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Distributed Resilient clustering Algorithm for Virtual Power Plants Under Cyber Attacks

IEEE ACCESS 2025年 13卷 38714-38725页

作者： Wu, Jia Sun, Yifan Qian, Jinyue Cui, Yin Wang, Qian Zhuo, Li Pinghu Power Supply Co State Grid Zhejiang Elect Power Co Ltd Jiaxing 314200 Peoples R China

In this paper, the resilient control issue of virtual power plants (VPPs) under hybrid cyber attacks, specifically focusing on denial-of-service (DoS) and false data injection (FDI) attacks is first addressed. To solve this problem, a novel distributed resilient clustering secondary control technique is developed to ensure the normal operation of the VPP under cyber attacks. Specifically, an FDI attack estimation technique is first introduced to precisely estimate unknown FDI attacks affecting the bulk power supply. Based on the estimator, distributed resilient secondary controllers are designed to effectively combat the effects of FDI attacks and help achieve the frequency and voltage restoration as well as the proportional allocation of power requirement and state-of-charge (SoC) balancing of energy storage systems (ESSs). Under the proposed FDI attack estimation-based cooperative resilient secondary controller, the stability of the closed-loop systems subjected to hybrid FDI and DoS attacks is rigorously analyzed using Lyapunov theory. Finally, several simulation examples are introduced to verify the effectiveness of the proposed method.

关键词： clustering algorithms Learning systems Estimation Cyberattack Voltage control Virtual power plants Power demand Frequency control Economics Stability criteria Resilient secondary control cyber attacks frequency and voltage regulation SoC balancing VPPs

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Gauging-δ: A Non-Parametric Hierarchical clustering Algorithm

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第6期47卷 4897-4907页

作者： Yao, Jinli Pan, Jie Zeng, Yong Concordia Univ Montreal PQ H3G 1M8 Canada Univ Calgary Calgary AB T2N 1N4 Canada

The development of a nonparametric and versatile clustering algorithm has been a longstanding challenge in unsupervised learning due to the exploratory nature of the clustering problem. This study presents a novel algorithm, named Gauging-delta, which can handle diverse cluster shapes and operate in a nonparametric manner. The algorithm employs a hierarchical merging process that starts from individual data points until no further clusters can be merged. The central component of Gauging-delta is the adaptive mergeability function, which progressively determines if two clusters are mergeable considering the perceptual statistics of the clusters and their environment. Empirical evaluations on 105 synthetic datasets demonstrate the superiority of the proposed algorithm, particularly in accurately handling well-separated clusters. Experiments on real-world datasets highlight the impact of selecting appropriate data features and distance metrics on clustering results.

关键词： clustering algorithms Shape Merging Training Synthetic data Standards Noise Electronic mail Couplings Complexity theory Hierarchical clustering non-parametric algorithm cluster shapes

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A Hollow-Core Anti-Resonant Fiber Multi-Objective Multi-Modal Optimization Assisted by a Proxy Model Combined With clustering Algorithm

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第2期43卷 815-823页

作者： Liu, Zihan Dong, Shiyuan Chen, Rongliang Zhou, Zhengyong Ren, Zengle Liu, Huanhuan Dong, Yuming Yang, Tianyu Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Shanxi Normal Univ Sch Math & Comp Sci Xian 030000 Shanxi Peoples R China

To address the optimization problem of expensive multi-modal multi-objective black-box functions, this paper proposes a systematic optimization method based on a back propagation neural network as a proxy model, combined with clustering algorithm to cluster and differentiate multiple modes within the model. The method utilizes normalization and weighted summation of numerical experimental data to optimize the hyperparameters of the optimization algorithm's decision-making process. This approach resolves the multiple constraints of traditional optimization methods. Finally, applied to the hollow-core anti-resonant fiber with gap angle constraints to ensure its topological properties remain unchanged, the model optimizes three categories totaling six modes, optimizing the objectives of birefringence and loss. The top five optimal models achieve a minimum loss of 3.54x10(-3) dB/m, maximum birefringence of 1.25x10(-4), higher order modulation enhanced receiver of 50, and bandwidth of 1.425 mu m.

关键词： Optimization Optical fiber networks Neural networks Optical fiber polarization Computational modeling clustering algorithms Closed box High birefringence hollow-core anti-resonant fibers low loss

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Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第5期47卷 3611-3624页

作者： Xu, Ruiyu Wang, Chao Li, Yongxiang Wu, Jianguo Peking Univ Dept Ind Engn & Management Beijing 100871 Peoples R China Univ Iowa Dept Ind & Syst Engn Iowa City IA 52242 USA Shanghai Jiao Tong Univ Dept Ind Engn & Management Shanghai 200240 Peoples R China

Dictionary learning is an effective tool for pattern recognition and classification of time series data. However, real-world time series data often exhibit temporal misalignment due to temporal delay, scaling or other temporal transformations, which poses significant challenges for effective dictionary learning. Dynamic time warping (DTW) is commonly used for dealing with such misalignment issues. Nevertheless, the DTW suffers overfitting or information loss due to its discrete nature in aligning time series data. To address this issue, we propose a generalized time warping invariant dictionary learning algorithm in this paper. Our approach features a generalized time warping operator, which consists of linear combinations of continuous basis functions for facilitating continuous temporal warping. The integration of the proposed operator and the dictionary learning is formulated as an optimization problem, where the block coordinate descent method is employed to jointly optimize warping paths, dictionaries, and sparse coefficients. The optimized results are then used as hyperspace distance measures to feed classification and clustering algorithms. The superiority of the proposed method in terms of dictionary learning, classification, and clustering is validated through ten sets of public datasets in comparison with various benchmark methods.

关键词： Time series analysis Machine learning Dictionaries clustering algorithms Optimization Atoms Kernel Accuracy Feature extraction Heuristic algorithms Time warping dictionary learning time series classification time series clustering

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Scale-Driven Tensor Representation-Based Multiview clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年

作者： Zhang, Chuanbin Chen, Long Ding, Weiping Zhao, Kai Shi, Zhaoyin Wang, Yingxu Chen, C. L. Philip Zhaoqing Univ Sch Comp Sci & Software Zhaoqing 526061 Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Taipa Macau Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China City Univ Macau Fac Data Sci Taipa Macau Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Real-world data tends to exhibit an inherent hierarchical structure, providing a natural multiview perspective where features at different scales can be treated as distinct views. However, most existing multiview clustering algorithms primarily focus on the inter-sample relationships at a single level. These methods overlook the hierarchical structures present in the data and are specifically designed for native multiview data. This article introduces a comprehensive multiview clustering framework that transforms both typical data and images into a unified multiview feature representation. The framework allows for extracting multiscale features from the raw data and clustering different types of data with the same algorithm. A novel scale-driven pre-processing approach unifies the feature structure across various data types and explores local relationships among samples at multiple scales. Features at larger scales delineate the global cluster contours, while features at smaller scales reveal fine-grained local details. Subsequently, the proposed method learns the view-specific partitions from different scales of views and derives consensus features through tensor low-rank representation. By optimizing these consensus features, the approach effectively captures the precise cluster shapes from coarse to fine-grained levels. The final label indicator matrix is directly obtained from these consensus features. To demonstrate the effectiveness and versatility of the proposed method, we conducted experimental comparisons with state-of-the-art (SOTA) algorithms in both multiview clustering and image segmentation across diverse datasets. The source code and datasets are released at https://***/ChuanbinZhang/SDTR

关键词： Tensors clustering algorithms Feature extraction Image segmentation Data mining Transforms Technological innovation Matrix decomposition Computer science Urban areas Low-rank tensor multiview clustering scale-driven unsupervised learning

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Enhancing Segmentation: A Comparative Study of clustering Methods

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IEEE ACCESS 2025年 13卷 47418-47439页

作者： Ling, Lew Sook Weiling, Claireta Tang Multimedia Univ Fac Informat Sci & Technol Melaka 75450 Malaysia

With the increasing complexity of consumer preferences and behaviors, businesses face challenges to capture the dynamic nature of online consumer behavior, highlighting the need for advanced approaches. This study aims to enhance customer segmentation in e-marketing by analyzing and comparing various machine learning-based clustering methods, with a particular focus on unsupervised clustering techniques for predicting Customer Lifetime Value (CLV). While prior research has utilized unsupervised clustering for customer segmentation, this current study uniquely integrates K-Means++ with other clustering techniques to enhance segmentation accuracy and gain deeper insights into consumer behavior. This study adopts a structured, unsupervised clustering approach, enabling natural customer groupings without predefined labels, which is particularly suitable for customer segmentation in scenarios with limited labeled data. Several clustering techniques are investigated, including K-Means, K-Medoids, Agglomerative clustering, DBSCAN, Fuzzy C-Means, K-Means++, Mini Batch K-Means, Mean Shift, and Gaussian Mixture Models (GMM). K-Means++ demonstrated superior performance in segmentation accuracy, outperforming other techniques under various conditions. Performance is evaluated using key metrics such as the Silhouette Score and Davies-Bouldin Index. Utilizing Kaggle datasets, the analysis follows a comprehensive preprocessing protocol comprising RFM (Recency, Frequency, Monetary) analysis, outlier removal, and data normalization to ensure data integrity and facilitate systematic identification of distinct consumer segments. This research highlights the potential and significance of machine learning in refining customer segmentation processes within e-marketing, ultimately aiding businesses in optimizing their marketing effectiveness and strategic planning. While focusing primarily on a limited selection of clustering methods, the study underscores the necessity for ongoing explor

关键词： Business clustering methods Companies Consumer behavior Accuracy Systematic literature review Image segmentation clustering algorithms Internet Indexes Customer segmentation clustering RFM K-Means plus plus K-Medoids CLV prediction

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AoI-Minimal clustering, Transmission and Trajectory Co-Design for UAV-Assisted WPCNs

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 1035-1051页

作者： Liu, Xiaoying Liu, Huihui Zheng, Kechen Liu, Jia Taleb, Tarik Shiratori, Norio ZheJiang Univ Technol Sch Comp Sci & Technol Hangzhou 310014 Zhejiang Peoples R China Natl Inst Informat Ctr Strateg Cyber Resilience Res & Dev Tokyo 1018430 Japan Ruhr Univ Bochum Fac Elect Engn & Informat Technol D-44801 Bochum Germany Chuo Univ Res & Dev Initiat Tokyo 1128551 Japan

This paper investigates the long-term average age of information (AoI)-minimal problem in an unmanned aerial vehicle (UAV)-assisted wireless-powered communication network (WPCN), which consists of a static hybrid access point (HAP), a mobile UAV, and many static sensor nodes (SNs) randomly distributed on multiple islands. The UAV first is fully charged by the HAP, and then flies to each island to charge SNs and receive data from them. Before running out the energy in battery, the UAV flies back to the HAP to offload the received data and be fully charged again. Due to the finite battery capacity of the UAV, it is impossible for the UAV to traverse all the islands to collect all the data from SNs for once flight. We are thus inspired to divide islands into multiple clusters so that the UAV could traverse all the islands in each cluster, and formulate the long-term average AoI-minimal problem by jointly optimizing the transmit power of SNs, clustering of islands, and UAV's flight trajectory. To tackle the NP-hard problem, we decouple it into two subproblems: the power allocation subproblem for SNs, and the joint clustering of islands and UAV's flight trajectory design subproblem. To solve the first subproblem, we propose a hybrid TDMA and NOMA (HTN) protocol that takes advantage of the two protocols. To solve the second subproblem, we propose a clustering-based dynamic adjustment of the shortest path (C-DASP) algorithm. Simulations verify the effectiveness and superiority of the proposed HTN protocol and C-DASP algorithm.

关键词： Autonomous aerial vehicles Trajectory Heuristic algorithms Batteries clustering algorithms Protocols Optimization Unmanned aerial vehicle wireless powered communication networks age of information trajectory design

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Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy clustering Algorithm

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IEEE ACCESS 2025年 13卷 15572-15584页

作者： Bian, Hongya Li, Deqing Liu, Yuang Ma, Rong Zeng, Wenyi Xu, Zeshui Xiamen Inst Technol Sch Data Sci & Intelligent Engn Xiamen 361021 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

The aim of this paper is to investigate the multiple criteria group decision-making problem in which the evaluation values provided by experts construct some hesitant fuzzy numbers. We first analyze the characteristics of hesitant fuzzy number (HFN) and define the concept of feature vector of HFN. Then, applying the feature vectors of HFNs, some new similarity measures of HFNs are presented, which do not need to add elements to the HFN with fewer elements in the calculation process. Therefore, fuzzy similarity matrix is constructed, which is used to obtain a fuzzy equivalent matrix by using transitive closure method. By applying the fuzzy equivalent matrix, a novel hesitant fuzzy clustering algorithm is given. Furthermore, a new multiple criteria group decision making (MCGDM) algorithm is developed on the basis of the hesitant fuzzy clustering algorithm and the idea of ideal solution in multiple criteria decision making theory. To illustrate the effectiveness and feasibility of the developed MCGDM method, a numerical example is given and analyzed in detail. The results illustrate that the proposed method can provide more reasonable and credible rankings comparing with existing methods owing to keeping original data during computation.

关键词： Fuzzy sets Vectors clustering algorithms Hafnium Fuzzy set theory Probabilistic logic Q measurement MCDM Linguistics Urban areas Hesitant fuzzy number feature vector of HFN hesitant fuzzy clustering algorithm multiple criteria group decision making

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Adaptive Non-Homogeneous Granulation-Aided Density-Based Deep Feature clustering for Far Infrared Sign Language Images

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1269-1280页

作者： Paral, Pritam Ghosh, Saibal Pal, Sankar K. Chatterjee, Amitava IIEST Dept Elect Engn Sibpur 711103 Howrah India Jadavpur Univ Dept Elect Engn Kolkata 700032 India ISI Ctr Soft Comp Res Kolkata 700108 India

In image clustering applications, deep feature clustering has recently demonstrated impressive performance, which employs deep neural networks for feature learning that favors clustering exercises. In this context, density-based methods have emerged as the preferred choice for the clustering mechanism within the framework of deep feature clustering. However, as the performance of these clustering algorithms is primarily effective on the low-dimensional feature data, deep feature learning models play a crucial role here. With far infrared (FIR) thermal imaging systems working in real-world scenarios, the images captured are largely affected by blurred edges, background noise, thermal irregularities, few details, etc. In this work, we demonstrate the effectiveness of granular computing-based techniques in such scenarios, where the input data contains indiscernible image regions and vague boundary regions. We propose a novel adaptive non-homogeneous granulation (ANHG) technique here that can adaptively select the smallest possible size of granules within a purview of unequally-sized granulation, based on a segmentation assessment index. Proposed ANHG in combination with deep feature learning helps in extracting complex, indiscernible information from the image data and capturing the local intensity variation of the data. Experimental results show significant performance improvement of the density-based deep feature clustering method after the incorporation of the proposed granulation scheme.

关键词： Image segmentation Feature extraction Finite impulse response filters Cameras Sensors clustering algorithms Sign language Indexes Granular computing Temperature sensors Adaptive non-homogeneous granulation American sign language deep feature clustering density-based clustering far infrared thermal imaging granular computing

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Graph Learning With Riemannian Optimization for Multi-View Integrative clustering

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 381-393页

作者： Khan, Aparajita Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 India Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India

Real-world multi-view data may manifest as point-clouds, but their meaningful structure often resides on a lower dimensional manifold embedded in the higher dimensional space. Consequently, existing graph based multi-view algorithms focus primarily on extraction of the low-rank subspaces for clustering. However, simultaneous optimization of the individual graph structures, their contributions/weights along with the clustering subspaces is likely to give a more comprehensive idea of the clusters present in the data set. In this regard, the paper proposes a Riemannian manifold optimization algorithm that harnesses the geometry and structure preserving properties of symmetric positive definite (SPD) manifold and Grassmann manifold for efficient multi-view clustering. The SPD manifold is used to optimize the graph Laplacians corresponding to the individual views, while preserving their symmetricity, positive definiteness, and related properties. The Grassmann manifold, on the other hand, is used to minimize the disagreement between the joint and individual clustering subspaces. Optimization over the Grassmann manifold additionally enforces the clustering solutions to be basis invariant such that all cluster indicator matrices whose columns span the same subspace map to the same clustering solution. A gradient based line-search algorithm, which alternates between two different manifolds, is proposed to optimize the multi-view graph Laplacians and its associated subspaces. The matrix perturbation theory is used to theoretically bound the disagreement between the clustering subspaces. Extensive experiments on several multi-view benchmark and multi-omics cancer data sets establish the effectiveness of the proposed method. The proposed work demonstrates how incorporation of Riemannanian optimization into the graph learning framework can boost multi-view clustering performance.

关键词： Manifolds Optimization Laplace equations clustering algorithms Noise Geometry Perturbation methods Graph learning matrix perturbation theory multi-view clustering Riemannian optimization spectral clustering

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