Search Results (422)

In air–ground cooperative systems, identifying the identities of unmanned ground vehicles (UGVs) from an unmanned aerial vehicle (UAV) perspective is a critical step for downstream tasks. Traditional approaches involving attaching markers, like AprilTags on UGVs, fail under low-resolution or occlusion conditions, and the visually identical UGVs are hard to distinguish through similar visual features. This paper proposes a markerless method that associates UGV onboard sensor data with UAV visual detections to achieve identification. Our approach employs a Dempster–Shafer fused methodology integrating two proposed complementary association techniques: a projection-based method exploiting sequential motion patterns through reprojection error validation, and a topology-based method constructing distinctive topology using positional and orientation data. The association process is further integrated into a multi-object tracking framework to reduce ID switches during occlusions. Experiments demonstrate that under low-noise conditions, the projection-based method and the topology-based method achieves association precision at 89.5% and 87.6% respectively, which is superior to the previous methods. The fused approach enables robust association at 79.9% precision under high noise conditions, nearly 10% higher than original performance. Under false detection scenarios, our method achieves effective false-positive exclusion, and the integrated tracking process effectively mitigates occlusion-induced ID switches. Full article

The Dynamic Voltage Restorer (DVR), which is connected in series between the power grid and the load, can rapidly compensate for voltage disturbances to maintain stable voltage at the load end. To enhance the energy supply capacity of the DVR and utilize its shared circuit topology with photovoltaic (PV) inverters—which enables the dual functions of voltage compensation and PV-storage power generation—this study integrates PV and energy storage as a coordinated energy unit into the DVR, forming a PV-storage-integrated DVR system. The core innovation of this system lies in extending the voltage disturbance detection capability of the DVR to include harmonics. By incorporating a Butterworth filtering module and voltage fluctuation tracking technology, high-precision disturbance identification is achieved, thereby supporting power balance control and functional coordination. Furthermore, a multi-mode-power coordinated regulation method is proposed, enabling dynamic switching between operating modes based on PV output. Simulation and experimental results demonstrate that the proposed system and strategy enable smooth mode transitions. This approach not only ensures reliable voltage compensation for sensitive loads but also enhances the grid-support capability of PV systems, offering an innovative technical solution for the integration of renewable energy and power quality management. Full article

Glutaredoxins (GRXs) are important proteins in plant development and environmental adaptation. Despite extensive characterization of GRX gene family members in various plant species, limited research has been conducted on the identification and functional analysis of GRXs in the economically important Solanaceae family pepper (Capsicum annuum). This study identified 35 typical GRX genes in pepper and categorized them into three distinct groups: CC-, CGFS-, and CPYC-type, based on the phylogenetic topology, which was consistent with motif or domain arrangement, and gene structures. Furthermore, the determination of ω values indicated that purifying selection was a significant factor in the evolutionary diversification of GRX genes in the eudicot family. Intra-genome investigations demonstrated that both segmental and tandem duplications were involved in the expansion of CaGRX genes. Moreover, examination of collinearity within the Solanaceae family revealed 53 orthologous pairs of GRX genes. Additionally, prediction of cis-regulatory elements and analysis of expression profiles revealed the significant involvement of GRX genes in plant stress response, specifically in relation to hypoxia and submergence. Subsequent subcellular localization examination suggested CaGRX may be involved in the endomembrane system and regulation of oxidative balance in plants. Collectively, these findings enhance our comprehension of the structural and functional properties of GRX in pepper, and establish a groundwork for subsequent functional characterization of the CaGRX genes. Full article

Accurate transformer–customer relationships are crucial for the efficient operation and high-quality service of the low-voltage distribution grid (LVDG). This paper proposes a novel two-stage transformer–customer relationship identification strategy for LVDG using physics-guided graph attention network (PGAT). First, considering both transient and steady-state voltage fluctuations, a modified piecewise aggregate approximation (MPAA) algorithm is developed to preprocess raw measurement data through compression and denoising while preserving key voltage correlation features. Second, electrical similarity among customers is explored using the Modified Piecewise Aggregate Approximation K-means (MPAA-K-means) algorithm, enabling preliminary identification of transformer–customer relationships. Then, a training paradigm based on PGAT is introduced to characterize node features constrained by grid topology and electrical properties, achieving refined identification of transformer–customer relationships. Finally, testing results on real LVDG demonstrate the effectiveness and accuracy of the proposed two-stage identification strategy, providing new insights for transformer–customer relationship identification. Full article

This paper proposes a harmonic source localization method for power systems, combining voltage difference features with a random forest classifier. The method captures harmonic propagation patterns and optimizes network topology handling to ensure accurate and efficient identification across various configurations. Validated on IEEE standard transmission networks, it achieves high accuracy and scalability. While effective in transmission systems, distribution networks pose challenges due to complex topologies and high impedance. Future enhancements will focus on advanced feature engineering, data augmentation, and real-time processing to improve adaptability in diverse power system environments. Full article

The identification of influential nodes in complex networks modeling manufacturing environments is a critical aspect, especially when considering both structure and nodal attributes. This becomes particularly relevant given that conventional weighted centrality measures typically only consider edge weights while ignoring node heterogeneity. We present SL-WLEN (Semi-Local centrality with Weighted Lexicographic Extended Neighborhood), a novel centrality metric designed to overcome these limitations. Based on LRASP (Local Relative Average Shortest Path) and lexicographic ordering, SL-WLEN integrates topological structure and nodal attributes by combining local components (degree and nodal values). The incorporation of lexicographic ordering preserves the relative importance of nodes at each neighborhood level, ensuring that those with high values maintain their influence in the final metric without distortions from statistical aggregations. This method is applied and its robustness evaluated in a quality control network for chip manufacturing, comprising 1555 nodes representing critical process characteristics, with weighted connections indicating their degree of correlation. Finally, the metric was evaluated against other established methods using the SIR propagation model and Kendall’s τ coefficient, demonstrating that SL-WLEN maintains consistent values across all analyzed test networks. Full article

The reconstruction of historic buildings requires the integration of diverse data sources, both geometric and non-geometric. This study presents a multi-source data analysis methodology for heritage reconstruction using 3D modeling and Historic Building Information Modeling (HBIM). The proposed approach combines geometric data, including point clouds acquired via Terrestrial Laser Scanning (TLS), with architectural documentation and non-geometric information such as photographs, historical records, and technical descriptions. The case study focuses on a wooden Orthodox church in Żmijowiska, Poland, analyzing geometric changes in the structure over multiple decades. The reconstruction process integrates modern surveys with archival sources and, in the absence of complete geometric data, utilizes semantic, topological, and structural information. Geometric datasets from the 1990s, 1930s, and the turn of the 20th century were analyzed, supplemented by intermediate archival photographs and technical documentation. This integrated method enabled the identification of transformation phases and verification of discrepancies between historical records and the building’s actual condition. The findings confirm that the use of HBIM and multi-source data fusion facilitates accurate reconstruction of historical geometry and supports visualization of spatial changes across decades. Full article

High-resistance faults on the DC lines of multi-terminal VSC-HVDC grids lead to insufficient protection reliability, and the introduction of current-limiting strategies alters the system’s intrinsic fault characteristics, degrading protection performance. To address these issues, we propose a DC-line protection scheme that is immune to converter control strategies and highly tolerant to fault resistance. First, based on the grid topology, post-fault current paths are analyzed, and the fault characteristics produced solely by the fault-induced voltage source are identified. A sequential overlapping derivative transformation is then employed to magnify the discrepancy between internal and external faults, forming the core of the fault-identification criterion; the zero-mode component is used for pole selection. Finally, a four-terminal VSC-HVDC model is built in PSCAD/EMTDC version 4.6.2 for validation. Simulation results show that, after applying the current-limiting strategy, the characteristic quantity changes only marginally, and the proposed protection can reliably withstand fault resistances of up to 700 Ω. Full article

Disc-shaped Autonomous Underwater Helicopters (AUHs) offer superior maneuverability but suffer from high hydrodynamic drag, which limits their operational endurance. To address this challenge, this study proposes a robust optimization framework for a novel sea-turtle-inspired AUH. A parametric hull, governed by two dimensionless shape factors based on modified Myring equations, was established to facilitate systematic exploration. To reduce the high computational cost of direct CFD evaluations, a high-precision Gaussian Process Regression (GPR) surrogate model was constructed from a small dataset of 24 samples. The core methodological innovation is T-NSGA-II, an algorithm featuring hyperparameters that are systematically optimized by the Optuna framework. In comparative evaluations, the T-NSGA-II-generated Pareto front demonstrated clear superiority over the standard NSGA-II, identifying designs with significantly lower drag for an equivalent vertical force. A key scientific contribution of this research is the identification of a distinct performance gap on the Pareto front. This phenomenon is interpreted not as an algorithmic artifact but as a ‘natural gap’, reflecting a deep physical trade-off, with potential underlying causes including a critical transition in flow physics or a topological shift in the optimal hull geometries. This work not only delivers a suite of optimized, practical AUH designs but also presents a powerful, intelligent optimization methodology that is capable of revealing fundamental physical trade-offs in complex engineering problems. Full article

To offer a viewpoint on convexity and connectedness inside intuitionistic fuzzy graphs (IFGs), the paper is devoted to the study of intuitionistic fuzzy geodetic convexity. The paper introduces an algorithm for precise identification and characterization of geodetic pathways in IFGs, supported by a Python program. Various properties of IF-geodetic convex sets such as IF-internal and IF-boundary vertices are obtained. Furthermore, this work introduces and characterizes the concepts of geodetic IF-cover, geodetic IF-basis, and geodetic IF-number. Additionally, the study develops the IF-geodetic Wiener index. The scope of the work explores the application of IF-geodetic cover in wireless mesh networks, focusing on the identification of gateway nodes, where symmetry in connectivity patterns enhances network efficiency. A practical implementation of the IF-geodetic Wiener index method in global human trading analysis underscores the real-world implications of the developed concepts, where the efficiency and interpretability of fuzzy geodetic measures are improved by symmetry in network topologies and trade patterns. Full article

Path flow always plays a critical role in extracting vehicle travel patterns and reflecting network-scale traffic features. However, the comprehensive topological structure of urban road networks induces massive route choices, so frequent travel routes have been gradually regarded as an ideal countermeasure to represent traffic states. Widely used license plate recognition (LPR) devices can collect the abundant traffic features of all vehicles, but their sparse spatial distributions restrict the conventional models in frequent travel identification. Therefore, this study develops a network reconstruction method to construct a topological network from the LPR dataset, avoiding the adverse effects caused by the sparse distribution of detectors on the road network and further uses the Snake algorithm to fully utilize the road network structure and traffic attributes for clustering to obtain various travel patterns, with frequent routes under different travel patterns finally identified based on Steiner trees and frequent item recognition. To address the sparse spatial distribution of LPR devices, we utilize the word2vec model to extract spatial correlations among intersections. A threshold-based method is then applied to transform the correlation matrix into a reconstructed network, connecting intersections with strong vehicle transition relationships. This community structure can be interpreted as representing different travel patterns. Consequently, the Snake algorithm is employed to cluster intersections into distinct categories, reflecting these varied travel patterns. By leveraging the word2vec model, the detector installation rate requirement for Snake is significantly reduced, ensuring that the clustering results accurately represent the intrinsic relevance of traffic roads. Subsequently, frequent routes are identified from both macro- and micro-perspectives using the Steiner tree and Frequent Pattern Growth (FP Growth) algorithm, respectively. Validated on the LPR dataset in Changsha, China, the experiment results demonstrate that the proposed method can effectively identify travel patterns and extract frequent routes in the sparsely installed LPR devices. Full article

The complex topology of shipboard DC microgrids and the strong coupling between positive and negative poles during faults pose significant challenges for faulted-pole identification, especially under high-resistance conditions. To address these issues, this paper proposes a novel faulted-pole identification method based on S-Transformation and convolutional neural networks (CNNs). Single-ended voltage and current measurements from the generator side are used to generate time–frequency spectrograms via S-Transformation, which are then processed by a CNN trained to classify the faulted pole. This approach avoids reliance on complex threshold settings. Simulation results on a representative shipboard DC microgrid demonstrate that the proposed method achieves high accuracy, fast response, and strong robustness, even under high-resistance fault scenarios. The method significantly enhances the selectivity and reliability of fault protection, offering a promising solution for advanced marine DC power systems. Compared to conventional fault-diagnosis techniques, the proposed model achieves notable improvements in classification accuracy and computational efficiency for line-fault detection. Full article

Visible-Infrared Person Re-Identification (VI-ReID) is of crucial importance in applications such as monitoring and security. However, challenges faced from intra-class variations and cross-modal differences are often exacerbated by inaccurate infrared analysis and insufficient structural modeling. To address these issues, we propose Parsing-guided Differential Enhancement Graph Learning (PDEGL), a novel framework that learns discriminative representations through a dual-branch architecture synergizing global feature refinement with part-based structural graph analysis. In particular, we introduce a Differential Infrared Part Enhancement (DIPE) module to correct infrared parsing errors and a Parsing Structural Graph (PSG) module to model high-order topological relationships between body parts for structural consistency matching. Furthermore, we design a Position-sensitive Spatial-Channel Attention (PSCA) module to enhance global feature discriminability. Extensive evaluations on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that our PDEGL method achieves competitive performance. Full article

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. Full article

In recent years, the concept of the myofascial network has transformed biomechanical understanding by emphasizing the body as an integrated, multidirectional system. This study advances that paradigm by applying graph theory to model the osteo-myofascial system as an anatomical network, enabling the identification of topologically central nodes involved in force transmission, stability, and coordination. Using the aNETomy model and the BIOMECH 3.4 database, we constructed an undirected network of 2208 anatomical nodes and 7377 biomechanical relationships. Centrality analysis (degree, betweenness, and closeness) revealed that structures such as the sacrum and thoracolumbar fascia exhibit high connectivity and strategic importance within the network. These findings, while derived from a theoretical modeling approach, suggest that such key nodes may inform targeted treatment strategies, particularly in complex or compensatory musculoskeletal conditions. The proposed concept of a polyconnective skeleton (PCS) synthesizes the most influential anatomical hubs into a functional core of the system. This framework may support future clinical and technological applications, including integration with imaging modalities, real-time monitoring, and predictive modeling for personalized and preventive medicine. Full article