Search Results (74)

This paper proposes a cooperative localization method based on time-division processing of interferometric measurements, in which the receiver updates the signals from multiple UAVs in separate time slots, thereby reducing spectrum usage and baseband hardware overhead. A Kalman-enhanced tracking loop is designed to achieve high-precision carrier-phase and Doppler estimation under low-SNR conditions. For angle estimation, a time-division update strategy is employed such that the receiver performs full carrier tracking for only one UAV in each time slot, while the carrier phases of the remaining UAVs are extrapolated from the Doppler states estimated in the previous epoch. This avoids the hardware complexity associated with maintaining multiple parallel tracking loops. By fusing the estimated azimuth, elevation, and pseudorange measurements with the master UAV’s high-precision GNSS observations, a factor-graph-based sliding-window cooperative localization algorithm is constructed. Simulation results show that the proposed method improves the RMSE of carrier-phase and Doppler estimation by nearly an order of magnitude compared with the traditional FLL-assisted PLL. The system maintains angle estimation accuracy better than 0.01° within a four-node configuration and achieves centimeter-level ranging accuracy when SNR ≥ 0 dB. In a cooperative flight scenario with one master and three follower UAVs, the method consistently delivers sub-decimeter 3D localization accuracy. Full article

Aiming at the challenges of complex spatial-temporal correlation and strong nonlinearity in the power prediction of large-scale wind farm clusters, this study proposes a short-term wind power prediction method that combines a dynamic graph structure and a Kolmogorov–Arnold Network (KAN) enhanced neural network. Firstly, a spectral embedding fuzzy C-means (FCM) cluster partition method combining geographic location and numerical weather prediction (NWP) is proposed to solve the problem of insufficient spatio-temporal representation ability of traditional methods. Secondly, a dynamic directed graph construction mechanism based on a stacked wind direction matrix and wind speed mutual information is designed to describe the directional correlation between stations with the evolution of meteorological conditions. Finally, a prediction model of dynamic graph convolution and Transformer based on KAN enhancement (DGTK-Net) is constructed to improve the fitting ability of complex nonlinear relationships. Based on the cluster data of 31 wind farms in Gansu Province of China and the cluster data of 70 wind farms in Inner Mongolia, a case study is carried out. The results show that the proposed model is significantly better than the comparison methods in terms of key evaluation indicators, and the root mean square error is reduced by about 1.16% on average. This method provides a prediction tool that can adapt to time and space changes for engineering practice, which is helpful to improve the wind power consumption capacity and operation economy of the power grid. Full article

For a graph G of a degree greater than or equal to 3, counting the number of independent sets (denoted as $i\left(G\right)$) is a classical #P-complete problem. Here, we establish a new worst-case upper bound time complexity for computing $i\left(G\right)$ for any non-constraint undirected graph. Our proposal applies the vertex division rule $i\left(G\right)=i(G-\{x\left\}\right)+i(G-N[x\left]\right)$ over a vertex x which satisfies some conditions, and considers cactus and outerplanar graphs as basic subgraphs.Our algorithm establishes a leading worst-case upper bound of $O^{*}(1.{2321}^n)$, where n is the number of vertices in the graph and $O^{*}$ omits polynomial terms in n. Full article

To obtain accurate location information in dynamic environments, we propose a dynamic visual–inertial SLAM algorithm that can operate in real-time. In this paper, we combine the YOLO-V5 algorithm and the depth threshold extraction algorithm to achieve real-time pixel-level segmentation of objects. Meanwhile, to address the situation where dynamic targets are occluded by other objects, we design the object depth extraction method based on K-means clustering. We also design a factor graph optimization with rigid and non-rigid dynamic objects based on object category division, in order to better utilize the motion information of dynamic objects. We use the Kalman filter algorithm to achieve object matching and tracking. At the same time, to obtain as many rigid targets as possible, we design the adaptive rigid point set modeling algorithm to further supplement the rigid objects. Finally, we evaluate the algorithm through public datasets and self-built datasets, verifying its ability to handle dynamic environments. Full article

Driven by comparative returns, non-grain use of cultivated land (NGUCL) has intensified, posing risks to food security. This study approaches the problem by employing a risk transfer valuation framework, integrating a multi-regional input–output model with a synthetic risk index to establish China’s virtual cultivated land risk transfer network. Complex network analysis was utilized to explore its features while a temporal exponential random graph model was used to identify driving factors of its formation. Results indicate that fewer provinces took on additional pressures and risks. Despite differing motifs, transfer patterns showed little variation. Block analysis showed increasing net recipient relationships (from four to nine) and variable block divisions. Economic development and industrial structure are negatively associated with outgoing transfers, whereas population, production capacity and resource endowment are positively associated with them. The network exhibits time-dependent stability, with few new risk transfer paths forming. This study provides a theoretical basis and new ideas for optimizing land resource efficiency, re-shaping risk transfer patterns and maintaining food security. Full article

Urban parks provide critical benefits for public health, mental well-being, and social connection. However, inequities in park access and use persist, particularly among socially and economically vulnerable populations. While previous studies have established that segregation and social vulnerability each contribute to uneven park access, little is known about how these two forces interact to shape real visitation patterns. This study addresses this research gap and answers the research question: How does highway segregation relate to differences in the different aspects of social vulnerability in influencing park access across Austin’s east–west divide? SafeGraph mobility data from 2019 and the Social Vulnerability Index (SVI), which included four themes (i.e., socioeconomic status, household composition, minority status and language, and housing and transportation characteristics), were analyzed through fixed-effects regression models for Austin, Texas. Results show that household composition and minority vulnerabilities have negative associations with park visitation, indicating that areas with more elderly, single-parent, or minority residents visit parks less frequently. Interaction terms reveal that highway segregation functions as a structural barrier that conditions the influence of social vulnerability on park use. Those associated with socioeconomic resources diminish, while the disadvantages linked to household composition and minority status intensify on the east side of I-35, reflecting the cumulative effects of segregation and infrastructural division. These findings confirm that inequities in park access are more pronounced on the east side of the I-35, consistent with the highway’s role in reinforcing segregation. Efforts to strengthen connectivity represent key strategies for advancing equitable park visitation across Austin. Full article

A graph G is said to be perfect if $\omega \left(H\right)=\chi \left(H\right)$ for every induced subgraph H of G, where $\omega \left(H\right)$ and $\chi \left(H\right)$ denote the clique number and the chromatic number of H. We say that a graph G admits a perfect division if its vertex set can be partitioned into two subsets A and B such that $G\left[A\right]$ is perfect and $\omega \left(G\right[B\left]\right)<\omega \left(G\right)$. If every induced subgraph of G admits a perfect division, then G is called perfectly divisible. A graph $P_3\cup P_4$ is the disjoint union of paths $P_3$ and $P_4$. A bull refers to the graph consisting of a triangle with two disjoint pendant edges. A homogeneous set X is a proper subset of $V\left(G\right)$ with at least two vertices such that every vertex in $V\left(G\right)\setminus X$ is either complete or anticomplete to X. In this paper, we prove that every ($P_3\cup P_4,P_6$, bull)-free graph G with $\omega \left(G\right)\ge 3$ admits a perfect division, provided that G contains no homogeneous set. Moreover, we establish that this clique number condition is tight by presenting a counterexample with clique number of exactly 2. Full article

Video-based point cloud compression (V-PCC) is a 3D point cloud compression standard that first projects the point cloud from 3D space onto 2D space, thereby generating geometric and attribute videos, and then encodes the geometric and attribute videos using high-efficiency video coding (HEVC). In the whole coding process, the coding of geometric videos is extremely time-consuming, mainly because the division of geometric video coding units has high computational complexity. In order to effectively reduce the coding complexity of geometric videos in video-based point cloud compression, we propose a fast segmentation algorithm based on the occupancy type of coding units. First, the CUs are divided into three categories—unoccupied, partially occupied, and fully occupied—based on the occupancy graph. For unoccupied CUs, the segmentation is terminated immediately; for partially occupied CUs, a geometric visual perception factor is designed based on their spatial depth variation characteristics, thus realizing early depth range skipping based on visual sensitivity; and, for fully occupied CUs, a lightweight fully connected network is used to make the fast segmentation decision. The experimental results show that, under the full intra-frame configuration, this algorithm significantly reduces the coding time complexity while almost maintaining the coding quality; i.e., the BD rate of D1 and D2 only increases by an average of 0.11% and 0.28% under the total coding rate, where the geometric video coding time saving reaches up to 58.71% and the overall V-PCC coding time saving reaches up to 53.96%. Full article

Aiming at the partition requirements of regional centralized wide-area backup protection systems, this paper proposes a grid partition method considering communication constraints. Firstly, a mathematical model is constructed by combining the communication delay threshold and the channel bandwidth utilization rate, and the optimal number of master stations is dynamically determined. The communication delay threshold strictly meets the backup protection requirements of 10 to 15 ms, and the bandwidth utilization rate is optimized to a 50% balance point. Secondly, a weighted evaluation function is established based on the shortest communication distance and site connectivity, and the master station is selected according to a geographical dispersion constraint. Furthermore, the dual factors of data transmission delay and propagation delay are combined, in which the minimum bandwidth of the transmission delay correlation path and the length of the propagation delay correlation path are used to realize the optimal division of sub-stations using a graph theory algorithm. Finally, a standby master station with a physical isolation distance of not less than 50 km is selected in each sub-area to effectively deal with failure problems caused by a master station failure. The simulation results show that the proposed method can reduce the propagation delay and significantly improve the reliability of the protection system, while maintaining a balanced distribution of the number of substations. Full article

In recent years, a type of biochip known as a Digital Microfluidic Biochip (DMFB) has been actively researched in the field of life sciences. DMFBs perform dilution operations by mixing reagent solutions and buffer solutions at a 1:1 ratio to generate droplets with the desired concentration. One of the challenges of DMFBs is that droplets may not always be evenly split during the droplet division process. To address this issue, an error correction method utilizing error cancellation has been proposed. This method modifies the dilution graph to minimize the impact of division errors on the target node. However, this approach has a significant drawback: when large division errors occur in nodes close to the target node, they can introduce substantial concentration errors at the target node. In this paper, we propose a method that duplicates nodes near the target node and performs re-dilution to correct errors. Furthermore, we present an efficient and accurate error correction approach by modifying the dilution graph so that the output nodes of the dilution operation are at equal levels relative to the target node. Through simulations conducted 10,000 times, we demonstrate that our method effectively reduces the average concentration error at the target node. Full article

The agricultural products trade network is essentially a complex adaptive system formed by nonlinear interactions between countries. Based on the complex system theory, this study reveals the dynamic self-organization law of the RCEP regional agricultural products trade network by using the panel data of RCEP agricultural products export trade from 2000 to 2023, combining social network analysis (SNA) and the temporal exponential random graph model (TERGM). The results show the following: (1) The RCEP agricultural products trade network presents a “core-edge” hierarchical structure, with China as the core hub to drive regional resource integration and ASEAN countries developing into secondary core nodes to deepen collaborative dependence. (2) The “China-ASEAN-Japan-Korea “riangle trade structure is formed under the RCEP framework, and the network has the characteristics of a “small world”. The leading mode of South–South trade promotes the regional economic order to shift from the traditional vertical division of labor to multiple coordination. (3) The evolution of trade network system is driven by multiple factors: endogenous reciprocity and network expansion are the core structural driving forces; synergistic optimization of supply and demand matching between economic and financial development to promote system upgrading; geographical proximity and cultural convergence effectively reduce transaction costs and enhance system connectivity, but geographical distance is still the key system constraint that restricts the integration of marginal countries. This study provides a systematic and scientific analytical framework for understanding the resilience mechanism and structural evolution of regional agricultural trade networks under global shocks. Full article

This work investigates multi-UAV navigation tasks where multiple drones need to reach initially unassigned goals in a limited time. Reinforcement learning (RL) has recently become a popular approach for such tasks. However, RL struggles with low sample efficiency when directly exploring (nearly) optimal policies in a large exploration space, especially with an increased number of drones (e.g., 10+ drones) or in complex environments (e.g., a 3D quadrotor simulator). To address these challenges, this paper proposes Multi-UAV Scalable Graph-based Planner (MASP), a goal-conditioned hierarchical planner that reduces space complexity by decomposing the large exploration space into multiple goal-conditioned subspaces. MASP consists of a high-level policy that optimizes goal assignment and a low-level policy that promotes goal navigation. MASP uses a graph-based representation and introduces an attention-based mechanism as well as a group division mechanism to enhance cooperation between drones and adaptability to varying team sizes. The results demonstrate that MASP outperforms RL and planning-based baselines in task and execution efficiency. Compared to planning-based competitors, MASP improves task efficiency by over 27.92% in a 3D continuous quadrotor environment with 20 drones. Full article

Aiming at the difficulties of scarcity of defect samples on the surface of power insulators, irregular morphology and insufficient pixel-level localization accuracy, this paper proposes a defect detection method based on graph feature diffusion distillation named GFDD. The feature bias problem is alleviated by constructing a dual-division teachers architecture with graph feature consistency constraints, while the cross-layer feature fusion module is utilized to dynamically aggregate multi-scale information to reduce redundancy; the diffusion distillation mechanism is designed to break through the traditional single-layer feature transfer limitation, and the global context modeling capability is enhanced by fusing deep semantics and shallow details through channel attention. In the self-built dataset, GFDD achieves 96.6% Pi.AUROC, 97.7% Im.AUROC and 95.1% F1-score, which is 2.4–3.2% higher than the existing optimal methods; it maintains excellent generalization and robustness in multiple public dataset tests. The method provides a high-precision solution for automated inspection of insulator surface defect and has certain engineering value. Full article

This article investigates scaling laws within language families using data from over six thousand languages and analyzes emergent patterns observed in Zipf-like classification graphs. Both macroscopic (based on the number of languages by family) and microscopic (based on the number of speakers by language within a family) aspects of these classifications are examined. Particularly noteworthy is the discovery of a distinct division among the fourteen largest contemporary language families, excluding Afro-Asiatic and Nilo-Saharan languages. These families are found to be distributed across three language family quadruplets, each characterized by significantly different exponents in the Zipf graphs. This finding sheds light on the underlying structure and organization of major language families, revealing intriguing insights into the nature of linguistic diversity and distribution. Full article

Human action recognition has become a key direction in computer vision. Deep learning models, particularly when combined with sensor data fusion, can significantly enhance various applications by learning complex patterns and relationships from diverse data streams. Thus, this study proposes a new model, the Feature Fusion Graph Consecutive-Attention Network (FFGCAN), in order to enhance performance in the classification of the main tennis strokes: forehand, backhand, volley forehand, and volley backhand. The proposed network incorporates seven basic blocks that are combined with two types of module: an Adaptive Consecutive Attention Module, and Graph Self-Attention module. They are employed to extract joint information at different scales from the motion capture data. Due to focusing on relevant components, the model enriches the network’s comprehension of tennis motion data representation and allows for a more invested representation. Moreover, the FFGCAN utilizes a fusion of motion capture data that generates a channel-specific topology map for each output channel, reflecting how joints are connected when the tennis player is moving. The proposed solution was verified utilizing three well-known motion capture datasets, THETIS, Tennis-Mocap, and 3DTennisDS, each containing tennis movements in various formats. A series of experiments were performed, including data division into training (70%), validating (15%), and testing (15%) subsets. The testing utilized five trials. The FFCGAN model obtained very high results for accuracy, precision, recall, and F1-score, outperforming the commonly applied networks for action recognition, such as the Spatial-Temporal Graph Convolutional Network or its modifications. The proposed model demonstrated excellent tennis movement prediction ability. Full article