demonstrate that the LCZ framework, combined with random forest modeling, effectively overcomes the limitations of traditional linear models, offering a robust analytical tool for decoding urban heat exposure mechanisms and informing targeted climate adaptation strategies. Full article

Accurate identification of the endometrial region is critical for the early detection of endometrial lesions. However, current detection models still face two major challenges when processing endometrial imaging data: (1) In complex and noisy environments, recognition accuracy remains limited, partly due to the insufficient exploitation of color information within the images; (2) Traditional Two-dimensional PCA-based (2DPCA-based) feature selection methods have limited capacity to capture and represent key characteristics of the endometrial region. To address these challenges, this paper proposes a novel algorithm named Feature-Level Image Fusion and Improved Swarm Intelligence Optimization Algorithm (FLFSI), which integrates a learning guided binary particle swarm optimization (BPSO) strategy with an image feature selection and reconstruction framework to enhance the detection of endometrial regions in clinical ultrasound images. Specifically, FLFSI contributes to improving feature selection accuracy and image reconstruction quality, thereby enhancing the overall performance of region recognition tasks. First, we enhance endometrial image representation by incorporating feature engineering techniques that combine structural and color information, thereby improving reconstruction quality and emphasizing critical regional features. Second, the BPSO algorithm is introduced into the feature selection stage, improving the accuracy of feature selection and its global search ability while effectively reducing the impact of redundant features. Furthermore, we refined the BPSO design to accelerate convergence and enhance optimization efficiency during the selection process. The proposed FLFSI algorithm can be integrated into mainstream detection models such as YOLO11 and YOLOv12. When applied to YOLO11, FLFSI achieves 96.6% Box mAP and 87.8% Mask mAP. With YOLOv12, it further improves the Mask mAP to 88.8%, demonstrating excellent cross-model adaptability and robust detection performance. Extensive experimental results validate the effectiveness and broad applicability of FLFSI in enhancing endometrial region detection for clinical ultrasound image analysis. Full article

The large-scale trajectory simulation serves as a fundamental basis for the mission planning of a rocket-powered vehicle swarm. However, the traditional flight trajectory calculation method for a rocket-powered vehicle, which employs strict dynamic and kinematic models, often struggles to meet the temporal requirements of mission planning. To address the challenges of timely computation and intelligent optimization, a segmented training strategy, derived from the domain knowledge of the multi-stage flight characteristics of a rocket-powered vehicle, is integrated into the deep neural network (DNN) method. A high-precision trajectory prediction model that fuses multi-DNN is proposed, which can rapidly generate high-precision trajectory data without depending on accurate dynamic models. Based on the determination of the characteristic parameters derived from rocket-powered trajectory theory, a homemade dataset is constructed through a traditional computation method and utilized to train the DNN model. Extensive and varying numerical simulations are given to substantiate the predictive accuracy, adaptability, and stability of the proposed DNN-based method, and the corresponding comparative tests further demonstrate the effectiveness of the segmented strategy. Additionally, the real-time computational capability is also confirmed by computing the simulation of generating full trajectory data. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

The swift growth of the low-altitude economic ecosystem and Unmanned Aerial Vehicle (UAV) swarm applications across diverse sectors presents significant challenges for engineering managers in terms of effective performance evaluation and operational optimization. Traditional evaluation methods often struggle with the inherent complexities, dynamic nature, and multi-faceted performance criteria of UAV swarms. This study introduces a novel Bayesian Network (BN)-based multicriteria decision-making framework that systematically integrates expert intuition with real-time data. By employing variance decomposition, the framework establishes theoretically grounded, bidirectional mapping between expert-assigned weights and the network’s probabilistic parameters, creating a unified model of subjective expertise and objective data. Comprehensive validation demonstrates the framework’s efficacy in identifying critical performance drivers, including environmental awareness, communication ability, and a collaborative decision. Ultimately, our work provides engineering managers with a transparent and adaptive tool, offering actionable insights to inform resource allocation, guide technology adoption, and enhance the overall operational effectiveness of complex UAV swarm systems. Full article

Credit card fraud remains a major cause of financial loss around the world. Traditional fraud detection methods that rely on supervised learning often struggle because fraudulent transactions are rare compared to legitimate ones, leading to imbalanced datasets. Additionally, the models must be retrained frequently, as fraud patterns change over time and require new labeled data for retraining. To address these challenges, this paper proposes an ensemble unsupervised learning approach for credit card fraud detection that combines Autoencoders (AEs), Self-Organizing Maps (SOMs), and Restricted Boltzmann Machines (RBMs), integrated with an Adaptive Reconstruction Threshold (ART) mechanism. The ART dynamically adjusts anomaly detection thresholds by leveraging the clustering properties of SOMs, effectively overcoming the limitations of static threshold approaches in machine learning and deep learning models. The proposed models, AE-ASOMs (Autoencoder—Adaptive Self-Organizing Maps) and RBM-ASOMs (Restricted Boltzmann Machines—Adaptive Self-Organizing Maps), were evaluated on the Kaggle Credit Card Fraud Detection and IEEE-CIS datasets. Our AE-ASOM model achieved an accuracy of 0.980 and an F1-score of 0.967, while the RBM-ASOM model achieved an accuracy of 0.975 and an F1-score of 0.955. Compared to models such as One-Class SVM and Isolation Forest, our approach demonstrates higher detection accuracy and significantly reduces false positive rates. In addition to its performance, the model offers considerable computational efficiency with a training time of 200.52 s and memory usage of 3.02 megabytes. Full article