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Robust Multitask Diffusion Bias Compensation M-Estimate algorithms for Distributed Adaptive Learning With Noisy Input

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1146-1150页

作者： Peng, Senran Jia, Lijuan Yang, Zi-Jiang Tao, Ran Wang, Yue Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Ibaraki Univ Coll Engn Dept Mech Syst Engn Ibaraki 3168511 Japan

This letter studies the issue of robust multitask distributed estimation under the error-in-variable (EIV) model where input noise and output impulsive noise are considered. In such cases, existing distributed algorithms suffer from severe performance degradation. To tackle this problem, a robust multitask diffusion bias-compensated least mean M-estimate (R-MD-BCLMM) is proposed. We adopt a new real-time input noise variance estimation method which utilizes piecewise linearity of the modified Huber function to resist input noises. To further improve network information exchange capability and estimation performance, a robust spatial average combination based multitask adaptive clustering strategy is proposed. Finally, simulations demonstrate that the proposed R-MD-BCLMM algorithm outperforms some state-of-the-art distributed algorithms.

关键词： Noise Estimation Signal processing algorithms clustering algorithms Noise measurement Vectors Real-time systems Cost function Adaptation models Modeling Multitask networks robust M-estimate bias compensation spatial average combination

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Evaluation of the clustering Method Used to Analyze the Proximity of Mobile Devices Using Indirect Geolocation Indicators 22nd

Evaluation of the Clustering Method Used to Analyze the Prox...

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22nd International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2024

作者： Kobiela, Jaroslaw Urbaniec, Piotr Institute of Computer Science University of Opole Opole Poland

ISBN: (纸本)9783031780486

This study introduces a new methodology for clustering mobile devices to determine their geographical proximity without using direct geolocation data. Using the DBSCAN algorithm, we aim to identify significant spatial patterns while preserving user privacy. The dataset used includes information on device manufacturers and network interactions. clustering was performed after data preprocessing, with DBSCAN effectively grouping devices in proximity. Our results show that this method successfully identifies clusters that reflect meaningful geographical relationships among the devices. The evaluation included calculating the average and maximum distances within the clusters, demonstrating the robustness of the method. Despite its effectiveness, the success of the method depends on the quality and completeness of the input data. Future research should explore additional data sources and refine used algorithms to enhance accuracy and efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： clustering algorithms Geographical Proximity Mobile Devices

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clustering of Wind Power Patterns Based on Partitional and Swarm algorithms

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IEEE ACCESS 2020年 8卷 111913-111930页

作者： Munshi, Amr A. Umm Al Qura Univ Coll Comp & Informat Syst Comp Engn Dept Mecca 21955 Saudi Arabia

Wind power pattern clustering can potentially supply information about the effect of incorporating wind farms in smart electrical grid without in-depth analysis and studies of lengthy data. The present study investigates the most effective clustering technique and optimum number of clusters for wind power pattern data through various unsupervised clustering techniques. It also presents the introduction of Ant Colony and Bat, swarm optimization strategies in clustering wind power patterns. Three clustering algorithms from two different unsupervised techniques were concerned. A total of eight validity indices were used;Davies Bouldin, mean square error, mean index adequacy, ratio of within-cluster sum-of-squares to between-cluster-variation, Dunn, Silhouette, Xie-Beni, and clustering dispersion indicator for evaluation of the unsupervised clustering algorithms in inclusive manner. Findings depicted that Bat bio inspired clustering is comparative to K-means clustering and the most effective combination of clustering algorithm and validity index was K-means and Silhouette index, respectively. Secondly, in order to achieve improved clustering of WPP, the best clustering algorithm (K-means with Silhouette index) was modified by integrating the Silhouette index as an objective function for K-means. To check the potency of the produced wind power pattern representatives during a wind system simulation, a short wind generation prediction model is presented. The results of those cluster representatives presented promising short-term prediction results and suggest that the produced wind power pattern cluster representatives can potentially be used in other wind power pattern simulations.

关键词： Wind power generation clustering algorithms Wind speed Indexes Wind turbines Prediction algorithms Predictive models clustering wind power swarm methods power patterns

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Identification Based on clustering Algorithm and Terrain Analysis of Thunderstorm Transmission Line Proximity 11th

Identification Based on Clustering Algorithm and Terrain Ana...

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11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Li, Lei Huang, Dengyi Zhang, Lei Wang, Jian Fu, Mengdi Kong, Fanzhe North China Branch of State Grid Corporation of China Beijing100053 China Wuhan NARI Limited Liability Company State Grid Electric Power Research Institute Wuhan430074 China National Energy Key Laboratory of Lightning Disaster Detection Early Warning and Safety Protection Wuhan430074 China Hubei Key Laboratory of Power Grid Lightning Risk Prevention Wuhan430074 China

ISBN: (纸本)9789819788156

Lightning strikes are the primary factor causing tripping of overhead transmission lines, and analysis of thunderstorm activity trajectories can provide guidance for differentiated lightning protection of transmission lines. This paper proposes research method for the analysis of cloud-to-ground lightning based on clustering algorithm, which is used to evaluate the ground flash process of a transmission lines where lightning strikes tripped frequently. In order to describe the spatial and temporal evolution of cloud-to-ground activity;OPTICS algorithm is used to determine structure of cloud-to-ground lightning, based on which the parametric expression cloud-to-ground lightning of is carried out, and to study changes of cloud-to-ground lightning parameters with the topography. The results show that the frequency of cloud-to-ground lightning tends to decrease as the ground flash activity develops towards high-altitude regions. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： clustering algorithms

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clustering Algorithm for Vessel Trajectory Anomaly Detection Based on Binary Hashing Method 13

Clustering Algorithm for Vessel Trajectory Anomaly Detection...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Liang, Yuhan Ng, Wing W.Y. Li, Qihua Tian, Xing Su, Xiong Qiu, Qin Hou, Zhengzhi School of Computer Science and Engineering South China University of Technology Guangzhou China School of Artificial Intelligence South China Normal University Guangzhou China Anti-Drug Tethnology Center of Guangdong Province Guangzhou China

ISBN: (纸本)9798331516147

Timely identification of abnormal ships through trajectories make daily commercial trade transportation safer. However, current methods for identifying abnormal ships through vessel trajectories have issues such as high time cost and high sensitivity to parameters. To address such issues efficiently, a clustering method based on hashing algorithm applying Long Short-Term Memory neural networks is proposed to capture abnormal local and temporal information in trajectories. The trajectories are converted into hash codes that present thier characteristics succinctly. Then, the dominant set clustering method that does not require setting hyperparameters is used to cluster and find abnomal trajectories according to similarity relationship computed by corresponding hash codes. The proposed method is validated on a dataset of real vessel trajectories, which basically achieves the identification of abnormal vessel trajectories with lower time cost compared to other methods. © 2025 IEEE.

关键词： clustering algorithms

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Distributed Bundle Adjustment Based on Penalty Function Method

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第6期10卷 5449-5456页

作者： Sun, Qixuan Zhang, Guanghui Zhu, Dongchen Wang, Lei Zhang, Xiaolin Li, Jiamao Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Bion Vis Syst Lab State Key Lab Transducer Technol Shanghai 200050 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Bundle Adjustment (BA) aims to estimate the camera poses and build maps utilizing the nonlinear optimization algorithm. The update step of the optimization is obtained by solving a linear system, which is the bottleneck of the BA efficiency. Many works perform bundle adjustment in a distributed manner to reduce the computational cost of solving the linear system and achieve outstanding performance. However, the distributed methods implement the constrained optimization method that involves the computation of the Lagrange multiplier, causing extra overhead. To improve the efficiency of BA, we propose a novel distributed algorithm based on the penalty method, dubbed PenBA, that formulates an unconstrained problem and hence saves the computation of extra variables. To solve the unconstrained linear system with low cost, we design a novel distributed Preconditioned Conjugate Gradient (PCG) which has complexity bounded by hyper-parameters. It can be proved that, given the parameter update strategy of the optimization method, the penalty linear system can be solved with fast convergence. Experimental results in the BAL dataset illustrate that our penalty method outperforms the Lagrange multiplier method and conventional BA. Compared with the most accurate conventional PCG-based BA solver, PenBA decreases the objective values by 3.8$\%$, the time per iteration by 17$\%$ and the maximum memory consumption by 24$\%$. Experimental results in the SfM and SLAM datasets prove that PenBA applies to both computer vision and robotics contexts.

关键词： Linear systems Bundle adjustment Convergence Cameras Partitioning algorithms Time complexity Optimization methods Costs clustering algorithms Simultaneous localization and mapping SLAM localization mapping

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Ship Formation and Route Optimization Design Based on Improved PSO and D-P Algorithm

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IEEE ACCESS 2025年 13卷 15529-15546页

作者： Xu, Peilong Lan, Dan Yang, Haolin Zhang, Shengtian Kim, Hyeonseok Shin, Incheol Pukyong Natl Univ Dept Artificial Intelligence Convergence Pusan 48513 South Korea Qingdao Univ State Key Lab Biofiber & Ecotext Qingdao 266071 Peoples R China

The rapid development of the global shipping industry and the changes in complex marine environments have put forward higher requirements for ship formation and route optimization. The purpose of the research is to improve the efficiency and accuracy of ship formation and route planning through improved algorithms. Based on this, a ship formation model combining improved particle swarm optimization algorithm and a generative route optimization method based on improved Douglas-Peucker algorithm are proposed. The particle swarm algorithm introduces dynamic adaptive parameter adjustment and the cross mutation strategy of genetic algorithm, while the Douglas-Peucker algorithm integrates density-based noise application spatial clustering algorithm to improve model performance. The test results show that the total navigation distance of the allocation path generated by the ship formation model is 605.3 meters, the calculation time is 31.8 seconds, and all ships can be accurately allocated to the target point. When the number of iterations is 1000, the route optimization model has a route coverage rate of 95.9% on the training set, an average error of 30.5 meters, and a computation time of 45.9 seconds, achieving zero collisions. The experimental results show that the improved algorithm outperforms traditional methods in accuracy and stability of formation target allocation and route planning, especially under complex sea conditions, and can significantly reduce computation time and errors. The research provides a new technological means for optimizing ship formations and routes, which has certain application potential and practical value.

关键词： Marine vehicles Optimization Heuristic algorithms Resource management Accuracy clustering algorithms Mathematical models Planning Navigation Sea surface PSO D-P algorithm ship formation route optimization spatial clustering algorithm

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Energy efficient power cap configurations through Pareto front analysis and machine learning categorization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3433-3449页

作者： Cabrera, Alberto Almeida, Francisco Castellanos-Nieves, Dagoberto Oleksiak, Ariel Blanco, Vicente Univ La Laguna ULL Comp Sci & Syst Dept San Francisco de Paula s n San Cristobal la Laguna 38270 Spain Inst Bioorgan Chem PAS Poznan Supercomp & Netwoking Ctr ul Jana Pawla 210 PL-61139 Poznan Poland

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.

关键词： Energy aware computing Power capping Machine learning clustering algorithms

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Influence Nodes Identifying Method via Community-Based Backward Generating Network Framework

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 236-253页

作者： Liu, Xiaoyang Ye, Shu Fiumara, Giacomo De Meo, Pasquale Chongqing Univ Technol Sch Comp Sci & Engn Chongqing 400054 Peoples R China Univ Messina MIFT Dept I-98166 Messina Italy Univ Messina Dept Ancient & Modern Civilizat I-98166 Messina Italy

Traditional methods for influential node identification usually require time consuming network traversal to select the candidate node set. In this article we propose a new influence nodes identification method, called Community-based Backward Generating Network (CBGN). First, the influence maximization framework is built by integrating community detection and Backward Generation Network (BGN);then, nodes in each community are selected using a new method, called imp_BGN, that uses graph traversal to assist the construction of BGN. The ultimate goal of the network generation method is to find a sequence of nodes that can minimize the cost function, and to select high influential nodes without restoring the original network during network construction. finally, an improved submodular CELF (Cost Effective Lazy Forward) algorithm is proposed to hunt for the final seed node from the candidate node pool considering the location relation and structural similarity among nodes. Experimental results show that: in the SIR (susceptible-infected-recovered) model experiment, compared with the benchmark methods, the infection scale of the proposed CBGN method in 6 real networks is improved by 0.45%, 0.59%, 0.84%, 1.05%, 0.71% and 0.14%, respectively.

关键词： Robustness Heuristic algorithms Greedy algorithms clustering algorithms Monte Carlo methods Indexes Diffusion processes Node identify influence maximization community detection backward generation network

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Energy Efficient Tree-Based Routing Algorithm for Wireless Sensor Networks

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IEEE ACCESS 2025年 13卷 20149-20159页

作者： Chen, Ni Wen, Rong Chengdu Technol Univ Coll Network & Commun Engn Chengdu 611730 Sichuan Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China

To address the issue of uneven sensor node distribution and unbalanced energy consumption leading to premature node death in wireless sensor networks, an energy efficient tree-based routing algorithm is proposed. The algorithm calculates the optimal number of branches that minimize network energy consumption by constructing a tree-based energy model. Based on the optimal number of branches, with the base station as the root node, a multi-layer tree routing is formed from near to far according to the distance between the node and the base station. During the formation of routing tree, the nodes whose residual energy of the nodes is less than the energy threshold can only become end nodes, thus avoiding premature death of the nodes due to excessive energy consumption of the nodes. Nodes transmit data to the base station along the routing tree. The routing tree is updated at dynamic intervals instead of every round to reduce energy consumption. Simulation results show that the algorithm has more balanced node energy consumption, lower network energy consumption, and longer network stability period and network lifespan than the other three protocols.

关键词： Routing clustering algorithms Energy consumption Base stations Wireless sensor networks Energy efficiency Magnetic heads Routing protocols Nickel Machine learning algorithms Energy efficient optimal number of branches routing tree wireless sensor networks

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