Search Results (3,212)

Nowadays, numerical simulation methods are advanced and widely used in industry, enabling the modeling of complex systems from printed circuit boards (PCBs) to full power converters. Among many isolated topologies, the phase-shift full-bridge (PSFB) topology is a well-established solution for isolated DC–DC conversion in electric vehicles. Therefore, this paper proposes a broadband electromagnetic compatibility (EMC) modeling methodology for a custom-designed 1 kW gallium nitride (GaN)-based PSFB converter intended for an electric vehicle (EV) DC powertrain. Moreover, the approach combines full-wave electromagnetic simulation with circuit-level simulation, including parasitic effects from PCB layout, power harnesses, and discrete components. Thus, the virtual prototype is assessed within a complete virtual test bench compliant with the standard Comité International Spécial des Perturbations Radioélectriques (CISPR) 25 over the 150 kHz–108 MHz range to capture common-mode (CM) and differential-mode (DM) conducted electromagnetic interference (EMI). Results show that the converter achieves efficiencies of 97.26% in standalone mode and 97.03% when integrated into the full DC powertrain. However, the conducted EMI assessment reveals that both CM and DM emissions exceed CISPR 25 Class 2 limits across the entire spectrum, with excess levels reaching up to 72 dBµV. Therefore, power harnesses significantly increase EMI levels at low frequencies due to the distributed inductance and stray capacitance. Finally, this study demonstrates the value of virtual prototyping for simulation-based EMI prediction in early-stage power converter design. Full article

Exploring the interaction relationship among multiple ecosystem services is vital for maintaining ecosystem function. However, traditional approaches are limited in their ability to: (i) characterize complex interactions and (ii) visualize the spatial connectivity of various ecosystem services delivered by social–ecological systems. To address these challenges, a framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by: (i) estimating the spatial distribution of multiple ecosystem services using the InVEST model, and (ii) generating network nodes and edges with geographical attributes based on the minimum cumulative resistance model and a multiresolution segmentation method. We conducted a case study in the Guangdong–Hong Kong–Macao Greater Bay Area and examined the topological features of the spatial networks using complex network indicators. For each network, winding and multiple edges connected adjacent nodes and formed continuous linkages across the entire study area, indicating that the proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. The different ecosystem service networks exhibited conspicuous spatial heterogeneity and generally maintained relatively high connectivity, as evidenced by their tree-like structure with winding pathways and the distribution of multi-edge nodes, indicating that each ES was predominantly connected with multiple other ecosystem services. Meanwhile, nodes with high values of degree centrality and clustering coefficient were mainly concentrated in coastal and mountainous regions. This study advances the representation of complex interactions among multiple ecosystem services from a spatial perspective, thereby facilitating a deeper understanding of the interaction mechanisms underlying ecosystem functioning. Full article

Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance—Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud–edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier “Teacher–Student” paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity “Teacher,” employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV’s edge digital twin. This resource-constrained edge tier relies on a localized “Student” network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. Full article

Complex networks have become a fundamental paradigm for modeling real-world systems. Synchronization of such networks, particularly under hyperchaotic dynamics, presents a significant control challenge due to the high-dimensional state space and multiple positive Lyapunov exponents. This paper addresses the driver node selection problem in a 4D Hyperchaotic Lorenz complex network, formulating it as a constrained binary optimization task. We evaluate a pool of advanced metaheuristics, including the quantum genetic algorithm (QGA), seahorse optimizer (SHO), and artificial bee colony (ABC), across multiple network experiments conducted over 30 independent runs to guarantee statistical validity. The performance of these solvers is rigorously benchmarked against traditional topological heuristics, a random selection baseline comprising 600 feasible configurations, and verified through Wilcoxon statistical testing. Furthermore, addressing computational sustainability, we introduce a “Green-Artificial Intelligence” architecture based on dual-tier structured query language memoization (SQL-memoization) and provide a detailed runtime comparison evaluating its efficiency. The empirical results indicate that swarm-intelligence methods such as ABC and SHO exhibit robust competitive performance in minimizing synchronization errors while the Green-AI framework consistently and drastically reduces the computation of the repetitive simulations. Full article

This paper addresses the problem that high pixel-level segmentation accuracy does not necessarily lead to geometrically compact building boundaries in vectorized outputs. A multi-task directional field learning framework is proposed based on U-Net with a ResNet-50 encoder. The framework introduces directional field supervision and a mask-field alignment loss to jointly optimize building region prediction and local boundary orientation consistency. In addition, a mild topological simplification procedure with a fixed small tolerance is applied to reduce residual staircase-like artifacts during vectorization. Experiments on the WHU building dataset at 0.2 m and 0.3 m spatial resolutions show that the proposed framework produces compact vector representations while maintaining high overlap relative to the raster reference annotations. In the 0.2 m setting, directional field learning improves Boundary IoU compared with the Baseline U-Net, whereas the complete pipeline slightly reduces Mask IoU and F1-score due to the additional simplification step. In the 0.3 m setting, the complete method does not consistently outperform several baselines in conventional pixel-level metrics, but it shows a favorable trade-off between polygon compactness and vector overlap under raster-reference evaluation. These results indicate that the proposed method is more suitable for geometry-aware vector reconstruction and vector simplification than for maximizing general semantic segmentation accuracy. In particular, the average number of polygon vertices is substantially reduced while Vector IoU remains approximately 90–92%. To further address the limitation of evaluating only on the WHU dataset, an additional in-domain validation experiment was conducted on the JAX dataset, which contains more complex building appearances and scene variations. The results show that the proposed Directional Field + Mild DP pipeline consistently reduces polygon complexity on the JAX dataset while maintaining competitive vector overlap. The central objective of the proposed framework is not only to improve mask-level building extraction, but also to enhance boundary-oriented vector reconstruction by learning local boundary-direction consistency and reducing raster-induced polygonal redundancy. Full article

Failures in critical systems and equipment within nuclear power plants (NPPs) significantly threaten operational safety and reliability. Therefore, rapid and accurate root cause localization during the incipient stages of failure is critical to preventing escalation. Traditional modeling methods often fail to address the inherent structural complexity of NPPs, the diversity of failure modes, and the stochastic mapping relationships between symptoms and causes. To address these challenges, this paper proposes an intelligent fault diagnosis framework integrating knowledge graphs (KGs) and Bayesian networks (BNs). First, by analyzing failure modes and anomaly characteristics, we define discrimination criteria for typical faults. Second, a structured knowledge modeling approach is developed to transform unstructured fault information into a KG, which is subsequently mapped to a BN topology. Finally, to mitigate the subjectivity of expert priors, data-driven structure and parameter learning algorithms are employed to optimize the model, enhancing inference accuracy. Robustness was validated through experiments targeting three fault severity levels, using signed directed graphs (SDGs), support vector machines (SVMs), domain generalization softmax (DG-softmax) and long short-term memory (LSTM) as benchmarks. Experimental results demonstrate that the proposed method maintains high diagnostic precision across varying severities, outperforming traditional data-driven methods in accuracy and stability. This study enhances the interpretability and engineering applicability of intelligent diagnosis in nuclear power systems. Full article

This paper proposes a source-level electromagnetic interference suppression strategy for high-voltage inverters that uses a series-connected IGBT topology and discrete staircase voltage shaping. From an information-theoretic perspective, the staircase shaping transforms chaotic wideband switching noise into a deterministic harmonic structure, thereby reducing the spectral entropy of the EMI source. This information optimization is achieved using a CPLD-based sequential gate drive circuit, which eliminates the need for complex active gate profiling algorithms. Experimental results obtained using a 1140 V explosion-proof motor drive platform demonstrate harmonic attenuation of 4–16 dB μV within a 2 MHz band. Importantly, this targeted entropy reduction occurs alongside a 68.7% reduction in active-region switching losses, suggesting a concurrent decrease in local thermodynamic entropy production during switching transients. Increasing spectral determinism and relaxing requirements for subsequent physical filters effectively lower the conditional entropy of the overall electromagnetic environment. Leveraging the structural flexibility of series IGBTs, this method provides a practical, low-complexity solution and establishes a novel framework between power electronics and information theory for electromagnetic compatibility. Full article

In the current Industrial Internet of Things (IIoT) environment, data security for product lifecycle management is greatly challenged, particularly in scenarios involving vertical multi-level Bill of Materials (BOM) deep nesting and lifecycle dynamic evolution. The traditional case-bounding model, in large-scale deployment, easily leads to rule expansion and an increase in database I/O overhead, thus causing authorization lag, authority boundary ambiguity and other problems. To address these limitations, this paper proposes a Decoupled Hybrid Access Resolution (DHAR) framework. The framework separates static organizational roles from dynamic lifecycle constraints, and the complexity of authorization configuration is reconstructed from case-dependent growth into an object-instance-independent bounded structure; combined with the state-based pre-filtering mechanism and memory cache strategy, redundant recursive query is reduced. Experiments on increasing BOM depths show that, under a 20-layer topology, DHAR reduces average access latency from 285.8 ms to 1.3 ms. Under a 20-layer BOM with 1000 concurrent requests, DHAR maintains an average latency of 5.2 ms, while compressing the authorization rule set from millions to hundreds. These results indicate that, within the studied vertical multi-level BOM setting, DHAR improves response performance while preserving data consistency and strengthening protection against unauthorized modification. Full article

Vision-and-Language Navigation in continuous environments (VLN-CE) requires embodied agents to ground natural language instructions into reliable long-horizon motion decisions under partial observability. Despite their strong semantic understanding and reasoning abilities, Multimodal Large Language Model (LVLM) struggle when directly applied to VLN, as they lack explicit spatial grounding, embodied memory, and awareness of geometric and reachability constraints, leading to perceptual misalignment and cascading decision errors in complex scenes. To address these limitations, we propose STAMP, a Spatial-Temporal Anchored Motion Planning framework for zero-shot VLN-CE, which systematically bridges the gap between pretrained world knowledge and embodied navigation. STAMP adopts a hierarchical design that decouples high-level semantic reasoning from low-level motion execution, enabling a frozen LVLM to operate over a structured, navigation-oriented abstraction. Its core novelty lies in a multimodal spatial-temporal anchoring mechanism that explicitly encodes instruction-relevant landmarks, action semantics, depth-aware geometry, and historical navigation context, together with an explicit Chain-of-Navigation reasoning process that constrains decision-making to navigation-critical cues. Furthermore, STAMP incrementally constructs an online, backtracking-enabled topological map, supporting robust planning under uncertainty. Extensive experiments demonstrate the effectiveness of the proposed STAMP framework, achieving performance comparable to state-of-the-art zero-shot methods on VLN-CE benchmarks and in real-world settings. Full article

Circular RNAs (circRNAs) are key regulators in the onset and progression of complex diseases, offering promise as diagnostic and prognostic biomarkers. However, most putative circRNA–disease associations remain experimentally unverified, largely due to the cost and time demands of wet-lab approaches. To bridge this gap, we present STRCDA (Sparse Topological Representation learning for CircRNA–Disease Associations). The pipeline first constructs fused similarity profiles for circRNAs and diseases by integrating diverse biological attributes. These initial matrices are then refined via random walk with restart to capture local features. Subsequently, a sparse-constrained dual-branch graph autoencoder extracts holistic topological embeddings from the refined local features and the known interaction network. Finally, an XGBoost classifier scores potential circRNA–disease pairs. On the CircR2Disease dataset, STRCDA achieves an AUC of 0.9771 and an AUPR of 0.9826 under five-fold cross-validation. Notably, 18 of the top 20 predicted associations were confirmed by independent experimental evidence, highlighting STRCDA’s efficacy as a robust tool for uncovering circRNA function in disease. Full article

Multi-source heterogeneous data in substation secondary systems are typically characterized by high entropy and disorder, which pose significant challenges for cross-modal information integration and efficient retrieval. Therefore, a method for multimodal information extraction and knowledge graph construction is proposed, enabling structured processing of heterogeneous data from multiple sources. For the image modality, positional and semantic information is extracted using YOLOv8n and Optical Character Recognition (OCR) techniques. To mitigate the effects of uncertain connection topology and noise interference, a Heuristic Circular Stepping Search Algorithm (HCSA) is designed to achieve deterministic path tracing of information flows. For the text modality, a RoFormer-BiLSTM-CRF model enhanced with Rotary Position Embedding (RoPE) is developed to alleviate information degradation in long-sequence texts, thereby enabling high-accuracy extraction of entities and relationships. Furthermore, by combining the domain ontology mapping rules and string similarity, the extracted device entities from the two modalities are aligned, thereby converting scattered data into a structured knowledge graph. Experiments conducted on the secondary-side data of a substation in China demonstrate that the proposed method effectively extracts multimodal information from substation secondary systems, providing valuable support for information management and decision-making assistance in complex industrial systems. Full article

This paper introduces a novel fault diagnosis framework for wiring networks that integrates Time Domain Reflectometry (TDR) with Gramian Angular Field (GAF) representations and a deep residual neural network. The proposed methodology transforms TDR responses into GAF images, which are directly exploited by the residual neural network to enable robust feature extraction from complex reflectometry signals. To support supervised learning, a forward modeling strategy is employed to generate representative TDR responses under a wide range of fault scenarios. Theframeworkis designed to provide real-time fault detection, localization, and characterization, demonstrating high effectiveness on complex topologies such as the YY-shaped network. Numerical results demonstrate high diagnostic performance for hard faults, achieving an overall accuracy and macro-averaged sensitivity exceeding 99%, thereby highlighting the effectiveness and reliability of the proposed approach. Full article

In ultra-cold narrow-window drilling, pipe connection causes flow-path switching as the main circulation is interrupted and bypass circulation is established, breaking the initial relative pressure balance of the whole wellbore and inducing asymmetric transient variations in flow distribution, annular friction, and bottomhole pressure response, thereby increasing the risks of wellbore instability, lost circulation, and kicks. To address the poor pressure-control accuracy, long non-productive time, and inadequate low-temperature adaptability of conventional drilling technologies in the Irkutsk block of Russia, this study developed and field-tested an integrated all-electric managed pressure drilling (MPD) and cold-resistant continuous circulation system (CCS). Existing conventional technologies often suffer from high communication latency and hydraulic freezing in extreme cold environments, leading to uncoordinated pressure compensation. To overcome these limitations, the scientific novelty of this work lies in proposing a transient pressure rebalancing mechanism that effectively suppresses the asymmetric pressure disturbances induced by topological flow path switching. Methodologically, the proposed system was validated through a comprehensive industrial field test. An improved Herschel–Bulkley temperature–pressure coupled model was established to dynamically calculate full wellbore annular pressure loss. Furthermore, a dedicated hardware adapter module utilizing multi-protocol conversion was integrated to achieve a communication delay of less than 8 ms, enabling high frequency coordinated pressure regulation. Field results demonstrate that compared to the delayed responses of conventional systems, the proposed integrated approach successfully maintained a dynamic backpressure tracking error within ±0.069 MPa under extreme conditions of −38 °C and a narrow pressure window of 0.08 g/cm³. The rapid suppression of asymmetric transient responses prevented any lost circulation, kicks, or wellbore collapse. These findings highlight the significant advantages of the integrated system in maintaining pressure field stability, thereby providing a robust and innovative engineering solution for complex well interventions. Full article

Movie genre classification is a significant challenge in narrative analysis, as traditional methods often fail to capture complex structural relationships within movie stories. This study introduces the Intra-Cluster Weighted Movie Network (ICWMN), a novel framework designed to improve classification by using intra-movie relationships through Graph Neural Networks (GNNs). We constructed a large-scale dataset of 1631 movie character networks using an automated pipeline comprising web scraping, regular expressions, and fine-tuned BERT models for entity recognition. To address the computational limitations of fully connected models, we partition ICWMN into clusters and establish edges only between the k-most similar nodes using the K-Nearest Neighbor algorithm and various distance measures, such as the Laplacian and NetLSD. XGBoost is applied to optimize high-dimensional node feature vectors. Experimental results demonstrate outstanding performance, with the Graph Attention Network (GAT) emerging as the top-performing architecture, resulting in classification accuracies that peak at $95.00\%$ on our 1631-movie dataset and an exceptional $97.30\%$ on the 773-movie Moviegalaxies dataset. These findings confirm that prioritizing spectral properties and cluster-based network topologies significantly improve the precision and stability of genre classification compared to state-of-the-art methods. Full article

Cascading failures in complex networks occur when local node or edge failures propagate to trigger large-scale collapse. Traditional pairwise network models cannot adequately capture group coordination and multi-agent higher-order interactions. Higher-order networks incorporating simplicial structures more accurately represent group and multi-node interactions, providing a new framework to study cascading failures and network robustness. The paper proposes a higher-order coupled map lattice (CML) model to characterize cascading failures in simplicial complex networks and analyze the influence of higher-order structures on network robustness. Further experiments on fourth-order simplicial networks investigate robustness differences under various topologies and attack strategies. Results indicate that fourth-order simplicial networks are vulnerable to targeted attacks but robust against random failures, regardless of network type. Furthermore in single-order networks, the higher simplex dimensions, the greater robustness. The theoretical perturbation thresholds for third-order networks show a negative correlation between the critical perturbation and the sum of network coupling parameters. These results are validated by analysis of simplices added to ordinary networks, destructive experiments, and empirical networks. This study deepens the understanding of cascading failure mechanisms and robustness in higher-order networks, and provides theoretical guidance for designing resilient networks based on higher-order structures. Full article