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Extended DL Coverage in SAGIN: Cell Association and Resource Allocation With Beam Hopping LEO

IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 6014-6028页

作者： Eberechukwu, N. Paulson Onyekwelu, Michael Yoon, Dongweon Hanyang Univ Dept Elect Engn Seoul 04763 South Korea

Realizing the potential of low-Earth orbit (LEO)-based space-air-ground integrated networks requires mitigating challenges associated with LEO satellites, such as limited on-board resources, extended downlink (DL) coverage, temporal variability, and dynamic traffic demand. To mitigate these challenges, an efficient resource allocation (RA) system for beam-hopping can be designed. In this article, we address two practical issues in multidimensional RA for beam-hopping LEO under mixed channel conditions. First, we propose a new clustering algorithm for user equipment cell associations. Second, we decompose the RA problem into three subproblems: 1) time-frequency;2) power;and 3) dynamic RA, and systematically address each subproblem to develop a comprehensive RA approach. We introduce a heuristic approach for time-frequency RA, achieving a good balance between performance and computational complexity. We extend the proposed algorithm to a practical dynamic RA scenario, where the channel parameters are unknown random variables throughout the next time slots, which makes the optimization problem an intrinsically stochastic problem. In addition, throughput balancing for several time slots becomes a dynamic process. Then, we use sequential convex approximations for power allocation and dynamic programming for dynamic RA. Numerical results show that the proposed algorithms outperform baseline methods in delay, capacity (the theoretical maximum data rate), and throughput (the actual data rate achieved).

关键词： Heuristic algorithms Low earth orbit satellites Satellites Space-air-ground integrated networks Resource management Dynamic scheduling clustering algorithms Optimization Computer architecture Satellite broadcasting Cell association low-Earth orbit (LEO) satellite optimization resource allocation (RA) space-air-ground integrated network (SAGIN)

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Unconstrained Fuzzy C-Means Algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第5期47卷 3440-3451页

作者： Nie, Feiping Zhang, Runxin Yu, Weizhong Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Shaanxi Peoples R China China Telecom Corp Ltd Inst Artificial Intelligence TeleAI Beijing 100033 Peoples R China

Fuzzy C-Means algorithm (FCM) is one of the most commonly used fuzzy clustering algorithm, which uses the alternating optimization algorithm to update the membership matrix and the cluster center matrix. FCM achieves effective results in clustering tasks. However, due to many constraints, the objective function is inconvenient to optimize directly and is prone to converges to a suboptimal local minimum, which affects the clustering performance. In this paper, we propose a minimization problem equivalent to FCM. Firstly, we use the optimal solution when fixing the cluster center matrix to replace the membership matrix, transforming the original constrained optimization problem into an unconstrained optimization problem, thus reducing the number of variables. We then use gradient descent instead of alternating optimization to solve the model, so we call this model UC-FCM. Extensive experimental results show that UC-FCM can obtain better local minimum and achieve superior clustering performance compared to FCM under the same initialization. Moreover, UC-FCM is also competitive compared with other advanced clustering algorithms.

关键词： clustering algorithms Optimization Linear programming Convergence Time complexity Symbols Noise Vectors Standards Sensitivity Fuzzy C-Means gradient descent membership matrix local minimum

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Distributed Online Generalized Nash Equilibrium Learning in Multi-Cluster Games: A Delay-Tolerant Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Liu, Bingqian Wen, Guanghui Fang, Xiao Huang, Tingwen Chen, Guanrong Southeast Univ Sch Math Dept Syst Sci Lab Secur Operat & Control Intelligent Autonomous Nanjing 211189 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

This paper addresses the problem of distributed online generalized Nash equilibrium (GNE) learning for multi-cluster games with delayed function feedback. Specifically, each agent in the game is assumed to be informed of a sequence of local cost functions and constraint functions, which are known to the agent with time-varying delays subsequent to decision-making at each round. The objective of each agent within a cluster is to collaboratively optimize the cluster's cost function, subject to time-varying coupled inequality constraints and local constraint sets over time. Additionally, it is assumed that each agent is required to estimate the decisions of all other agents through interactions with its neighbors, rather than directly accessing the decisions of all agents, i.e., each agent needs to make decisions under partial-decision information. To solve such a challenging problem, a novel distributed online delay-tolerant GNE learning algorithm is developed based upon the primal-dual algorithm with an aggregation gradient mechanism. The system-wise regret and the constraint violation are formulated to measure the performance of the algorithm, demonstrating sublinear growth with respect to the time horizon ${\boldsymbol} T$ under certain conditions. Finally, numerical results are presented to verify the effectiveness of the proposed algorithm.

关键词： Games Delays Cost function Vectors Electronic mail Decision making clustering algorithms Nash equilibrium Time measurement Heuristic algorithms Distributed optimization online multi-cluster game generalized Nash equilibrium time-varying delay

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Two-Dimensional Balanced Partitioning and Efficient Caching for Distributed Graph Analysis

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第2期36卷 133-149页

作者： Lin, Shuai Wang, Rui Li, Yongkun Xu, Yinlong Lui, John C. S. Univ Sci & Technol China Hefei 230026 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Univ Sci & Technol China AnHui Prov Key Lab High Performance Comp Hefei 230026 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

Distributed graph analysis usually partitions a large graph into multiple small-sized subgraphs and distributes them into a cluster of machines for computing. Therefore, graph partitioning plays a crucial role in distributed graph analysis. However, the widely used existing graph partitioning schemes balance only in one dimension (number of edges or vertices) or incur a large number of edge cuts, so they degrade the performance of distributed graph analysis. In this article, we propose a novel graph partition scheme BPart and two enhanced algorithms BPart-C and BPart-S to achieve a balanced partition for both vertices and edges, and also reduce the number of edge cuts. Besides, we also propose a neighbor-aware caching scheme to further reduce the number of edge cuts so as to improve the efficiency of distributed graph analysis. Our experimental results show that BPart-C and BPart-S can achieve a better balance in both dimensions (the number of vertices and edges), and meanwhile reducing the number of edge cuts, compared to multiple existing graph partitioning algorithms, i.e., Chunk-V, Chunk-E, Fennel, and Hash. We also integrate these partitioning algorithms into two popular distributed graph systems, KnightKing and Gemini, to validate their impact on graph analysis efficiency. Results show that both BPart-C and BPart-S can significantly reduce the total running time of various graph applications by up to 60% and 70%, respectively. In addition, the neighbor-aware caching scheme can further improve the performance by up to 24%.

关键词： Partitioning algorithms Computational modeling Synchronization Costs Analytical models Social networking (online) Reviews Load modeling clustering algorithms Blogs Distributed graph systems graph partition graph algorithms graph processing

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Max-Sum-Based Data Associations for Tracking Point and Extended Targets

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 3059-3075页

作者： Ma, Weizhen Jing, Zhongliang Dong, Peng Leung, Henry Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

For multitarget tracking applications, data association is a fundamental problem of assigning measurements to their corresponding targets. In this article, we propose two algorithms for tracking point and extended targets, respectively, based on factor graph representations of the joint probability density functions. Both employ the max-sum (MS) algorithm to find the maximum a posteriori assignment such that the state of each target is updated with the most probable measurement(s). We model the single target densities as Gaussian distribution for point targets and gamma Gaussian inverse Wishart distribution for extended targets. Under linear Gaussian assumptions on the target models, the proposed algorithms provide analytical solutions to multitarget tracking problems. Specifically, the messages flowed in the factor graphs, existence probabilities and states of the targets are analytically calculated. These two algorithms have reduced computational load compared to the particle-based sum-product (SP) algorithms and avoid gating or clustering used by traditional multitarget tracking methods. We compare the proposed MS-based algorithms (MSAs) with the Poisson multi-Bernoulli mixture filters and the SP-based algorithms, and simulation results show that the MSAs have comparable or improved tracking performance.

关键词： Target tracking Filters clustering algorithms Approximation algorithms Probability density function Heuristic algorithms Aerospace and electronic systems Probabilistic logic Kinematics Extraterrestrial measurements Data association (DA) extended target tracking (ETT) factor graph max-sum (MS) algorithm point target tracking (PTT)

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Illicit Social Accounts? Anti-Money Laundering for Transactional Blockchains

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 391-404页

作者： Song, Jie Zhang, Sijia Zhang, Pengyi Park, Junghoon Gu, Yu Yu, Ge Northeastern Univ Software Coll Shenyang Liaoning Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang Liaoning Peoples R China

recent years, blockchain anonymity has led to more illicit accounts participating in various money laundering transactions. Existing studies typically detect money laundering transactions, known as AML (Anti-money Laundering), through learning transaction features on transaction graphs of transactional blockchains. However, transaction graphs fail to represent the accounts' social features within transactional organizations. Account graphs reveal such features well, and detecting illicit accounts on account graphs provides a new perspective on AML. For example, it helps uncover illegal transactions whose transaction features are not distinct in transaction graphs, with a loose assumption that illicit accounts are likely involved in illegal transactions. In this paper, we propose a Social Attention Graph Neural Network ( SGNN ) on account graphs converted from transaction graphs. To detect illicit accounts, SGNN learns the social features on two sub-graphs, a heterogeneous graph and a hypergraph, extracted from the account graph, and fuses these features into account attribute vectors through attention. The experimental results on the Elliptic++ dataset demonstrate SGNN's advances. It outperforms the best baseline by 14.18% in precision, 7.37% in F1 score, 0.96% in accuracy, and 0.64% in recall when detecting illicit accounts on account graphs, as well as detects 20.3% more recall of illegal transactions through these illicit accounts than state-of-the-art methods based on transaction graphs when the mappings between illegal transactions and illicit accounts are provided. Moreover, thanks to social features, SGNN has a novel capability that works under many account scales and activity degrees. We release our code on https:// ***/CloudLab-NEU/SGNN.

关键词： Blockchains Feature extraction Automated machine learning Law Bitcoin Graph neural networks Vectors Machine learning algorithms Data models clustering algorithms Blockchain anti-money laundering social features anonymity cryptocurrency graph neural network attention mechanism

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Scheduling Single-Arm Cluster Tools With an Equipment Front-End Module Subject to Wafer Residency Time Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9544-9558页

作者： Huang, Baoying Wu, Naiqi Qiao, Yan Guo, Weiwen Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau Peoples R China Hitachi Bldg Technol Guangzhou Co Ltd Guangzhou 510670 Peoples R China

Semiconductor manufacturing widely employs cluster tools that comprise three critical components: a vacuum module (VM), a loadlock module (LLM), and an equipment front-end module (EFEM). In operating such tools, LLM plays a pivotal role. Additionally, wafer fabrication in VM faces strict wafer residency time constraints, making the coordination among the modules particularly challenging. This paper addresses the cyclic scheduling problem of single-arm cluster tools with EFEM and wafer residency time constraints. It focuses on the tool efficiency in module cooperation and schedulability. We explore cooperative strategies for the robot operating within VM to access loadlocks, elucidating the influence of EFEM and LLM on VM. Based on these strategies, we establish necessary and sufficient conditions for the existence of a feasible periodic schedule, providing a foundational basis for the systematic analysis. For schedulable scenarios, we derive an efficient algorithm to identify the feasible and optimal schedule in terms of tool cycle time. The performance and efficiency of the proposed algorithm are validated through experimental verification. Note to Practitioners-In wafer fabs, single-arm cluster tools with EFEM are widely adopted. Studies that neglect the impact of EFEM on scheduling cluster tools are often inapplicable to real-world scenarios. Wafer residency time constraints are essential for maintaining wafer quality. As VM and EFEM operate in different pressure environments, the cooperation via LLM makes it challenging to meet residency time constraints. To address this problem, this paper analyzes the impact of cooperative strategies on the tool performance, and the influence of EFEM and LLM on VM as well. Under these strategies, we examine the schedulability conditions, helping engineers understand how processing parameters affect scheduling feasibility. If a feasible schedule exists, an efficient algorithm is derived to find an optimal schedule. Experimental

关键词： Optimal scheduling Video recording Job shop scheduling Time factors Robot kinematics clustering algorithms Schedules Loading Transportation Petri nets Semiconductor manufacturing cluster tools scheduling

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Polynomial Phase Signal Denoising via Sparse Representations Over a SMAF-Based Dictionary Learning Algorithm

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IEEE ACCESS 2025年 13卷 38268-38281页

作者： Ou, Guojian Zeng, Chenping Dong, Jiaqiang Han, Die Xichang Univ Coll Informat Technol Xichang 615000 Sichuan Peoples R China Key Lab Sichuan Univ Digital Transformat Liangshan Xichang 615000 Peoples R China

In this paper, we address the problem of denoising polynomial phase signals (PPS) by removing additive white Gaussian noise. Our approach is based on sparse representation using a trained dictionary, which is obtained through the secondary moving average filtering (SMAF) dictionary learning algorithm. The proposed algorithm consists of two main steps: first, we utilize K-SVD or other dictionary learning algorithms to obtain the initial trained dictionary;second, we apply the SMAF method to modify the dictionary atoms, enabling them to behave more smoothly and effectively reducing the noise present in the atomic structures. Consequently, the signal-to-noise ratio (SNR) of the reconstructed signal using sparse representation over the refined dictionary is significantly improved compared to K-SVD and RLS-DLA. To achieve optimal denoising effectiveness, we first estimate the SNR of the PPS using our proposed SNR estimation algorithm. Based on the estimated SNR, we then determine the number of samples for the moving average filter impulse response.

关键词： Dictionaries Machine learning Atoms Signal to noise ratio Noise reduction Filtering Low-pass filters Filtering theory clustering algorithms Noise Polynomial phase signal K-SVD dictionary learning secondary moving average filtering

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Hybrid Raft-PoW Blockchain Consensus Algorithm

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IEEE ACCESS 2025年 13卷 72067-72076页

作者： Ulukok, Mehtap Kose Sariyildiz, Irfan Evrim, Vesile Cyprus Aydin Univ Dept Software Engn TR-99010 Mersin Northern Cyprus Turkiye European Univ Lefke Dept Comp Engn TR-99010 Mersin Northern Cyprus Turkiye

The Raft consensus algorithm is widely used in private networks as an alternative to the energy-intensive PoW consensus algorithm in blockchains. The Raft consensus algorithm's voting mechanism performs well in reliable and well-planned networks with optimized timeouts. However, in unreliable or poorly configured networks, it encounters several challenges. These include multiple candidacies, repeated election cycles, insufficient or failed leader elections during network splits, prolonged leader election times and vulnerability to Sybil attacks. In this study, a novel Hybrid Raft-PoW consensus algorithm is introduced. It integrates the hash puzzle-based competition of the PoW consensus algorithm with the fast leader election mechanism of the Raft consensus algorithm. This combination ensures both speed and certainty in leader election, ensuring that leadership is delegated to the most capable nodes. At the same time, it promotes decentralization by ensuring a fair distribution across nodes, achieving at least 80% leadership distribution. Therefore, the proposed Hybrid Raft-PoW consensus algorithm improves or eliminates problems caused by Raft consensus algorithm's leader election mechanism.

关键词： Voting Consensus algorithm Blockchains Security Leadership Partitioning algorithms Energy consumption Internet of Things clustering algorithms Simulation Blockchain technology consensus algorithms proof of work raft

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Industrial Anomaly Detection System: A Multicase Algorithm Leveraging Feature Information and Memory Bank

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Shi, Yunhan Gao, Bin Yang, Geng Li, Haoran Woo, Wai Lok Univ Elect Sci & Technol China UESTC Sch Automat Engn Chengdu 610056 Peoples R China Northumbria Univ Newcastle Upon Tyne NE1 8ST England

The conventional anomaly detection (AD) methods typically rely on training normal samples without defects to identify deviations from the background. However, these methods suffer from issues, such as missing detection or false detection. Although subsequent research has attempted to improve algorithm performance, this often results in overfitting to specific cases within the AD framework. To address these challenges, this article proposes a semi-supervised AD algorithm that combines improved metric learning techniques with a memory bank (MB) update module. In order to enhance the algorithm's generalization capabilities across different cases, a multicases MB inheritance approach is introduced. This approach facilitates rapid generalization to unknown test cases with minimal iterative learning ( $\le 5$ epochs). Additionally, a bank-case matching module is designed to select the appropriate MB and calculate anomaly scores within our framework. The effectiveness of the proposed algorithm has been validated through real industrial tests and ablation experiments, demonstrating its capability in detecting anomalies accurately and reliably. Code is available on: https://***/FrankCloud-UESTC/Multi-case-AD.

关键词： Vectors Training Feature extraction Measurement Testing Anomaly detection Overfitting Input variables Extraterrestrial measurements clustering algorithms Anomaly detection (AD) defect localization and segmentation metric learning multicase generalization nondestructive testing

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