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OAM Spatial Field Digital Modulation System for Physical-Level Secure Communication

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 18472-18486页

作者： Chen, Yuqi Xiong, Xiaowen Zheng, Shilie Yang, Zhaohui Zhu, Zelin Pan, Bingchen Wu, Bincai Hui, Xiaonan Jin, Xiaofeng Yu, Xianbin Zhang, Xianmin Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China NingboTech Univ Sch Informat Sci & Engn Ningbo 315100 Peoples R China

In this paper, we investigate a novel physical layer secure (PLS) potential technique which is summarized into a category of index modulation (IM) scheme, namely the spatial field digital modulation (SFDM). In an SFDM communication system, bit streams are modulated in the spatial distribution of electromagnetic (EM) field. Orbital angular momentum (OAM) mode groups can be utilized for beamforming and thus realize an OAM based SFDM (OAM-SFDM) system. This kind of IM systems that utilize the distribution of EM field to transmit information can radiate different signals to different directions and possess inherent anti-eavesdropping capability. Since an eavesdropper lacks prior knowledge, it is difficult to construct appropriate and effective estimators for signal demodulation. However, such system is vulnerable when potential eavesdroppers adopt clustering algorithm, a type of non-realtime demodulation. We propose two PLS strategies with different computational complexity, the power hopping (PH) method and the symbol variation (SV) method, to confront single-antenna eavesdroppers and multi-antenna eavesdroppers, respectively. Both approaches are key-free, and do not require any prior channel state information (CSI) about eavesdroppers. The mentioned methods could constantly disrupt the statistical properties of eavesdroppers' channels and even their received signals. Theoretical analysis and numerical simulations have been conducted to validate the feasibility of these two schemes. Furthermore, a prototype of OAM-SFDM based PLS communication system is built in a realistic scenario. The experimental results demonstrate that both PH method and SV method can effectively resist clustering algorithm in their corresponding application scenarios.

关键词： Symbols Receiving antennas Demodulation Wireless communication Antenna arrays Indexes Transmitting antennas clustering algorithms Array signal processing Prototypes Orbital angular momentum spatial field digital modulation physical level security index modulation

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Feature Discrimination of News Based on Canopy and KMGC-Search clustering

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IEEE ACCESS 2022年 10卷 26307-26319页

作者： Shahroz, Mobeen Mushtaq, Muhammad Faheem Majeed, Rizwan Samad, Ali Mushtaq, Zaigham Akram, Urooj Islamia Univ Bahawalpur Dept Artificial Intelligence Bahawalpur 63100 Punjab Pakistan Islamia Univ Bahawalpur Directorate Informat Technol Bahawalpur 63100 Punjab Pakistan Islamia Univ Bahawalpur Dept Data Sci Bahawalpur 63100 Punjab Pakistan Islamia Univ Bahawalpur Dept Comp Sci Bahawalpur 63100 Punjab Pakistan

The internet provides a very vast amount of sources of news and the user has to search for desirable news by spending a lot of time because the user always prefers their related interest, desirable and informative news. The clustering of the news article having a great impact on the preferences of the user. The unsupervised learning techniques such that K-means clustering and Spectral clustering are proposed to categorize the news articles by extracting discriminant features that help the user to search and get informative news without wasting time. The BBC news articles dataset is used to perform experiments that consist of 2225 news articles. The TF-IDF feature extraction technique is used with K-means clustering and Spectral clustering to get the most similar clusters to categorize the news articles in respective domains. Those domains are sports, tech, entertainment, politics, and business. The clustering algorithms are evaluated using adjusted rand index, V-measure, homogeneity score, completeness score, and Fowlkes mallows score. The experimental results illustrated that K-means clustering performs better than spectral clustering using the TF-IDF feature extraction approach. But to improve the results the canopy centroid selection is used with the grid search optimization technique to optimize the results of the Kmeans and named its as a K-Means using Grid Search based on Canopy (KMGC-Search). The experimental results shows the proposed approach can be used as a viable method for the categorization of news articles.

关键词： Feature extraction clustering algorithms Machine learning Training Text mining Search engines Companies News categorization K-means clustering canopy machine learning TF-IDF grid search

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Efficient and Robust MultiView clustering With Anchor Graph Regularization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第9期32卷 6200-6213页

作者： Yang, Ben Zhang, Xuetao Lin, Zhiping Nie, Feiping Chen, Badong Wang, Fei Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Xi An Jiao Tong Univ Coll Artificial Intelligence Xian 710049 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Multi-view clustering has received widespread attention owing to its effectiveness by integrating multi-view data appropriately, but traditional algorithms have limited applicability to large-scale real-world data due to their high computational complexity and low robustness. Focusing on the aforementioned issues, we propose an efficient and robust multi-view clustering algorithm with anchor graph regularization (ERMC-AGR). In this work, a novel anchor graph regularization (ARG) is designed to improve the quality of the learned embedded anchor graph (EAG), and the obtained EAG is decomposed by nonnegative matrix factorization (NMF) under correntropy criterion to acquire clustering results directly. Different from the traditional graph regularization that needs to construct a large-scale Laplacian matrix pertaining to the all-sample graph, our lightweight AGR, constructed from the perspective of anchors, can reduce the computational complexity significantly while improving the EAG quality. Moreover, a factor matrix of NMF is constrained to be the cluster indicator matrix to omit additional k-means after optimization. Subsequently, correntropy is utilized to improve the effectiveness and robustness of ERMC-AGR owing to its promising performance to complex noises and outliers. Extensive experiments on real-world datasets and noisy datasets show that ERMC-ARG can improve the clustering efficiency and robustness while ensuring comparable or even better effectiveness.

关键词： Robustness clustering methods clustering algorithms Matrix decomposition Computational complexity Standards Optimization methods Multi-view clustering anchor graph regularization correntropy nonnegative matrix factorization

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Accurate Stock Price Forecasting Based on Deep Learning and Hierarchical Frequency Decomposition

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IEEE ACCESS 2024年 12卷 49878-49894页

作者： Li, Yi Chen, Lei Sun, Cuiping Liu, Guoxu Chen, Chunlei Zhang, Yonghui Weifang Univ Sch Econ & Management Weifang 261061 Peoples R China Weifang Univ Sci & Technol Sch Econ & Management Weifang 262700 Peoples R China Weifang Univ Sch Comp Engn Weifang 261061 Peoples R China

The stock market is playing an increasingly important role in the global economy. Accurate stock price forecasting not only aids the government in predicting economic trends but also helps investors anticipate higher expected returns. Nevertheless, hurdles such as nonlinearity, complexity and high volatility make it a daunting task to predict stock prices. To address this issue, this study proposes a new hybrid model, termed Hierarchical Decomposition-based Forecasting Model (HDFM), to decompose and forecast stock prices in a hierarchical fashion. The model utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for the initial decomposition of stock price time series. To enhance the predictive efficiency, sub-series with similar sample entropy from the decomposition were combined using the K-means clustering method. Through a thorough analysis, it was found that the first combined sub-series contained more high-frequency signals. Therefore, the first combined sub-series is subjected to a second decomposition with variational mode decomposition (VMD). Subsequently, the gated recurrent unit (GRU) model was used to individually predict each sub-series. The final results were obtained by merging the prediction outcomes. The proposed model was evaluated on three different stock markets. The experimental results show that the proposed model outperforms other forecasting methods across all stock indices. Moreover, ablation studies demonstrated the effectiveness of each component within the proposed model.

关键词： Predictive models Hidden Markov models Forecasting Autoregressive processes Deep learning Time series analysis Adaptation models Stock markets Hierarchical systems clustering algorithms Stock price prediction deep learning hierarchical decomposition clustering

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GCN-Based Encoder-Decoder Networks With Markov Random Field for Unsupervised Community Detection

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IEEE ACCESS 2024年 12卷 142141-142154页

作者： Sun, Fan Huang, Jiashuang Guo, Chang Shi, Zhenquan Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Nantong Univ Sch Sci Nantong 226019 Peoples R China Nantong Univ Sch Transportat & Civil Engn Nantong 226019 Peoples R China

Community discovery is an essential research area with significant real-world applications. Lately, Graph Convolutional Networks (GCNs) have gained popularity for their ability to effectively identify and represent node relationships in low-dimensional spaces, enhancing community detection. However, previous studies have ignored two problems: insufficient network node labeling and incomplete information capture of similar dependencies between nodes, which affect the understanding and analysis of the network and the accuracy of node embedding. To this end, this paper proposed GCN-based encoder-decoder networks with Markov random field for unsupervised community detection. Specifically, we propose a comprehensive framework for enhancing the representation of learning nodes, which consists of the following components: a GCN-based encoder-decoder module, an MRF-based information enhancement module, and a modularity maximization module. The GCN-based encoder-decoder module uses graph attention autoencoder and dual decoder to learn node embedding matrix, which solves the problems of insufficient node labels and unclear information. The MRF-based information enhancement module uses the Markov random field to comprehensively consider the characteristics of the node itself and the relationship between the nodes to improve the representation ability of node embedding and solves the problem of incomplete information acquisition of similar dependencies between nodes. The modularity maximization module divides the network into subgroups with tight connections and divides similar nodes into the same community. Finally, we conducted node clustering experiments on three datasets, which showed improvements of approximately 0.3 percent, 3 percent, and 5 percent compared to the state-of-the-art baseline, respectively.

关键词： Markov random fields Decoding clustering algorithms Image edge detection Topology Accuracy Unsupervised learning Graph neural networks Community detection graph neural networks Markov random field modularity maximization unsupervised learning

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Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-Supervised Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5517-5532页

作者： Mou, Zhiyu Gao, Feifei Liu, Jun Yun, Xiang Wu, Qihui Tsinghua Univ THUAI Inst Artificial Intelligence Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Intelligence Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Beijing 100084 Peoples R China Tsinghua Univ Sch Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing 100084 Peoples R China Baicells Technol Co Ltd Beijing 100044 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China

In this paper, we study the cluster head detection problem of a hierarchical unmanned aerial vehicle (UAV) swarm network (USNET), consisting of multiple double-level clusters, from the perspective of the observers who are assumed not to know any internal information on the USNET. Specifically, we propose a multi-cluster graph attention self-supervised learning (MC-GASSL) framework for the observers. The MC-GASSL comprises two main algorithms, including the sequential metric learning (SML) algorithm for detecting the clusters in the USNET, and the graph attention self-supervised learning (GASSL) algorithm for detecting the cluster head in each cluster. The basic idea of the SML is to learn an embedding space where the UAVs can be easily clustered based on Euclidean distances, and the GASSL works by calculating the attention weights between UAVs and finding the attention center of the cluster. Extensive numerical results validate the effectiveness of the MC-GASSL. Moreover, the ablation study shows that the GASSL can detect the cluster head in single clusters with over 98% detection rate on average. The ablation study also shows that the clustering purity of the USNET with the SML exceeds that with traditional clustering algorithms by at least 10% on average.

关键词： Autonomous aerial vehicles Observers clustering algorithms Self-supervised learning Signal processing algorithms Measurement Indexes Cluster head detection graph attention network self-supervised learning hierarchical UAV swarm

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Automatic Generation of Multidimensional Labels of Educational Resources Based on Grey clustering 9th

Automatic Generation of Multidimensional Labels of Education...

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9th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2023

作者： Pan, Xiafu Shi, Yuanyuan Department of Public Safety Technology Hainan Vocational College of Political Science and Law Haikou571100 China Changchun University of Architecture and Civil Engineering Changchun130607 China

ISBN: (纸本)9783031514708

With the rapid development of Internet+education and the implementation of the "Three Connections and Two Platforms Project", online education resources are becoming more and more abundant, and society is increasingly dependent on online education resources. How to efficiently and reasonably classify resources and help users quickly access education resources is a research hotspot of online education. Therefore, an automatic generation method of multidimensional labels of educational resources based on gray clustering is proposed. The method first preprocesses educational resources, including word segmentation and de stop word processing. Feature words are extracted by combining TF-IDF and TextRank. Based on the extracted feature words, the improved KNN clustering algorithm CLKNN is used to cluster educational resources. The method of grey correlation degree is used to filter the text feature words that are clustered into the same type, and the selected feature words are used as their tag words to complete the automatic generation of multi-dimensional tags of educational resources. The results show that the accuracy P (Precision), recall rate R (Recall rate) and F-Measure of the automatic generation method of multi-dimensional labels of educational resources based on gray clustering are higher, which indicates that the research method can generate labels more comprehensively and accurately. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： clustering algorithms

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clustering Driven Iterated Hybrid Search for Vertex Bisection Minimization

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IEEE TRANSACTIONS ON COMPUTERS 2022年第10期71卷 2370-2380页

作者： Jin, Yan Xiong, Bowen He, Kun Hao, Jin-Kao Li, Chu-Min Fu, Zhang-Hua Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Univ Angers Dept Comp Sci LERIA F-49045 Angers France Inst Univ France F-75231 Paris France Univ Picardie Jules Verne MIS Fac Comp Sci F-80039 Amiens 01 France Chinese Univ Hong Kong Inst Robot & Intelligent Mfg Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China

The Vertex Bisection Minimization Problem (VBMP) is a relevant graph partitioning model with a variety of practical applications. This work introduces a clustering driven iterated hybrid search algorithm (CLUHS), which is the first approach that applies clustering to reinforce iterated local search for solving VBMP. The proposed CLUHS uses hierarchical clustering to build an initial solution, guide local search process and perform search diversification. Experimental studies on 137 benchmark instances show the high competitiveness of the proposed approach compared to the state-of-the-art methods. In particular, CLUHS finds new record-breaking solutions for 18 instances.

关键词： clustering algorithms Minimization Measurement Partitioning algorithms Search problems Perturbation methods Linear programming Vertex bisection graph partitioning clustering NP-hard local search

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A Two-Stage Strategy for UAV-Enabled Wireless Power Transfer in Unknown Environments

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第2期23卷 1785-1802页

作者： Shi, Junling Cong, Peiyu Zhao, Liang Wang, Xingwei Wan, Shaohua Guizani, Mohsen Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110136 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Abu Dhabi U Arab Emirates

Due to the outstanding merits such as mobility, high maneuverability, and flexibility, Unmanned Aerial Vehicles (UAVs) are viable mobile power transmitters that can be rapidly deployed in geographically constrained regions. They are good candidates for supplying power to energy-limited Sensor Nodes (SNs) with Wireless Power Transfer (WPT) technology. In this paper, we investigate a UAV-enabled WPT system that transmits power to a set of SNs at unknown positions. A key challenge is how to efficiently gather the locations of SNs and design a power transfer scheme. We formulate a multi-objective optimization problem to jointly optimize these objectives: maximization of UAV's search efficiency, maximization of total harvested energy, minimization of UAV's flight energy consumption and maximization of UAV's energy utilization efficiency. To tackle these issues, we present a two-stage strategy that includes a UAV Motion Control (UMC) algorithm for obtaining the coordinates of SNs and a Dynamic Genetic clustering (DGC) algorithm for power transfer via grouping SNs into clusters. First, the UMC algorithm enables the UAV to autonomously control its own motion and conduct target search missions. The objective is to make the energy-restricted UAV find as many SNs as feasible without any priori known location information. Second, the DGC algorithm is used to optimize the energy consumption of the UAV by combining a genetic clustering algorithm with a dynamic clustering strategy to maximize the amount of energy harvested by SNs and the energy utilization efficiency of the UAV. Finally, experimental results show that our proposed algorithms outperform their counterparts.

关键词： Heuristic algorithms clustering algorithms Autonomous aerial vehicles Wireless sensor networks Mobile computing Genetics Energy consumption Energy consumption optimization sensor node (SN) target search two-stage strategy unmanned aerial vehicle (UAV) wireless power transfer (WPT)

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Construction and Evaluation of Information Granules: From the Perspective of clustering

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第3期52卷 2024-2037页

作者： Zhu, Xiubin Pedrycz, Witold Li, Zhiwu Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Macau Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China

While granular computing has experienced rapid growth in the past decades and some milestones have been reached, a comprehensive study of the representation capabilities delivered by numeric prototypes and granular prototypes produced by different techniques still calls for comprehensive research and a comparative analysis. Well-constructed information granules are reflective of the nature of the numeric evidence and serve as backbones of granular classifiers and granular models. The objective of this study is to review a number of clustering paradigms aimed at the construction of information granules, discuss the development of granular prototypes, and conduct a comprehensive evaluation of quality of numeric prototypes and their corresponding augmentations coming in the form of granular prototypes. We have been witnessing many studies devoted to the construction of information granules, but a comparative analysis of the quality of information granules constructed on a basis of prototypes produced by different clustering algorithms is still lacking. In this regard, the review of the clustering algorithms supporting the formation of information granules and the comprehensive comparative study of their usefulness in classification and modeling tasks offered in this study make sense. This will promote the usage of information granules in various future works, especially classification problem and system modeling.

关键词： Prototypes clustering algorithms Systems modeling Prediction algorithms Numerical models Fuzzy sets Computational modeling clustering algorithm granular computing (GrC) information granule principle of justifiable granularity

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