Search Results (87)

This study aims to explore the tracking control challenge in a swarm of multiple nonholonomic wheeled mobile robots (NWMRs) by utilizing a distributed leader–follower strategy grounded in the cascade system theory. Firstly, the kinematic control law is developed for the leader by constructing a sliding surface based on the error tracking model with a virtual reference trajectory. Secondly, a communication topology with the desired formation pattern is modeled for the multiple robots by using the graph theory. Further, in the leader–follower NWMR system, each follower lacks direct access to the leader’s information. Therefore, a novel distributed-based controller by PD-based controller for the follower is developed, enabling each follower to obtain the leader’s information. Thirdly, for each case, we give a further analysis of the closed-loop system to guarantee uniform global asymptotic stability with the conditions based on the cascade system theory. Finally, the trajectory tracking performance of the proposed controllers for the NWMR system is illustrated through simulation results. The leader robot achieved a low RMSE of 1.6572 (Robot 1), indicating accurate trajectory tracking. Follower robots showed RMSEs of 2.6425 (Robot 2), 3.0132 (Robot 3), and 4.2132 (Robot 3), reflecting minor variations due to the distributed control strategy and local disturbances. Full article

In recent years, Academia and industry have conducted extensive and in-depth research on bearing-fault-diagnosis technology. However, the current modeling of time–space coupling characteristics in rolling bearing fault diagnosis remains inadequate, and the integration of multi-modal correlations requires further improvement. To address these challenges, this paper proposes a joint diagnosis framework integrating graph convolutional networks (GCNs) with attention-enhanced bidirectional gated recurrent units (BiGRUs). The proposed framework first constructs an improved K-nearest neighbor-based spatio-temporal graph to enhance multidimensional spatial–temporal feature modeling through GCN-based spatial feature extraction. Subsequently, we design an end-to-end spatio-temporal joint learning architecture by implementing a global attention-enhanced BiGRU temporal modeling module. This architecture achieves the deep fusion of spatio-temporal features through the graph-structural transformation of vibration signals and a feature cascading strategy, thereby improving overall model performance. The experiment demonstrated a classification accuracy of 97.08% on three public datasets including CWRU, verifying that this method decouples bearing signals through dynamic spatial topological modeling, effectively combines multi-scale spatiotemporal features for representation, and accurately captures the impact characteristics of bearing faults. Full article

To reduce carbon emissions from fossil fuel generators in sustainable energy systems, an option is increasing the integration of gas-fired generators into the power system. The increasing reliance on natural gas for electricity generation has strengthened the interdependence between the electric power network and the natural gas infrastructure within the Integrated Power and Gas System (IPGS). This strengthened interdependence increases the risk that disruptions originating in one system may propagate to the other, potentially leading to extensive cascading failures throughout the IPGS. Ensuring the reliability of critical energy infrastructure is vital for sustainable development. This paper proposes a vulnerability assessment method for the IPGS using an influence graph, which can be formulated based on fault chain theory to capture the interactions among failed components in the IPGS. With the influence graph, eigenvector centrality is used to pinpoint the critical components in the IPGS. The proposed methodology is validated using 39-bus 29-node IPGS through the Scenario Analysis Interface for Energy Systems (SAInt) software version 3.5.17.7. Results show that the proposed method has effectively identified the most critical branches in the IPGS, which play a key role in initiating cascading failures. These insights contribute to enhancing the resilience and sustainability of interconnected energy systems. Full article

Background: Electroencephalography (EEG) assists clinicians in diagnosing epileptic seizures by recording brain electrical activity. Existing models process spatiotemporal features inefficiently either through cascaded spatiotemporal architectures or static functional connectivity, limiting their ability to capture deeper spatial–temporal correlations. Objectives: To address these limitations, we propose a Graph Transformer with Spatiotemporal Attention Fusion Gate (GT-STAFG). Methods: We analyzed 18-channel EEG data sampled at 200 Hz, transformed into the frequency domain, and segmented into 30- second windows. The graph transformer exploits dynamic graph data, while STAFG leverages self-attention and gating mechanisms to capture complex interactions by augmenting graph features with both spatial and temporal information. The clinical significance of extracted features was validated using the Integrated Gradients attribution method, emphasizing the clinical relevance of the proposed model. Results: GT-STAFG achieves the highest area under the precision–recall curve (AUPRC) scores of 0.605 on the TUSZ dataset and 0.498 on the CHB-MIT dataset, surpassing baseline models and demonstrating strong cross-patient generalization on imbalanced datasets. We applied transfer learning to leverage knowledge from the TUSZ dataset when analyzing the CHB-MIT dataset, yielding an average improvement of 8.3 percentage points in AUPRC. Conclusions: Our approach has the potential to enhance patient outcomes and optimize healthcare utilization. Full article

Reliable fault diagnosis in railway operational equipment is critical to ensuring system safety, operational efficiency, and predictive maintenance. Existing methods struggle to capture the intricate interdependencies among fault causes, failure modes, and corrective actions, limiting their ability to model fault propagation effectively. To address this, we propose TH-RotatE, a novel knowledge graph (KG) embedding framework that integrates TransH’s hierarchical modeling with RotatE’s complex space transformations, while incorporating a hybrid scoring function and self-adversarial negative sampling to enhance embedding quality and fault relationship differentiation. This approach effectively captures hierarchical dependencies, cyclic patterns, and asymmetric transitions inherent in railway faults, enabling a more expressive representation of fault propagation. Furthermore, we construct the Chinese railway operational equipment fault knowledge graph (CROEFKG), a structured multi-relational repository encoding fault descriptions, causal chains, and mitigation strategies. Extensive experiments on real-world railway fault data demonstrate that TH-RotatE outperforms both traditional and advanced KG embedding models, achieving superior fault diagnosis accuracy and link prediction effectiveness. In practical applications, TH-RotatE enables timely fault diagnosis and detection of cascading failures, providing interpretable fault propagation pathways through the CROEFKG’s structured representation. These capabilities offer a scalable, knowledge-driven solution for railway systems, improving diagnostic accuracy while reducing safety risks and unplanned downtime. This work advances domain-specific KG embeddings, bridging the gap between theoretical innovation and industrial reliability. Full article

Background: D-glucose (dextrose) is used as a 5000–25,000 mg% solution in the injection-based pain therapy known as dextrose prolotherapy (DPT). The number of peer-reviewed clinical trials supporting its use is growing. However, the mechanism of action is unknown, limiting further research. A commonly expressed theory is that hyperosmotic dextrose injection induces inflammation, initiating a healing-specific inflammatory cascade. In vitro study models have used continuous exposure to high concentration dextrose. But the rate of dextrose clearance after intra-articular injection, and, therefore, the duration of exposure of tissues to any particular dextrose concentration, remains unknown. We therefore determined the rate of dextrose concentration diminution in one human participant’s knees after intra-articular dextrose knee injection. Method: In this pre–post N-of-1 study, the first author (KDR), a well 70-year-old male without knee-related pathology, injected his own knees with 30 mL of 12,500 mg% dextrose on three occasions; performed serial aspirations of 1.2 mL of intra-articular fluid from 7 to 360 min post-injection; and assessed synovial dextrose concentration. Dextrose clearance kinetics were determined using Minitab and GraphPad Prism software. Results: Dextrose concentration dropped rapidly in all three trials, approximating an exponential or steep S curve. A third order chemical reaction pattern was found, suggesting factors other than dilution or glucose transporter activity, such as rapid diffusion of dextrose across the synovial membrane, may have contributed to the rapid drop in dextrose concentration. Conclusion: This pre-post N-of-1 study shows that, after intraarticular injection of 30 mL of 12,500 mg% dextrose injection into a well knee, the concentration of dextrose diminished rapidly, suggesting that intra-articular cells, tissue, and anatomic structures are exposed to an initially high dextrose concentration for a very short time. This likely affects the mechanism of action of DPT and should inform in vitro study methods. Full article

Dysarthria is a speech disorder characterized by difficulties in articulation and vocalization due to impaired control of the articulatory system. Around 30% of individuals with speech disorders have dysarthria, facing significant communication challenges. Existing assistive tools for dysarthria either require additional manipulation or only provide word-level speech support, limiting their ability to support effective communication in real-world situations. Thus, this paper proposes a real-time communication aid system that converts sentence-level Korean dysarthric speech to non-dysarthric normal speech. The proposed system consists of two main parts in cascading form. Specifically, a Korean Automatic Speech Recognition (ASR) model is trained with dysarthric utterances using a conformer-based architecture and the graph transducer network–connectionist temporal classification algorithm, significantly enhancing recognition performance over previous models. Subsequently, a Korean Text-To-Speech (TTS) model based on Jointly Training FastSpeech2 and HiFi-GAN for end-to-end Text-to-Speech (JETS) is pipelined to synthesize high-quality non-dysarthric normal speech. These models are integrated into a single system on an app server, which receives 5–10 s of dysarthric speech and converts it to normal speech after 2–3 s. This can provide a practical communication aid for people with dysarthria. Full article

This paper investigates the effectiveness of centrality measures for the influence maximization problem in competitive social networks (SNs). We consider a framework, which we call “I-Game” (Influence Game), to conceptualize the adoption of competing products as a strategic game. Firms, as players, aim to maximize the adoption of their products, considering the possible rational choice of their competitors under a competitive diffusion model. They independently and simultaneously select their seeds (initial adopters) using an algorithm from a finite strategy space of algorithms. Since strategies may agree to select similar seeds, it is necessary to include an initial seed tie-breaking rule into the game model of the I-Game. We perform an empirical study in a two-player game under the competitive independent cascade model with three different seed-tie-breaking rules using four real-world SNs. The objective is to compare the performance of centrality-based strategies with some state-of-the-art algorithms used in the non-competitive influence maximization problem. The experimental results show that Nash equilibria vary according to the SN, seed-tie-breaking rules, and budgets. Moreover, they reveal that classical centrality measures outperform the most effective propagation-based algorithms in a competitive diffusion setting in three graphs. We attempt to explain these results by introducing a novel metric, the Early Influence Diffusion (EID) index, which measures the early influence diffusion of a strategy in a non-competitive setting. The EID index may be considered a valuable metric for predicting the effectiveness of a strategy in a competitive influence diffusion setting. Full article

This paper proposes an ancillary decision-making method for hydropower station failure handling based on knowledge graph and case-based reasoning. The proposed method assists the power station dispatcher to carry out accurate and timely failure handling after an accident. First, the main steps of case-based reasoning are introduced. The main difficulties and their corresponding solutions when applying case-based reasoning to hydropower station failure handling are discussed. Then, an ancillary decision-making method for hydropower station failure handling is proposed. Key steps such as case construction, case retrieval, and case revision are designed. In the proposed method, each case is represented in the form of multiple knowledge graphs, i.e., a system topology knowledge graph, a dispatching regulation knowledge graph, and an accident case knowledge graph. The flexibility of case knowledge extraction, management, and retrieval is greatly enhanced. Finally, the simulation analysis is carried out on a large-scale cascade hydropower station in China. The simulation results show that the proposed method can provide reasonable and reliable ancillary decision-making for the power station dispatcher in the failure handling process, and greatly improve the intelligence level of emergency management at a hydropower station. Full article

A one-dimensional numerical overland flow model based on the cascade plane theory was developed to estimate rainfall-induced runoff and soil erosion on converging and diverging plane surfaces. The model includes three components: (i) soil infiltration using Horton’s infiltration equation, (ii) overland flow using the kinematic wave approximation of the one-dimensional Saint-Venant shallow water equations for a cascade of planes, and (iii) soil erosion based on the sediment transport continuity equation. The model’s performance was evaluated by comparing numerical results with laboratory data from experiments using a rainfall simulator and a soil flume. Four independent experiments were conducted on converging and diverging surfaces under varying slope and rainfall conditions. Overall, the numerically simulated hydrographs and sediment graphs closely matched the laboratory results, showing the efficiency of the model for the tested controlled laboratory conditions. The model was then used to numerically explore the impact of different plane soil surface geometries on runoff and soil loss. Seven geometries were studied: one rectangular, three diverging, and three converging. A constant soil surface area, the rainfall intensity, and the slope gradient were maintained in all simulations. Results showed that increasing convergence angles led to a higher peak and total soil loss, while decreasing divergence angles reduced them. Full article

The purpose of entity linking is to connect entity mentions in text to real entities in the knowledge base. Existing methods focus on using the text topic, entity type, linking order, and association between entities to obtain the target entities. Although these methods have achieved good results, they ignore the exploration of candidate entities, leading to insufficient semantic information among entities. In addition, the implicit relationship and discrimination within the candidate entities also affect the accuracy of entity linking. To address these problems, we introduce information about candidate entities from Wikipedia and construct a graph model to capture implicit dependencies between different entity decisions. Specifically, we propose a cascade attention mechanism and develop a novel local entity linkage model termed CAM-LEL. This model leverages the interaction between entity mentions and candidate entities to enhance the semantic representation of entities. Furthermore, a global entity linkage model termed DG-GEL based on a dynamic graph is established to construct an entity association graph, and a random walking algorithm and entity entropy are used to extract the implicit relationships within entities to increase the differentiation between entities. Experimental results and in-depth analyses of multiple datasets show that our model outperforms other state-of-the-art models. Full article

In the storage of hazardous chemicals, due to space limitations, various hazardous chemicals are usually mixed stored when their chemical properties do not conflict. In a fire or other accidents during storage, the emergency response includes two key steps: first, using fire extinguishers like dry powder and carbon dioxide to extinguish the burning hazardous chemicals. In addition, hazardous chemicals around the accident site are often watered to cool down to prevent the spread of the fire. But both the water and extinguishers may react chemically with hazardous chemicals at the accident site, potentially triggering secondary accidents. However, the existing research about hazardous chemical domino accidents only focuses on the pre-rescue stage and ignores the simulation of rescue-induced accidents that occur after rescue. Aiming at the problem, a quantitative representation algorithm for the spatial correlation of hazardous chemicals is first proposed to enhance the understanding of their spatial relationships. Subsequently, a graph neural network is introduced to simulate the evolution process of hazardous chemical cascade accidents. By aggregating the physical and chemical characteristics, the initial accident information of nodes, and bi-temporal node status information, deep learning models have gained the ability to accurately predict node states, thereby improving the intelligent simulation of hazardous chemical accidents. The experimental results validated the effectiveness of the method. Full article

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have made considerable advances in hyperspectral image (HSI) classification. However, most CNN-based methods learn features at a single-scale in HSI data, which may be insufficient for multi-scale feature extraction in complex data scenes. To learn the relations among samples in non-grid data, GCNs are employed and combined with CNNs to process HSIs. Nevertheless, most methods based on CNN-GCN may overlook the integration of pixel-wise spectral signatures. In this paper, we propose a pyramid cascaded convolutional neural network with graph convolution (PCCGC) for hyperspectral image classification. It mainly comprises CNN-based and GCN-based subnetworks. Specifically, in the CNN-based subnetwork, a pyramid residual cascaded module and a pyramid convolution cascaded module are employed to extract multiscale spectral and spatial features separately, which can enhance the robustness of the proposed model. Furthermore, an adaptive feature-weighted fusion strategy is utilized to adaptively fuse multiscale spectral and spatial features. In the GCN-based subnetwork, a band selection network (BSNet) is used to learn the spectral signatures in the HSI using nonlinear inter-band dependencies. Then, the spectral-enhanced GCN module is utilized to extract and enhance the important features in the spectral matrix. Subsequently, a mutual-cooperative attention mechanism is constructed to align the spectral signatures between BSNet-based matrix with the spectral-enhanced GCN-based matrix for spectral signature integration. Abundant experiments performed on four widely used real HSI datasets show that our model achieves higher classification accuracy than the fourteen other comparative methods, which shows the superior classification performance of PCCGC over the state-of-the-art methods. Full article

Against the background of the construction of new power systems, power generation, transmission, distribution, and dispatching services are open to the outside world for interaction, and the accessibility of attack paths has been significantly enhanced. We are facing cyber-physical cross-domain attacks with the characteristics of strong targeting, high concealment, and cross-space threats. This paper proposes a quantitative analysis method for the influence of power cyber-physical cross-domain attack paths based on graph knowledge. First, a layered attack graph was constructed based on the cross-space and strong coupling characteristics of the power cyber-physical system business and the vertical architecture of network security protection focusing on border protection. The attack graph included cyber-physical cross-domain attacks, control master stations, measurement and control equipment failures, transient stable node disturbances, and other vertices, and achieved a comprehensive depiction of the attack path. Second, the out-degree, in-degree, vertex betweenness, etc., of each vertex in the attack graph were comprehensively considered to calculate the vertex vulnerability, and by defining the cyber-physical coupling degree and edge weights, the risk of each attack path was analyzed in detail. Finally, the IEEE RTS79 and RTS96 node systems were selected, and the impact of risk conduction on the cascading failures of the physical space system under typical attack paths was analyzed using examples, verifying the effectiveness of the proposed method. Full article

Entity–relationship extraction plays a pivotal role in the construction of domain knowledge graphs. For the aquatic disease domain, however, this relationship extraction is a formidable task because of overlapping relationships, data specialization, limited feature fusion, and imbalanced data samples, which significantly weaken the extraction’s performance. To tackle these challenges, this study leverages published books and aquatic disease websites as data sources to compile a text corpus, establish datasets, and then propose the Fd-CasBGRel model specifically tailored to the aquatic disease domain. The model uses the Casrel cascading binary tagging framework to address relationship overlap; utilizes task fine-tuning for better performance on aquatic disease data; trains on specialized aquatic disease corpora to improve adaptability; and integrates the BRC feature fusion module—which incorporates self-attention mechanisms, BiLSTM, relative position encoding, and conditional layer normalization—to leverage entity position and context for enhanced fusion. Further, it replaces the traditional cross-entropy loss function with the GHM loss function to mitigate category imbalance issues. The experimental results indicate that the F1 score of the Fd-CasBGRel on the aquatic disease dataset reached 84.71%, significantly outperforming several benchmark models. This model effectively addresses the challenges of ternary extraction’s low performance caused by high data specialization, insufficient feature integration, and data imbalances. The model achieved the highest F1 score of 86.52% on the overlapping relationship category dataset, demonstrating its robust capability in extracting overlapping data. Furthermore, We also conducted comparative experiments on the publicly available dataset WebNLG, and the model in this paper obtained the best performance metrics compared to the rest of the comparative models, indicating that the model has good generalization ability. Full article