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Leakage-Based Precoder Optimization and Deep Unfolding for User-Centric Cell-Free Massive MIMO

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第5期74卷 8369-8373页

作者： Xu, Chunmei Zhang, Cheng Huang, Yongming Wu, Yi Lu, Zhaohua Southeast Univ Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Univ Surrey Inst Commun Syst 6GIC Guildford GU2 7XH England Purple Mt Labs Nanjing 211111 Peoples R China Fujian Normal Univ Fujian Prov Engn Technol Res Ctr Photoelect Sensin Key Lab Optoelect Sci & Technol Med Minist Educ Fuzhou 350007 Peoples R China ZTE Corp Shenzhen 518057 Peoples R China State Key Lab Mobile Network & Mobile Multimedia T Shenzhen 518057 Peoples R China

This article investigates the leakage-based precoder design for user-centric cell-free massive MIMO systems. Given the clustered APs for each UE, the problem is formulated as a group sparse optimization to maximize the leakage rate under per-AP power constraints. Its equivalent formulation is derived by introducing two auxiliary variables. An iterative optimization method (IOM) is proposed based on block coordinate descent and bisection search techniques, which converges to a stationary solution. To reduce the required computations, a deep unfolding-based method (DUBM) is developed to unfold the iterative procedure of IOM via the proposed neural network layer. Simulation results demonstrate the competitive performance of the proposed methods, with IOM achieving higher sum-rates while DUBM providing higher computational efficiency compared to the reference methods.

关键词： Interference Massive MIMO Iterative methods Signal to noise ratio clustering algorithms Measurement Vectors Resource management Receivers Optimization methods Cell-free deep unfolding optimization precoder SLNR

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A Fault Diagnosis Method for PV Arrays Based on New Feature Extraction and Improved the Fuzzy C-Mean clustering

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IEEE JOURNAL OF PHOTOVOLTAICS 2022年第3期12卷 833-843页

作者： Xu, Liuchao Pan, Zhiheng Liang, Chuandong Lu, Min Shihezi Univ Shihezi Peoples R China

Photovoltaic (PV) array fault diagnosis is vital for the safe and stable operation of PV systems. Up to now, there are many methods to diagnose and classify PV array faults successfully. However, the difficulty of fault diagnosis is increased because the PV arrays have nonlinear output characteristics and complex working environments. In practice, the difference in characteristic parameters of different faults is not apparent, and it is not easy to effectively obtain labels of many samples. In order to address the above problems, this article proposes a new fault detection method for PV arrays based on the output current-voltage (I-V) characteristic, an improved fuzzy C-mean clustering (FCM) algorithm to identify four common PV array faults. The measured I-V characteristic curves are used to extract the initial feature parameters and then calculate the initial parameters to obtain characteristic parameters. In addition, it used characteristic parameters as feature variables of the FCM fault diagnosis model. Finally, classification results are verified by inner cluster maximum mean discrepancy and reclassification pseudoparameter based on the relationship between characteristic parameters. This method defines new characteristic parameters and achieves the purpose of fault detection by reclassification;in addition, we verify the high accuracy and simplicity of the method through simulation and experiment.

关键词： Circuit faults clustering algorithms Integrated circuit modeling Fault diagnosis Degradation Resistance Feature extraction Fault diagnosis fuzzy C-mean clustering (FCM) algorithm ICMMD photovoltaic (PV) array

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Rank Consistency Induced Multiview Subspace clustering via Low-Rank Matrix Factorization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第7期33卷 3157-3170页

作者： Guo, Jipeng Sun, Yanfeng Gao, Junbin Hu, Yongli Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Inst Artificial Intelligence Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Univ Sydney Business Sch Discipline Business Analyt Camperdown NSW 2006 Australia

Multiview subspace clustering has been demonstrated to achieve excellent performance in practice by exploiting multiview complementary information. One of the strategies used in most existing methods is to learn a shared self-expressiveness coefficient matrix for all the view data. Different from such a strategy, this article proposes a rank consistency induced multiview subspace clustering model to pursue a consistent low-rank structure among view-specific self-expressiveness coefficient matrices. To facilitate a practical model, we parameterize the low-rank structure on all self-expressiveness coefficient matrices through the tri-factorization along with orthogonal constraints. This specification ensures that self-expressiveness coefficient matrices of different views have the same rank to effectively promote structural consistency across multiviews. Such a model can learn a consistent subspace structure and fully exploit the complementary information from the view-specific self-expressiveness coefficient matrices, simultaneously. The proposed model is formulated as a nonconvex optimization problem. An efficient optimization algorithm with guaranteed convergence under mild conditions is proposed. Extensive experiments on several benchmark databases demonstrate the advantage of the proposed model over the state-of-the-art multiview clustering approaches.

关键词： Sparse matrices Optimization clustering algorithms clustering methods Tensors Matrix decomposition Learning systems Complementary information low-rank representation (LRR) low-rank matrix factorization multiview subspace clustering rank consistency

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Euclidean Distance is Not Your Swiss Army Knife

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 8179-8191页

作者： Tan, Yuze Liu, Yixi Wu, Hongjie Huang, Shudong Xu, Zenglin Tsang, Ivor W. Lv, Jiancheng Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518057 Peoples R China ASTAR Ctr Frontier AI Res Singapore 138632 Singapore

Graph-based multi-view learning, which has hitherto been used to discover the intrinsic patterns of graph data giving the credit to its convenience of implementation and effectiveness. Note that even though these approaches have been increasingly adopted in multi-view clustering and have generated promising outcomes, they are still faced with the sub-optimal solution. For one thing, multi-view data can be corrupted in the raw feature space. For the other, most existing approaches normally utilize euclidean distance to obtain the similarity between two samples, which can not be the best option for all types of real-world data and leads to inferior results. Therefore, to overcome the aforementioned issues, we integrate multi-metric learning, graph filtering, and subspace learning into a collaborative learning framework for multi-view clustering. Particularly, we prefer to recover a smooth representation of data by graph filtering, which can reserve the geometric structure of the original multi-view data and discard the corruptions simultaneously. Furthermore, instead of using euclidean distance as a Swiss army knife, multiple metrics are utilized to fully exploit the correlation of data based on the smooth representation, hence finally facilitating the downstream clustering task. Extensive experiments on multi-view clustering tasks validate our theoretical findings of ours and prove the improvement of our method over the SOTA approaches.

关键词： Measurement clustering algorithms Euclidean distance Task analysis Filtering Termination of employment clustering methods clustering graph filtering metric learning multi-view learning

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Strategic Node Deployment Scheme for Maximizing Coverage Area and Network Lifetime in UASNs Using Voronoi-Fuzzy C-Means and Salp Swarm Optimization

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IEEE SENSORS JOURNAL 2024年第10期24卷 16926-16934页

作者： Sunil Kumar, Kammula Singh, Deepak Anand, Veena NIT Raipur Dept Comp Sci & Engn Raipur 492010 India ABV IIITM Dept Informat Technol Gwalior 474015 India

The strategic deployment of nodes in underwater acoustic sensor networks (UASNs) is pivotal for enabling critical network functions such as localization and efficient monitoring. Achieving optimal deployment necessitates balancing two vital parameters: area coverage and network lifetime. Improving one parameter in isolation may adversely affect the other, emphasizing the need for joint optimization of area coverage and energy utilization to enhance overall UASN efficiency. This article introduces a novel node deployment scheme leveraging Voronoi-fuzzy C-means (FCM) and salp swarm optimization (SSO) to address challenges associated with random node deployment, such as uneven distribution, coverage overlaps, and holes. First, FCM computes initial centroids for optimal node placement. Subsequently, Voronoi polygons around these centroids address issues of spatial overlaps and indistinct boundaries. Additionally, Voronoi facilitates the determination of an optimum sensing radius. SSO refines node locations within each polygon by converging the nodes toward centroids. Furthermore, a sleep-wake mechanism for sensor nodes further extends the network lifetime. Performance evaluation reveals that our proposed scheme outperforms existing methods in diverse network scenarios. The proposed method achieved a remarkable 99.31% area coverage, extended network lifetime, and reduced energy consumption across diverse scenarios, all accomplished with a high convergence rate.

关键词： Sensors Monitoring Energy consumption clustering algorithms Wireless sensor networks Particle swarm optimization Heuristic algorithms Coverage holes node deployment salp swarm optimization (SSO) underwater acoustic sensor networks (UASNs) Voronoi polygons

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Clustered Federated Learning in Heterogeneous Environment

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第9期35卷 12796-12809页

作者： Yan, Yihan Tong, Xiaojun Wang, Shen Harbin Inst Technol Dept Comp Sci Harbin 150001 Peoples R China

Federated learning (FL) is a distributed machine learning framework that allows resource-constrained clients to train a global model jointly without compromising data privacy. Although FL is widely adopted, high degrees of systems and statistical heterogeneity are still two main challenges, which leads to potential divergence and nonconvergence. Clustered FL handles the problem of statistical heterogeneity straightly by discovering the geometric structure of clients with various data generation distributions and getting multiple global models. The number of clusters contains prior knowledge about the clustering structure and has a significant impact on the performance of clustered FL methods. Existing clustered FL methods are inadequate for adaptively inferring the optimal number of clusters in environments with high systems' heterogeneity. To address this issue, we propose an iterative clustered FL (ICFL) framework in which the server dynamically discovers the clustering structure by successively performing incremental clustering and clustering in one iteration. We focus on the average connectivity within each cluster and give incremental clustering and clustering methods that are compatible with ICFL based on mathematical analysis. We evaluate ICFL in experiments on high degrees of systems and statistical heterogeneity, multiple datasets, and convex and nonconvex objectives. Experimental results verify our theoretical analysis and show that ICFL outperforms several clustered FL baseline methods.

关键词： Servers Data models Training Convergence clustering methods clustering algorithms Optimization clustering deep learning distributed learning federated learning (FL) multitask learning

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An Empirical Taxonomy of Video Summarization Model From a Statistical Perspective

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IEEE ACCESS 2024年 12卷 173850-173866页

作者： Babu Veesam, Sai Satish, Aravapalli Rama VIT AP Univ Sch Comp Sci & Engn Amaravati 522241 India

This paper has thrown out a comprehensive taxonomic view of all different video summarization techniques that show variously adopted methodologies efficiently condensing large video data. The review categorizes and evaluates techniques against their core approaches: clustering-based methods, deep learning frameworks, and multimodal integration strategies. Some of the notable techniques include the SVS_MCO method using DBSCAN clustering optimized by the Artificial Algae Algorithm and the KDAN framework, which leverages knowledge distillation for supervised summarization. The review also gives advanced models like the Query-based Deep African Vulture Learning and the Audio-Visual Recurrent Network, which are observed to be very helpful in handling dynamic and multimodal video summarization tasks. Performance of such techniques benchmarks against datasets like SumMe, TVSum, and other custom datasets and has shown considerable improvement in precision, recall, and F1-scores. In the final analysis, this paper identifies some of the best models that apply in domains such as multimedia, surveillance, and user-generated content and finally discusses improvements in adaptability and efficiency of video summarization technologies in the future. Results show a trend that reinforcement learning combined with attention mechanisms and self-supervised learning will provide robust, scalable summarization solutions.

关键词： Feature extraction clustering algorithms Optimization Complexity theory Deep learning Benchmark testing Accuracy Visualization Reviews Physiology Analysis deep learning models scenarios summarization taxonomical video

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Research on Anomaly Identification of PV Power Station Operation Data Based on Fuzzy Association Rules

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IEEE ACCESS 2024年 12卷 156662-156672页

作者： Tang, Ren Zhu, Weijun He, Yongling Zhejiang Huayun Elect Power Engn Design Consulting Dept Nephrol Hangzhou 310000 Zhejiang Peoples R China

Aiming at the problem that the operating data of photovoltaic power station is characterized by high suddenness and irregularity, which affects the accuracy of abnormal identification of operating data of photovoltaic power station, the method of abnormal identification of operating data of photovoltaic power station based on fuzzy association rules is studied. Collect the operation data of photovoltaic power station, use the fuzzy C-means clustering algorithm to process the operation data of photovoltaic power station, and build a fuzzy attribute set. The association rule algorithm is used to mine the association rules in the fuzzy attribute set of the operation data of photovoltaic power station, and form the strong association rules for abnormal identification of the operation data of photovoltaic power station;The multi mutation particle swarm optimization algorithm is used to optimize the association rule algorithm, and the optimized fuzzy association rules are used to output the abnormal identification results of the operation data of the photovoltaic power station. The experimental results show that the method can accurately identify the abnormal data in the operation data of photovoltaic power station, and the identification accuracy is higher than 98%.

关键词： Photovoltaic systems Association rule learning clustering algorithms Data mining Safety Optimization Temperature measurement Solar radiation Monitoring Databases Anomaly detection Fuzzy systems Particle swarm optimization Anomaly identification fuzzy association rules fuzzy attribute set operation data particle swarm PV power station

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An Accurate and Efficient Large-Scale Regression Method Through Best Friend clustering

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第11期33卷 3129-3140页

作者： Li, Kun Yuan, Liang Zhang, Yunquan Chen, Gongwei Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing 100190 Peoples R China Univ Chinese Acad Sci UCAS Sch Comp Sci & Technol Beijing 100190 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China

As the data size in Machine Learning fields grows exponentially, it is inevitable to accelerate the computation by utilizing the ever-growing large number of available cores provided by high-performance computing hardware. However, existing parallel methods for clustering or regression often suffer from problems of low accuracy, slow convergence, and complex hyperparameter-tuning. Furthermore, the parallel efficiency is usually difficult to improve while striking a balance between preserving model properties and partitioning computing workloads on distributed systems. In this article, we propose a novel and simple data structure capturing the most important information among data samples. It has several advantageous properties supporting a hierarchical clustering strategy that contains well-defined metrics for determining optimal hierarchy, balanced partition for maintaining the clustering property, and efficient parallelization for accelerating computation phases. Then we combine the clustering with regression techniques as a parallel library and utilize a hybrid structure of data and model parallelism to make predictions. Experiments illustrate that our library obtains remarkable performance on convergence, accuracy, and scalability.

关键词： clustering algorithms Training Mathematical models Computational modeling Libraries Kernel Support vector machines Distributed machine learning scalable algorithm large-scale clustering parallel regression

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Trading Off Scalability, Privacy, and Performance in Data Synthesis

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IEEE ACCESS 2024年 12卷 26642-26654页

作者： Ling, Xiao Menzies, Tim Hazard, Christopher Shu, Jack Beel, Jacob North Carolina State Univ Comp Sci Dept Raleigh NC 27695 USA Howso Raleigh NC 27603 USA

Synthetic data has been widely applied in the real world recently. One typical example is the creation of synthetic data for privacy concerned datasets. In this scenario, synthetic data substitute the real data which contains the privacy information, and is used to public testing for machine learning models. Another typical example is the unbalance data over-sampling which the synthetic data is generated in the region of minority samples to balance the positive and negative ratio when training the machine learning models. In this study, we concentrate on the first example, and introduce (a) the Howso engine, and (b) our proposed random projection based synthetic data generation framework. We evaluate these two algorithms on the aspects of privacy preservation and accuracy, and compare them to the two state-of-the-art synthetic data generation algorithms DataSynthesizer and Synthetic Data Vault. We show that the synthetic data generated by Howso engine has good privacy and accuracy, which results in the best overall score. On the other hand, our proposed random projection based framework can generate synthetic data with highest accuracy score, and has the fastest scalability.

关键词： Synthetic data clustering algorithms Data models Engines Biomedical imaging Generative adversarial networks Data privacy Regression analysis Classification algorithms Scalability Homomorphic encryption Synthetic data generation privacy preservation regression classification

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