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The observation data is the basis for the global navigation satellite system (GNSS) to provide positioning, navigation and timing (PNT) service, and the observation quality directly determines the performance level of the PNT service. At present, the analysis of GNSS observations quality is partial and can only be based on a single index assessment. GNSS observation quality is difficult to analyze comprehensively by fusing multiple indicators. To solve the above problem, the supervised and unsupervised machine learning algorithms are applied, and a new comprehensive and classification method of GNSS observations quality based on the k-means clustering algorithm (k-means) and K-nearest neighbor algorithm (KNN) was proposed. The four core index features of GNSS observations, including data integrity rate, carrier-to-noise-density ratio (CNR), pseudorange multipath and the number of observations per slip, were selected to construct the sample dataset. The sample set was unsupervised clustered based on the k-means algorithm, and the classification label of GNSS observations quality was obtained. Then KNN algorithm was used to construct a comprehensive classification and evaluation model for GNSS observations quality. The data from 30 MGEX stations in the Asia–Pacific region in 2019 were selected for modeling analysis. The experiment results show that: (1) a strong correlation has been revealed between pseudorange multipath, CNR and the number of observations per slip. (2) The average classification correctness rate of the new model was over 90% by n-fold cross-validation. (3) The new model can effectively realize the automatic evaluation and classification of GNSS observations quality and easily distinguish the superiority and inferiority of the station observations. The relevant results provide a new idea for the automatic classification and assessment of GNSS observation quality.

This paper presents a static network equivalent model for Korean power systems. The proposed equivalent model preserves the overall transmission network characteristics focusing on power flows among areas in Korean power systems. For developing the model, a power transfer distribution factor (PTDF)-based k-means++ algorithm was used to cluster the bus groups in which similar PTDF characteristics were identified. For the reduction process, the bus groups were replaced by a single bus with a generator or load, and an equivalent transmission line was determined to maintain power flows in the original system model. Appropriate voltage levels were selected, and compensation for real power line losses was made for the correct representation. A Korean power system with more than 1600 buses was reduced to a 38-bus system with 13 generators, 25 loads, and 74 transmission lines. The effectiveness of the developed equivalent model was evaluated by performing power flow simulations and comparisons of various characteristics of the original and reduced systems. The simulation comparisons show that the developed equivalent model maintains inter-area power flows as close as possible to the original Korean power systems.

Grounding grid corrosion is one of the main reasons that affect the stable operation of electrical equipment in substations and endanger personal safety. After many years of operation, the grounding conductors will be eroded by soil. It may even cause major accidents and economic losses. Therefore, it is of great significance to diagnose the corrosion faults of the grounding grid and find out the corroded conductors. In this paper, the genetic K‐means algorithm (GKA) is proposed to solve the mathematical model and judge the corrosion of grounding conductors. This algorithm combines GA's global searching ability and K‐means's local searching ability, which improves the diagnosis result. In the simulation experiment, compared with the single GA's diagnosis, the diagnosis results of GKA were improved, and the number of misdiagnosed branches decreased by 66.7%. The simulation results show that the proposed algorithm takes less time to run, can eliminate the misdiagnosed branches commendably, and improve the accuracy of diagnosis. The proposed method provides a new idea to evaluate the corrosion degree of the grounding grid. The clustering algorithm is used to classify branches with similar corrosion degrees to achieve the purpose of corrosion diagnosis. Flow chart of genetic K‐means algorithm for corrosion diagnosis of grounding grid.

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

【Background and Objective】Accurate prediction of dam deformation is crucial for ensuring the safety of dam structures in engineering monitoring. Dam deformation is influenced by multiple factors, including water pressure, temperature, and material aging, which often exhibit nonlinear and dynamic relationships. During monitoring, system noise and observation errors frequently interfere with data quality, posing additional challenges for analysis. To address the challenges posed by system noise and strong nonlinear effects in dam deformation, this paper proposes a dam deformation monitoring model based on multi-layer integrated signal processing technology.【Method】The model uses sample entropy reconstruction and the K-means clustering algorithm to optimize the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) process, generating multiple intrinsic mode functions (IMF). High-frequency modal components undergo secondary decomposition using variational mode decomposition (VMD) to extract the optimal intrinsic mode function. Finally, an improved symbiotic biological search algorithm combined with a Bidirectional Gated Recurrent Unit (BiGRU) is used to accurately predict dam deformation.【Result】Case analysis demonstrates that, compared to traditional prediction models, the proposed model achieves a root mean square error (RMSE) of 0.031 9 mm, mean absolute error (MAE) of 0.015 3 mm, mean absolute percentage error (MAPE) of 2.51%, and determination coefficient (R2) of 0.971 2.【Conclusion】 The results verify that the proposed model captures and simulates the dam deformation process more accurately, exhibiting higher prediction accuracy and stronger generalization ability.

Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance (DED) measure. In the first phase, the fast global K-means clustering algorithm is used to obtain the cluster number and the prototypes. Then, the prototypes of all these clusters and representatives of points belonging to these clusters are regarded as the input data set of the second phase. Afterwards, all the prototypes are clustered according to a DED measure which makes data points locating in the same structure to possess high similarity with each other. In experimental studies, the authors test the proposed algorithm on seven artificial as well as seven UCI data sets. The results demonstrate that the proposed algorithm is flexible to different data distributions and has a stronger ability in clustering data sets with complex non-convex distribution when compared with the comparison algorithms.

To address the range-angle coupling issue of frequency diverse array (FDA), a beamforming method based on subarray partitioning is proposed, with a focus on analyzing uniform continuous and nonuniform discontinuous subarray structures. Based on the transmit–receive signal model established to solve the time-varying issue of FDA, two subarray partitioning methods under the same array aperture are investigated. In the case of uniform continuous subarray structure, when different linear frequency offsets (FOs) are applied to each subarray, the mainlobe width in range dimension is 4.35 km, and the peak sidelobe level (PSLL) is −7.25 dB. When nonlinear FOs are applied, the mainlobe width is reduced to 2.76 km, and the PSLL is decreased to −9.64 dB. Furthermore, by adopting a nonuniform discontinuous subarray structure combined with nonlinear FOs, the mainlobe width is further narrowed to 1.29 km, and the PSLL is reduced to −11.75 dB. The simulation results demonstrate that under the same conditions, the nonuniform discontinuous subarray structure significantly improves range resolution and effectively suppresses sidelobe. Based on above results, a joint optimization combining the bat algorithm (BA) and K-means++ clustering algorithm is proposed to optimize the subarray structure and element amplitudes simultaneously. Finally, the mainlobe width of the optimized FDA is 1.18 km and the PSLL is −12.32 dB. Simulation results confirm the effectiveness and potential of the proposed method in enhancing range resolution and achieving a focused beampattern.

In response to the limitations of existing network security alert screening methods in handling high-noise and incomplete data, this paper proposes a multi-level alert screening framework based on DBSCAN density clustering and RETE rule reasoning. The proposed method achieves adaptive analysis and precise screening of alert data by constructing a multi-stage processing pipeline that integrates density clustering, fuzzy reasoning, and dynamic neural networks. Key innovations include: employing the DBSCAN algorithm to perform unsupervised clustering and noise identification of alert data; introducing an improved RETE rule reasoning mechanism that supports weighted fuzzy matching to enhance fault tolerance for incomplete alert streams; and designing a BP neural network with dynamically adjustable structure to achieve accurate alert classification. Experimental results demonstrate that the proposed method achieves significant performance advantages on multiple real-world and benchmark datasets, with a true positive rate of 96.6%, a noise rate controlled within 18.7%, and CPU utilization below 1%, substantially outperforming existing mainstream solutions and exhibiting high practical application value.

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VKECE-3D, based on 3D-Voronoi partitioning and the K-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the K-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes’ energy consumption and lengthen the network’s lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VKECE-3D improves network coverage and greatly lengthens the network’s lifetime.

Unmanned on-board mobile base stations (MBSs) can more effectively solve wireless connectivity problems in terrestrial communication networks without fixed infrastructure. The purpose of this article is to minimize the number of MBS required to provide wireless coverage for a set of distributed ground terminals (GTs). Traditional clustering algorithms are no longer applicable because each drone has a different coverage area size and the traditional K-Means clustering algorithm has no limit on the number of heaps that can exceed the maximum coverage area of a single drone, making it impossible for a drone to provide services. In response to this problem, the traditional K-Means clustering algorithm is optimized, and the results of the optimized K-Means clustering algorithm are stacked to ensure that each pile has the corresponding drone capability to serve it.