Search Results (110)

Hyperspectral anomaly detection (HAD) refers to a method of identifying abnormal targets through the differences in spectral separabilities of anomaly versus background clutter. It plays a significant role in fields such as commercial agriculture, for instance, in pest and disease monitoring and environmental monitoring. Collaborative representation detector (CRD) is a classic hyperspectral anomaly detection method. However, by constructing a sliding dual window, it leads to a high computational complexity and thus takes a relatively long time. In response to the deficiencies existing in that CRD method, we propose a method that first extracts extended morphological profiles (EMP) and then uses the obtained feature images to construct K-means CRD (EMPKCRD). This method performs window reconstruction on complex hyperspectral background pixels through the K-means clustering algorithm to separate abnormal pixels with similar features and obtain the background dictionary matrix. The method leverages the observation that background pixels can be effectively approximated by a linear combination of their spatially adjacent pixels, whereas anomalous pixels, due to their distinct nature, cannot be similarly reconstructed from their local neighborhood. This fundamental disparity in reconstructibility is then exploited to separate anomalies from the background. Then, anomaly detection can be carried out on this matrix faster, avoiding the high computational complexity caused by the use of a sliding dual window. Through comparative simulation experiments with seven widely used algorithms at present on three real-world datasets, the empirical evaluations validate that this method has excellent performance while exhibiting a favorable balance between detection precision and operational speed. Full article

To improve the efficiency of multi-region multi-unmanned aerial vehicle (UAV) inspection, this paper proposes a composite task planning strategy integrating the K-Means++ genetic algorithm (KMGA) and the multi-neighborhood iterative dynamic programming (MNIDP) method. Firstly, the multi-region multi-UAV inspection problem is modeled as a multiple traveling salesmen problem with neighborhoods (MTSPN). Then, this problem is decomposed into two interrelated subproblems to mitigate the complexity inherent in the solution process: that is, the multiple traveling salesmen problem (MTSP) and multi-neighborhoods path planning (MNPP) problem. Based on this decomposition, the MTSP is solved by the KMGA by converting it into m spatially non-overlapping traveling salesmen problems (TSPs) and then these TSPs are solved to obtain the approximate optimal visiting sequences for the nodes in each TSP in a short time. Subsequently, the MNPP can be efficiently solved by an MNIDP which plans the paths between the corresponding neighborhood of each node based on the node visiting sequences, thus obtaining the approximate optimal path length of the MTSPN. The simulation results demonstrate that the proposed composite strategy exhibits advantages in computational efficiency and optimal path length. Specifically, compared to the baseline algorithm, the average tour length obtained by the KMGA decreased by 23.24%. Meanwhile, the average path lengths computed by MNIDP in three instances were reduced from 8.00% to 11.41% and from 6.46% to 10.08% compared to two baseline algorithms, respectively. It provides an efficient task and path planning solution for multi-region multi-UAV operations in power transmission line inspections, thereby enhancing inspection efficiency. Full article

This study introduces a parametric multi-objective optimization framework for urban block morphology. It integrates micro-climate data corrected by the Urban Weather Generator (UWG), energy simulation through EnergyPlus and Honeybee, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) within the Wallacei platform. Using Wuhan, China, a city with a representative hot-summer and cold-winter climate, as a case study, the framework simultaneously optimizes three key objectives: Average Sunshine Hours (Av.SH), Energy Use Intensity (EUI), and Average Universal Thermal Climate Index (Av.UTCI). The framework systematically links parametric modeling, environmental simulation, and evolutionary optimization to explore how block typologies and height configurations affect the trade-offs among solar access, energy demand, and outdoor thermal comfort. Among the feasible solutions, Av.SH exhibits the greatest variation, ranging from 4.30 to 7.93 h, followed by Av.UTCI (44.13 to 45.46 °C), while EUI shows the least fluctuation, from 91.69 to 93.36 kWh/m². Key design variables, such as building type and height distribution, critically influence the outcomes. Optimal configurations are achieved by interweaving low-rise (2 to 3 floors), mid-rise (6 to 8 floors), and high-rise (15 to 20 floors) buildings to enhance openness and ventilation. The proposed framework offers a quantifiable strategy for guiding future climate-responsive and energy-efficient neighborhood design. Full article

Education has become one of the important drivers of residential mobility. The school district system in China has transformed school choice into a competition for housing ownership based on family capital, resulting in the capitalization of education and gentrification. Understanding the patterns of education-driven residential mobility is therefore of significant importance for urban planning, educational policy and social equity research. In this study, we depicted and analyzed the heterogeneity of residential mobility formed by the interaction of schooling choice, diversity of family characteristics, and housing preferences. Based on the household questionnaire survey conducted in Tianjin, we identified five typical education-driven residential mobility patterns by using the K-Prototype clustering algorithm. The empirical results implied that in China, particularly in megacities like Tianjin with a strict school district system tied to housing, wealthy families approach high-quality education through their socio-economic advantages for cultural reproduction; families sacrifice living conditions to access leading schools by acquiring old second-hand housing or smaller new-commercial housing; lower-income families relocate to within a short distance of the city center to change home ownership status for basic school eligibility; and families opting out of school districts achieve residential improvements and display greater locational diversity in relocation. Education-driven residential mobility is reshaping urban space, and may intensify socio-spatial stratification, even influencing long-term urban sustainability through patterns of resource allocation, neighborhood stability, and social equity. While this study focuses on Tianjin, the impacts of such school-housing-linked policies hold broader relevance for global cities facing similar challenges. Full article

Hyperspectral band selection (BS) is an important technique to reduce data dimensionality for the classification applications of hyperspectral remote sensing images (HSIs). Recently, searching-based BS methods have received increasing attention for their ability to select the best subset of bands while preserving the essential information of the original data. However, existing searching-based BS methods neglect effective exploitation of the spatial and spectral prior information inherent in the data, thus limiting their performance. To address this problem, in this study, a novel unsupervised BS method called Spectral–Spatial Iterative Greedy Algorithm (SSIGA) is proposed. Specifically, to facilitate efficient local search using spectral information, SSIGA conducts clustering on all the bands by employing a K-means clustering method with balanced cluster size constraints and constructs a K-nearest neighbor graph for each cluster. Based on the nearest neighbor graphs, SSIGA can effectively explore the neighborhood solutions in local search. In addition, to efficiently evaluate the discriminability and information redundancy of the solution given by SSIGA using the spatial and spectral information of HSIs, we designed an effective objective function for SSIGA. The value of the objective function is derived by calculating the Fisher score for each band in the solution based on the results of the superpixel segmentation performed on the target HSI, as well as by computing the average information entropy and mutual information of the bands in the solution. Experimental results on three publicly available real HSI datasets demonstrate that the SSIG algorithm achieves superior performance compared to several state-of-the-art methods. Full article

The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty. Full article

Forest spatial structure is of significant importance for studying forest biomass accumulation and management. However, above-ground biomass (AGB) estimation based on satellite remote sensing struggles to capture forest spatial structure information, which to some extent affects the accuracy of AGB estimation. To address this issue, this study focused on Chinese fir (Cunninghamia lanceolata) plantations in Zhejiang Province. Using UAV-LiDAR (unmanned aerial vehicle light detection and ranging) data and a seed-point-based individual tree segmentation algorithm, information on individual fir trees was obtained. Building on this foundation, structural parameters such as neighborhood comparison (U), crowding degree (C), uniform angle index (W), competition index (CI), and canopy openness (K) were calculated, and their distribution characteristics analyzed. Finally, these parameters were integrated with UAV-LiDAR point cloud features to build machine learning models, and a geographical detector was used to quantify their contribution to AGB estimation. The research findings indicate the following: (1) The studied stands exhibited a random spatial pattern, moderate competition, and sufficient growing space. (2) A significant correlation existed between the U and AGB (r > 0.6), followed by CI. The optimal stand structure for AGB accumulation was C = 0.25, U < 0.5, CI in (0, 0.8], and K > 0.3. (3) The four machine learning models constructed by coupling spatial structure with point cloud features all improved the accuracy of AGB estimation for the fir forest to some extent. Among them, the XGBoost model performed best, achieving a model accuracy (R²) of 0.92 and a relatively low error (RMSE = 14.02 kg). (4) Geographical detector analysis indicated that U and CI contributed most to AGB estimation, with q-values of 0.44 and 0.37, respectively. Full article

Detecting leakage using a point cloud acquired by mobile laser scanning (MLS) presents significant challenges, particularly from within three-dimensional space. These challenges primarily arise from the prevalence of noise in tunnel point clouds and the difficulty in accurately capturing the three-dimensional morphological characteristics of leakage patterns. To address these limitations, this study proposes a classification method based on XGBoost classifier, integrating both intensity and geometric features. The proposed methodology comprises the following steps: First, a RANSAC algorithm is employed to filter out noise from tunnel objects, such as facilities, tracks, and bolt holes, which exhibit intensity values similar to leakage. Next, intensity features are extracted to facilitate the initial separation of leakage regions from the tunnel lining. Subsequently, geometric features derived from the k neighborhood are incorporated to complement the intensity features, enabling more effective segmentation of leakage from the lining structures. The optimal neighborhood scale is determined by selecting the scale that yields the highest F_1-score for leakage across various multiple evaluated scales. Finally, the XGBoost classifier is applied to the binary classification to distinguish leakage from tunnel lining. Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. The consistent performance across four heterogeneous datasets indicates the robust generalization capability of the proposed methodology. Comparative analysis further shows that XGBoost outperforms other classifiers, such as Random Forest, AdaBoost, LightGBM, and CatBoost, in terms of balance of accuracy and computational efficiency. Moreover, compared to deep learning models, including PointNet, PointNet++, and DGCNN, the proposed method demonstrates superior performance in both detection accuracy and computational efficiency. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

Large language models (LLMs) have driven transformative progress in artificial intelligence, yet critical challenges persist in data management and privacy protection during model deployment and training. The approximate nearest neighbor (ANN) search, a core operation in LLMs, faces inherent trade-offs between efficiency and security when implemented through conventional locality-sensitive hashing (LSH)-based secure ANN (SANN) methods, which often compromise either query accuracy due to false positives. To address these limitations, this paper proposes a novel secure ANN scheme based on nearest neighbor graph (NNG-SANN), which is designed to ensure the security of approximate k-nearest neighbor queries for vector data commonly used in LLMs. Specifically, a secure indexing structure and subset partitioning method are proposed based on LSH and NNG. The approach utilizes neighborhood information stored in the NNG to supplement subset data, significantly reducing the impact of false positive points generated by LSH on query results, thereby effectively improving query accuracy. To ensure data privacy, we incorporate a symmetric encryption algorithm that encrypts the data subsets obtained through greedy partitioning before storing them on the server, providing robust security guarantees. Furthermore, we construct a secure index table that enables complete candidate set retrieval through a single query, ensuring our solution completes the search process in one interaction while minimizing communication costs. Comprehensive experiments conducted on two datasets of different scales demonstrate that our proposed method outperforms existing state-of-the-art algorithms in terms of both query accuracy and security, effectively meeting the precision and security requirements for nearest neighbor queries in LLMs. Full article

The track-to-track association (T2TA) problem is a fundamental and critical challenge in information fusion, situational awareness, and target tracking. Existing algorithms based on statistical mathematics, fuzzy mathematics, gray theory, and artificial intelligence suffer from several limitations that are hard to solve, such as over-idealized models, unrealistic assumptions, insufficient real-time performance, and high computational complexity due to pairwise matching requirements. Considering these limitations, we propose a self-adaptive step-2-based K-nearest neighbor density peak clustering (SAS-KNN-DPC) algorithm to address T2TA problem. Firstly, the step-2 temporal neighborhood affinity matrix under a non-registration framework is defined and the calculation methods for multi-feature track-point fusion similarity matrix are given. Secondly, the proposed self-adaptive multi-feature similarity truncation matrix is defined to measure the multidimensional distance between track points and the self-adaptive step-2 truncation distance is also defined to enhance the adaptivity of the algorithm. Finally, we propose improved definitions of local distance and global relative distance to complete both cluster center selection and association assignment. The proposed algorithm eliminates the need for exhaustive pairwise matching between track sequences and avoids time alignment, significantly improving the real-time performance of T2TA. Simulation results demonstrate that compared to other algorithms, the proposed algorithm achieves higher accuracy, reduced computational time, and better real-time performance in complex scenarios. Full article

The identification of gully-type debris flow remains a challenging task due to the irregularity of terrain, which causes significant fluctuations in local curvature and hinders accurate feature extraction using traditional methods. To address this issue, this study proposes a novel identification approach based on point cloud local curvature extrema. The methodology involves collecting image data of debris flow and landslide areas using DJI Matrice 300 RTK (M300RTK), planning control points and flight routes, and generating three-dimensional point cloud data through image matching and point cloud reconstruction techniques. A quadratic surface fitting method was employed to calculate the curvature of each point in the point cloud, while a topological k-neighborhood algorithm was introduced to establish spatial relationships and extract extreme curvature features. These features were subsequently used as inputs to a convolutional neural network (CNN) for landslide identification. Experimental results demonstrated that the CNN architecture used in this method achieved rapid convergence, with the loss value decreasing to 0.0032 (cross-entropy loss) during training, verifying the model’s effectiveness. The introduction of early stopping and learning rate decay strategies effectively prevented overfitting. Receiver-operating characteristic (ROC) curve analysis revealed that the proposed method achieved an area under the ROC curve (AUC) of 0.92, significantly outperforming comparative methods (0.78–0.85). Full article

Currently, urban development has entered the stage of renewal and transformation. Energy transition is an important trend for sustainable urban development, and the assessment of solar energy potential in old residential areas in cold regions is of great significance. This study selects 47 old residential communities in Jinan, a cold region of China, as case samples. Using clustering algorithms based on spatial form characteristic parameters, the study divides the samples into five categories. The study then uses the Ladybug tool to simulate the distribution and total solar energy utilization potential of buildings in the five categories and analyzes the correlation between eight spatial form parameters and building solar energy potential. A linear regression model is established, and strategies for the application of BIPV in community buildings are proposed. The study finds that factors such as plot ratio, building density, open space ratio, volume-to-surface ratio, and form coefficient have a significant impact on the solar energy potential of residential communities; the p-values are −0.785, −0.783, 0.783, −0.761, and 0.724, respectively. Among these, building density (BD) is the most crucial factor affecting the solar energy potential of building facades. Increasing by one unit can reduce the solar energy utilization potential by 28.00 kWh/m²/y. At the same time, installing photovoltaic panels on old residential buildings in cold regions can reduce building carbon emissions by approximately 48%. The research findings not only provide methodological references for photovoltaic technology application at varying neighborhood scales in urban settings but also offer specific guidance for low-carbon retrofitting of aging urban communities, thereby facilitating progress in urban carbon emission reduction. Full article

Clustering by Measuring Local Direction Centrality (CDC) is a recently proposed innovative clustering method. It identifies clusters by assessing the direction centrality of data points, i.e., the distribution of their k-nearest neighbors. Although CDC has shown promising results, it still faces challenges in terms of both effectiveness and efficiency. In this paper, we propose a novel algorithm, Distributed Clustering with Local Direction Centrality and Density Measure (DEALER). DEALER addresses the problem of weak connectivity by using a well-designed hybrid metric of direction centrality and density. In contrast to traditional density-based methods, this metric does not require a user-specified neighborhood radius, thus alleviating the parameter-setting burden on the user. Further, we propose a distributed clustering technique empowered by z-value filtering, which significantly reduces the cost of k-nearest neighbor computations in the direction centrality metric, lowering the time complexity from $O\left(n^2\right)$ to $O(nlogn)$. Extensive experiments on both real and synthetic datasets validate the effectiveness and efficiency of our proposed DEALER algorithm. Full article

Migraine is a chronic neurovascular disease with unclear pathophysiological mechanisms. In this study, its pathogenic mechanisms were investigated through bioinformatics analysis of migraine-related pathways and key genes. Female Sprague Dawley rats were divided into control and migraine model groups. The control group received saline, while the migraine model group received nitroglycerin (NTG) to induce migraines over four weeks. Migraine-like behaviors were assessed within two hours following the final NTG injection. Genes of hypothalamus were identified using DESeq2. Gene ontology enrichment and KEGG pathway analyses were conducted, followed by the identification of hub genes based on protein interaction networks by using algorithms such as Closeness, Degree, and Maximum Neighborhood Component. Rats with NTG-induced migraine showed increased head scratching and cage climbing and a reduced sucrose preference. Transcriptome analysis revealed 1564 differentially expressed genes, with 1233 upregulated and 331 downregulated. Pathways linked to inflammation, PI3K–Akt signaling, and cytokine–cytokine receptor interactions were found to have enriched expression of several genes. Further protein interaction network analysis identified nine hub genes: Alb, Tgfb1, Cd4, Ptprc, Itgb1, Icam1, Col1a1, Pxdn, and Itgad. These findings suggest that migraine involves PI3K–Akt signaling and cytokine–cytokine receptor interactions, providing insights into molecular mechanisms and potential therapeutic targets. However, the study was limited by a small sample size and reliance on a single experimental model, which may constrain the clinical applicability of the findings. Full article