Search Results (323)

To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source heterogeneous data and subsequently utilizing text mining and natural language processing techniques to extract design themes and method elements. A “theme–stage–attribute” three-dimensional mapping model is established to achieve semantic coupling of knowledge. The BERT-BiLSTM-CRF (Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) model is employed for entity recognition and relation extraction, while the Sentence-BERT (Sentence Bidirectional Encoder Representations from Transformers) model is used to perform multi-source knowledge fusion. The Neo4j graph database facilitates knowledge storage, visualization, and querying, forming the basis for developing a prototype of a design method recommendation system. The framework’s effectiveness was validated through experiments on extraction performance and knowledge graph quality. The results demonstrate that the framework achieves an F1 score of 91.2% for knowledge extraction, and an 8.44% improvement over the baseline. The resulting graph’s node and relation coverage reached 94.1% and 91.2%, respectively. In complex semantic query tasks, the framework shows a significant advantage over traditional classification systems, achieving a maximum F1 score of 0.97. It can effectively integrate dispersed knowledge in the field of design methods and support method matching throughout the entire design process. This research is of significant value for advancing knowledge management and application in innovative product design. Full article

Reliable three-dimensional (3D) object detection is critical for intelligent vehicles to ensure safety in complex traffic environments, and recent progress in multi-modal sensor fusion, particularly between LiDAR and camera, has advanced environment perception in urban driving. However, existing approaches remain vulnerable to occlusions and dense traffic, where depth estimation errors, calibration deviations, and cross-modal misalignment are often exacerbated. To overcome these limitations, we propose BEVAlign, a local–global feature alignment framework designed to generate unified BEV representations from heterogeneous sensor modalities. The framework incorporates a Local Alignment (LA) module that enhances camera-to-BEV view transformation through graph-based neighbor modeling and dual-depth encoding, mitigating local misalignment from depth estimation errors. To further address global misalignment in BEV representations, we present the Global Alignment (GA) module comprising a bidirectional deformable cross-attention (BDCA) mechanism and CBR blocks. BDCA employs dual queries from LiDAR and camera to jointly predict spatial sampling offsets and aggregate features, enabling bidirectional alignment within the BEV domain. The stacked CBR blocks then refine and integrate the aligned features into unified BEV representations. Experiment on the nuScenes benchmark highlights the effectiveness of BEVAlign, which achieves 71.7% mAP, outperforming BEVFusion by 1.5%. Notably, it achieves strong performance on small and occluded objects, particularly in dense traffic scenarios. These findings provide a basis for advancing cooperative environment perception in next-generation intelligent vehicle systems. Full article

Emerging complex multi-step attacks such as Advanced Persistent Threats (APTs) pose significant risks to national economic development, security, and social stability. Effectively detecting these sophisticated threats is a critical challenge. While deep learning methods show promise in identifying unknown malicious behaviors, they often struggle with fragmented modal information, limited feature representation, and generalization. To address these limitations, we propose GATransformer, a new dual-modal detection method that integrates topological structure analysis with temporal sequence modeling. Its core lies in a cross-attention semantic fusion mechanism, which deeply integrates heterogeneous features and effectively mitigates the constraints of unimodal representations. GATransformer reconstructs network behavior representation via a parallel processing framework in which graph attention captures intricate spatial dependencies, and self-attention focuses on modeling long-range temporal correlations. Experimental results on the CIDDS-001 and CIDDS-002 datasets demonstrate the superior performance of our method compared to baseline methods with detection accuracies of 99.74% (nodes) and 88.28% (edges) on CIDDS-001 and 99.99% and 99.98% on CIDDS-002, respectively. Full article

Trajectory planning for logistics UAVs in complex environments faces a key challenge: balancing global search breadth with fine constraint accuracy. Traditional algorithms struggle to simultaneously manage large-scale exploration and complex constraints, and lack sufficient modeling capabilities for multi-UAV systems, limiting cluster logistics efficiency. To address these issues, we propose a GWOP algorithm based on dual-layer fusion of GWO and GRPO and incorporate a graph attention network (GAT). First, CEC2017 benchmark functions evaluate GWOP convergence accuracy and balanced exploration in multi-peak, high-dimensional environments. A hierarchical collaborative architecture, “GWO global coarse-grained search + GRPO local fine-tuning”, is used to overcome the limitations of single-algorithm frameworks. The GAT model constructs a dynamic “environment–UAV–task” association network, enabling environmental feature quantification and multi-constraint adaptation. A multi-factor objective function and constraints are integrated with multi-task cascading decoupling optimization to form a closed-loop collaborative optimization framework. Experimental results show that in single UAV scenarios, GWOP reduces flight cost (FV) by over 15.85% on average. In multi-UAV collaborative scenarios, average path length (APL), optimal path length (OPL), and FV are reduced by 4.08%, 14.08%, and 24.73%, respectively. In conclusion, the proposed method outperforms traditional approaches in path length, obstacle avoidance, and trajectory smoothness, offering a more efficient planning solution for smart logistics. Full article

Emerging technologies are revolutionizing power system operation and maintenance. Intelligent state perception is pivotal for stable grid operation, with small object detection technology being vital for identifying minor hazards in power facilities. However, challenges like small object size, low resolution, occlusion, and low confidence arise in small object detection for power operation and maintenance. This paper proposes PyraFAN, a feature fusion method designed for small object detection, and introduces a cloud-edge collaborative inference based smart detection method. This method boosts detection accuracy while ensuring real-time performance. Additionally, a graph-guided distillation method is developed for edge models. By quantifying model performance and task similarity, multi-model collaborative training is realized to improve detection accuracy. Experimental results show that compared with standalone edge models, the proposed method improves detection accuracy by 6.98% and reduces the false negative rate by 19.56%. The PyraFAN module can enhance edge model detection accuracy by approximately 12.2%. Updating edge models via cloud model distillation increases the mAP@0.5 of edge models by 2.7%. Compared to cloud models, the cloud-edge collaboration method reduces average inference latency by 0.8%. This research offers an effective solution for improving the accuracy of deep learning based small object detection in power operation and maintenance within cloud-edge computing environments. Full article

The globalization of hardware design flows has increased the risk of Hardware Trojan (HT) insertion during the design phase. Graph-based learning methods have shown promise for HT detection at the Register Transfer Level (RTL). However, most existing approaches rely on representing RTL designs through a single graph structure. This single-view modeling paradigm inherently constrains the model’s ability to perceive complex behavioral patterns, consequently limiting detection performance. To address these limitations, we propose GNN-MFF, an innovative multi-view feature fusion model based on Graph Neural Networks (GNNs). Our approach centers on joint multi-view modeling of RTL designs to achieve a more comprehensive representation. Specifically, we construct complementary graph-structural views: the Abstract Syntax Tree (AST) capturing structure information, and the Data Flow Graph (DFG) modeling logical dependency relationships. For each graph structure, customized GNN architectures are designed to effectively extract its features. Furthermore, we develop a feature fusion framework that leverages a multi-head attention mechanism to deeply explore and integrate heterogeneous features from distinct views, thereby enhancing the model’s capacity to structurally perceive anomalous logic patterns. Evaluated on an extended Trust-Hub-based HT benchmark dataset, our model achieves an average F1-score of 97.08% in automated detection of unseen HTs, surpassing current state-of-the-art methods. Full article

Cyberattacks, especially Advanced Persistent Threats (APTs), have become more complex. These evolving threats challenge traditional defense systems, which struggle to counter long-lasting and covert attacks. Cybersecurity Knowledge Graphs (CKGs), enabled through the integration of multi-source CTI, introduce novel approaches for proactive defense. However, building CKGs faces challenges such as unclear terminology, overlapping entity relationships in attack chains, and differences in CTI across sources. To tackle these challenges, we propose the CyberKG framework, which improves entity recognition and relation extraction using a SecureBERT_Plus-BiLSTM-Attention-CRF joint architecture. Semantic features are captured using a domain-adapted SecureBERT_Plus model, while temporal dependencies are modeled through BiLSTM. Attention mechanisms highlight key cross-sentence relationships, while CRF incorporates ATT&CK rule constraints. Hierarchical clustering (HAC), based on contextual embeddings, facilitates dynamic entity disambiguation and semantic fusion. Experimental evaluations on the DNRTI and MalwareDB datasets demonstrate strong performance in extraction accuracy, entity normalization, and the resolution of overlapping relations. The constructed knowledge graph supports APT tracking, attack-chain provenance, proactive defense prediction. Full article

The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view remote sensing data increase structural heterogeneity while simultaneously hindering effective information extraction and fusion. Existing MVGC methods cannot selectively integrate and fully refine both graph structure and node attribute information for consensus representation learning. Furthermore, current methods tend to overlook distant nodes, thus failing to capture the global graph structure. To solve these issues, we propose a novel method called Improved Multi-View Graph Clustering with Global Graph Refinement (IMGCGGR). Specifically, we first design a view-specific fusion network (VSFN) to extract and integrate node attribute and structural information into view-specific representation for each view. VSFN not only utilizes a global self-attention mechanism to enhance the global properties of structural information but also constructs a clustering loss through a self-supervised strategy to guide the view-specific clustering distribution assignment. Moreover, to enhance the capability of view-specific representation, a learnable attention-driven aggregation strategy is introduced to flexibly fuse the attribute and structural feature. Then, we adopt a cross-view fusion module to adaptively merge multiple view-specific representations for generating the final consensus representation. Comprehensive experiments show that IMGCGGR achieves significant clustering performance improvements over baseline methods across various benchmark datasets. Full article

Cameras and LiDAR are the primary sensors utilized in contemporary 3D object perception, leading to the development of various multi-modal detection algorithms for images, point clouds, and their fusion. Given the demanding accuracy requirements in autonomous driving environments, traditional multi-modal fusion techniques often overlook critical information from individual modalities and struggle to effectively align transformed features. In this paper, we introduce an improved modal interaction strategy, called CrossInteraction. This method enhances the interaction between modalities by using the output of the first modal representation as the input for the second interaction enhancement, resulting in better overall interaction effects. To further address the challenge of feature alignment errors, we employ a graph convolutional network. Finally, the prediction process is completed through a cross-attention mechanism, ensuring more accurate detection out- comes. Full article

In the era of big-data-driven multi-platform and multimodal health information dissemination, the rapid spread of false and misleading content poses a critical threat to public health awareness and decision making. To address this issue, a dual-stream Transformer-based multimodal health misinformation detection framework is presented, incorporating a symbol drift detection module, a symbol-aware text graph neural network, and a crossmodal alignment fusion module. The framework enables precise identification of implicit misleading health-related symbols, comprehensive modeling of textual dependency structures, and robust detection of crossmodal semantic conflicts. A domain-specific health-symbol-sensitive lexicon is constructed, and contextual drift intensity is quantitatively measured and embedded as explicit features into the text GNN. Bidirectional cross-attention and contrastive learning are further employed to enhance crossmodal semantic alignment. Extensive experiments on a large-scale real-world multimodal health information dataset, encompassing heterogeneous data sources typical of big data environments, demonstrate that the proposed method consistently outperforms state-of-the-art baselines in CTR prediction, multimodal recommendation, and ranking tasks. The results indicate substantial improvements in both accuracy and ranking quality, while ablation studies further verify the contributions of symbol drift modeling, graph-structured representation, and crossmodal fusion. Overall, the proposed approach advances big data analytics for multimodal misinformation detection and provides an interpretable and scalable solution for public health communication governance. Full article

With the advancement of new power system construction, thermostatically controlled loads represented by regional air conditioning systems are being extensively integrated into the grid, leading to a surge in the number of user nodes. This large-scale integration of new loads creates challenges for the grid, as the resulting load data exhibits strong periodicity and randomness over time. These characteristics are influenced by factors like temperature and user behavior. At the same time, spatially adjacent nodes show similarities and clustering in electricity usage. This creates complex spatiotemporal coupling features. These complex spatiotemporal characteristics challenge traditional forecasting methods. Their high model complexity and numerous parameters often lead to overfitting or the curse of dimensionality, which hinders both prediction accuracy and efficiency. To address this issue, this paper proposes a load forecasting method based on spatiotemporal partitioning and collaborative cross-regional attention. First, a spatiotemporal similarity matrix is constructed using the Shape Dynamic Time Warping (ShapeDTW) algorithm and an adaptive Gaussian kernel function based on the Haversine distance. Spectral clustering combined with the Gap Statistic criterion is then applied to adaptively determine the optimal number of partitions, dividing all load nodes in the power grid into several sub-regions with homogeneous spatiotemporal characteristics. Second, for each sub-region, a local Spatiotemporal Graph Convolutional Network (STGCN) model is built. By integrating gated temporal convolution with spatial feature extraction, the model accurately captures the spatiotemporal evolution patterns within each sub-region. On this basis, a cross-regional attention mechanism is designed to dynamically learn the correlation weights among sub-regions, enabling collaborative fusion of global features. Finally, the proposed method is evaluated on a multi-node load dataset. The effectiveness of the approach is validated through comparative experiments and ablation studies (that is, by removing key components of the model to evaluate their contribution to the overall performance). Experimental results demonstrate that the proposed method achieves excellent performance in short-term load forecasting tasks across multiple nodes. Full article

Multi-label arrhythmia classification from 12-lead ECG signals is a tricky problem, including spatiotemporal feature extraction, feature fusion, and class imbalance. To address these issues, a multi-scale temporal–dynamic graph convolutional with orthogonal gates method, termed MST-DGCN, is proposed for ECG arrhythmia classification. In this method, a temporal–dynamic graph convolution with dynamic adjacency matrices is used to learn spatiotemporal patterns jointly, and an orthogonal gated fusion mechanism is used to eliminate redundancy, so as to strength their complementarity and independence through adjusting the significance of features dynamically. Moreover, a multi-instance learning strategy is proposed to alleviate class imbalance by adjusting the proportion of a few arrhythmia samples through adaptive label allocation. After validating on the St Petersburg INCART dataset under stringent inter-patient settings, the experimental results show that the proposed MST-DGCN method can achieve the best classification performance with an F1-score of 73.66% (+6.2% over prior baseline methods), with concurrent improvements in AUC (70.92%) and mAP (85.24%), while maintaining computational efficiency. Full article

Multimodal Sentiment Analysis (MSA) aims to predict subjective human emotions by leveraging multimodal information. However, existing research inadequately utilizes explicit sentiment semantic information at the lexical level in text and overlooks noise interference from non-dominant modalities, such as irrelevant movements in visual modalities and background noise in audio modalities. To address this issue, we propose a multimodal sentiment analysis model based on knowledge enhancement and text-guided learning (MMKT). The model constructs a sentiment knowledge graph for the textual modality using the SenticNet knowledge base. This graph directly annotates word-level sentiment polarity, strengthening the model’s understanding of emotional vocabulary. Furthermore, global sentiment knowledge features are generated through graph embedding computations to enhance the multimodal fusion process. Simultaneously, a dynamic text-guided learning approach is introduced, which dynamically leverages multi-scale textual features to actively suppress redundant or conflicting information in visual and audio modalities, thereby generating purer cross-modal representations. Finally, concatenated textual features, cross-modal features, and knowledge features are utilized for sentiment prediction. Experimental results on the CMU-MOSEI and Twitter2019 dataset demonstrate the superior performance of the MMKT model. Full article

Urbanization and traffic congestion caused by the surge in car ownership have exacerbated energy consumption and carbon emissions, and dynamic traffic flow prediction and energy-saving route planning have become the key to solving this problem. Dynamic traffic flow prediction accurately captures the spatio-temporal changes of traffic flow through advanced algorithms and models, providing prospective information for traffic management and travel decision-making. Energy-saving route planning optimizes travel routes based on prediction results, reduces the time vehicles spend on congested road sections, thereby reducing fuel consumption and exhaust emissions. However, there are still many shortcomings in the current relevant research, and the existing research is mostly isolated and applies a single model, and there is a lack of systematic comparison of the adaptability, generalization ability and fusion potential of different models in various scenarios, and the advantages of heterogeneous graph neural networks in integrating multi-source heterogeneous data in traffic have not been brought into play. This paper systematically reviews the relevant global studies from 2020 to 2025, focuses on the integration path of dynamic traffic flow prediction methods and energy-saving route planning, and reveals the advantages of LSTM, graph neural network and other models in capturing spatiotemporal features by combing the application of statistical models, machine learning, deep learning and mixed methods in traffic forecasting, and comparing their performance with RMSE, MAPE and other indicators, and points out that the potential of heterogeneous graph neural networks in multi-source heterogeneous data integration has not been fully explored. Aiming at the problem of disconnection between traffic prediction and path planning, an integrated framework is constructed, and the real-time prediction results are integrated into path algorithms such as A* and Dijkstra through multi-objective cost functions to balance distance, time and energy consumption optimization. Finally, the challenges of data quality, algorithm efficiency, and multimodal adaptation are analyzed, and the development direction of standardized evaluation platform and open source toolkit is proposed, providing theoretical support and practical path for the sustainable development of intelligent transportation systems. Full article

Lithology identification is a critical technology for geological resource exploration and engineering safety assessment. However, traditional methods suffer from insufficient feature representation and low classification accuracy due to challenges such as weathering, vegetation cover, and spectral overlap in complex sedimentary rock regions. This study proposes a hierarchical multi-feature random forest algorithm based on Feature-Preserved Compressive Sampling (FPCS). Using 3D laser point cloud data from the Manas River outcrop in the southern margin of the Junggar Basin as the test area, we integrate graph signal processing and multi-scale feature fusion to construct a high-precision lithology identification model. The FPCS method establishes a geologically adaptive graph model constrained by geodesic distance and gradient-sensitive weighting, employing a three-tier graph filter bank (low-pass, band-pass, and high-pass) to extract macroscopic morphology, interface gradients, and microscopic fracture features of rock layers. A dynamic gated fusion mechanism optimizes multi-level feature weights, significantly improving identification accuracy in lithological transition zones. Experimental results on five million test samples demonstrate an overall accuracy (OA) of 95.6% and a mean accuracy (mAcc) of 94.3%, representing improvements of 36.1% and 20.5%, respectively, over the PointNet model. These findings confirm the robust engineering applicability of the FPCS-based hierarchical multi-feature approach for point cloud lithology identification. Full article