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Hilbert-Ordering Based clustering of Complex-Excitations Linear Arrays

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第8期70卷 6751-6762页

作者： Benoni, Arianna Rocca, Paolo Anselmi, Nicola Massa, Andrea Univ Trento Dept Civil Environm & Mech Engn DICAM ELEDIA Res Ctr ELEDIA UniTN I-38123 Trento Italy CNIT Univ Trento Res Unit I-38123 Trento Italy Xidian Univ ELEDIA Res Ctr ELEDIA XIDIAN Xian 710071 Shaanxi Peoples R China ELEDIA Res Ctr ELEDIA UESTC UESTC Sch Sci & Engn Chengdu 611731 Peoples R China Tsinghua Univ ELEDIA Res Ctr ELEDIA TSINGHUA Beijing 100084 Haidian Peoples R China

The clustering of linear phased arrays (PAs) with complex weights is discussed. By exploiting the locality-preservation property of the Hilbert curve, the dimensionality of the problem at hand has been first reduced, and then, a simple clustering algorithm has been applied to maximize the matching of the radiated pattern with a reference one. Both contiguous and noncontiguous partitions of the Hilbert-ordered list of complex excitations have been evaluated to effectively sample the solution space of clustered solutions. A set of representative results, including reference PAs affording steered pencil and shaped beams, are reported for validation purposes as well as to point out the effectiveness of the proposed approach in comparison with state-of-the-art k-means algorithms as well.

关键词： Pattern matching Phased arrays Costs clustering algorithms Linear antenna arrays Cost function Wavelength measurement Arbitrary-shaped pattern beams complex excitations Hilbert curve linear phased array (PA) power pattern matching subarraying

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Subspace clustering via Structured Sparse Relation Representation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4610-4623页

作者： Wei, Lai Ji, Fenfen Liu, Hao Zhou, Rigui Zhu, Changming Zhang, Xiafen Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Wuhan Digit Engn Inst Wuhan 430205 Peoples R China

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of ``relation reconstruction.'' We pointed out that a data sample could be represented by the neighborhood relation computed between its neighbors and itself. The neighborhood relation could indicate the true membership of its corresponding original data sample to the subspaces of a data set. We also claimed that a data sample's neighborhood relation could be reconstructed by the neighborhood relations of other data samples;then, we suggested a much different way to define affinity graphs consequently. Based on these propositions, a sparse relation representation (SRR) method was proposed for solving subspace clustering problems. Moreover, by introducing the local structure information of original data sets into SRR, an extension of SRR, namely structured sparse relation representation (SSRR) was presented. We gave an optimization algorithm for solving SRR and SSRR problems and analyzed its computation burden and convergence. Finally, plentiful experiments conducted on different types of databases showed the superiorities of SRR and SSRR.

关键词： clustering algorithms Sparse matrices Image reconstruction Optimization Faces Convergence Task analysis Low-rank representation (LRR) neighborhood relation sparse subspace clustering (SSC) subspace clustering

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A Spectral clustering Algorithm for Similar Matrix Reconstruction based on Adaptive Neighborhood Standard Deviation 6

A Spectral Clustering Algorithm for Similar Matrix Reconstru...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Gao, Xuwen Su, Xinyan Yao, Jinjie Shanxi Provincial Key Laboratory of Intelligent Detection Technology and Equipment North University of China Taiyuan China

ISBN: (纸本)9798331541699

In view of the shortcomings of the classical spectral clustering algorithm in the artificial setting of scale parameters and the accuracy of the similar matrix construction, which leads to the low accuracy of the clustering results, a similar matrix reconstruction spectral clustering algorithm based on adaptive neighborhood standard deviation is proposed. The algorithm uses the inverse of the sample neighborhood standard deviation as a parameter to measure the local density of the sample. It constructs an adaptive scale parameter by incorporating a multi-region density adjustment strategy. Additionally, a nonlinear Sigmoid function is introduced to design the scale difference adjustment term, and the similarity matrix is reconstructed by integrating global density. The experimental results demonstrate that the proposed algorithm achieves good clustering performance on both synthetic datasets and real UCI datasets, with more stable clustering results. © 2024 IEEE.

关键词： clustering algorithms

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Identifying Gene Signatures for Cancer Drug Repositioning Based on Sample clustering

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第2期19卷 953-965页

作者： Wang, Fei Ding, Yulian Lei, Xiujuan Liao, Bo Wu, Fangxiang Univ Saskatchewan Div Biomed Engn 57 Campus Dr Saskatoon SK S7N 5A9 Canada Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Hainan Normal Univ Sch Math & Stat Haikou 571158 Hainan Peoples R China Univ Saskatchewan Dept Mech Engn 57 Campus Dr Saskatoon SK S7N 5A9 Canada Univ Saskatchewan Dept Comp Sci 57 Campus Dr Saskatoon SK S7N 5A9 Canada

Drug repositioning is an important approach for drug discovery. Computational drug repositioning approaches typically use a gene signature to represent a particular disease and connect the gene signature with drug perturbation profiles. Although disease samples, especially from cancer, may be heterogeneous, most existing methods consider them as a homogeneous set to identify differentially expressed genes (DEGs)for further determining a gene signature. As a result, some genes that should be in a gene signature may be averaged off. In this study, we propose a new framework to identify gene signatures for cancer drug repositioning based on sample clustering (GS4CDRSC). GS4CDRSC first groups samples into several clusters based on their gene expression profiles. Second, an existing method is applied to the samples in each cluster for generating a list of DEGs. Then a weighting approach is used to identify an intergrated gene signature from all the lists of DEGs. The integrated gene signature is used to connect with drug perturbation profiles in the Connectivity Map (CMap)database to generate a list of drug candidates. GS4CDRSC has been tested with several cancer datasets and existing methods. The computational results show that GS4CDRSC outperforms those methods without the sample clustering and weighting approaches in terms of both number and rate of predicted known drugs for specific cancers.

关键词： Drugs Cancer Databases clustering algorithms Gene expression Perturbation methods Drug repositioning sample clustering gene signature gene expression drug perturbation

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Maximum Joint Probability With Multiple Representations for clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4300-4310页

作者： Zhang, Rui Zhang, Hongyuan Li, Xuelong 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

Classical generative models in unsupervised learning intend to maximize p(X). In practice, samples may have multiple representations caused by various transformations, measurements, and so on. Therefore, it is crucial to integrate information from different representations, and lots of models have been developed. However, most of them fail to incorporate the prior information about data distribution p(X) to distinguish representations. In this article, we propose a novel clustering framework that attempts to maximize the joint probability of data and parameters. Under this framework, the prior distribution can be employed to measure the rationality of diverse representations. K-means is a special case of the proposed framework. Meanwhile, a specific clustering model considering both multiple kernels and multiple views is derived to verify the validity of the designed framework and model.

关键词： Kernel Unsupervised learning Data models clustering algorithms Task analysis Feature extraction Computational modeling clustering maximum joint probability multikernel learning multiview learning unsupervised learning

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Blockchain-Based File Replication for Data Availability of IPFS Consumers

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1191-1204页

作者： Yang, Feng Ding, Zongya Jia, Linpeng Sun, Yi Zhu, Qingfeng Shandong Univ Finance & Econ Sch Comp Sci & Technol Shandong Key Lab Blockchain Finance Jinan 250014 Shandong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Shandong Univ Finance & Econ Shandong Key Lab Blockchain Finance Jinan 250014 Shandong Peoples R China Shandong Univ Finance & Econ Sch Stat & Math Shandong Key Lab Blockchain Finance Jinan 250014 Shandong Peoples R China

In the Interplanetary File System (IPFS), consumers can help each other protect data against hardware failures and improve data availability through replication. While previous replication methods in peer-to-peer (P2P) networks can be used to increase data availability in the IPFS network, they are either hostile to peers with limited availability, preventing them from achieving adequate data availability, or lack flexibility. An ideal replication method should optimize data availability in a manner equitable to all peers while providing flexibility. To achieve this goal, this paper introduces a blockchain-based file replication mechanism. Leveraging the non-temperable and traceable nature of blockchain technology, our mechanism achieves secure storage and trustworthy query of peers' information used in the file replication process. Unlike most earlier methods, our mechanism employs an Arweave-inspired file replication algorithm that prioritizes the less available files within the system for replication until all files' availabilities are optimized. Replicating files according to predefined system-wide cooperation rules like this not only limits the selfishness of peers but also facilitates timely adjustments in response to changes in the P2P system. In addition, our mechanism also uses smart contracts to judge and exclude dishonest peers, thereby fostering honest cooperation among peers without involving any third party.

关键词： Blockchains InterPlanetary File System Smart contracts Finance Codes clustering algorithms Consumer electronics Interplanetary file system (IPFS) blockchain file replication data availability distributed storage

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TTK is Getting MPI-Ready

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第8期30卷 5875-5892页

作者： Le Guillou, Eve Will, Michael Guillou, Pierre Lukasczyk, Jonas Fortin, Pierre Garth, Christoph Tierny, Julien Sorbonne Univ CNRS F-75005 Paris France Univ Lille F-75005 Paris France Univ Lille CNRS UMR 9189 Cent LilleCRIStAL F-59000 Lille France

This system paper documents the technical foundations for the extension of the Topology ToolKit (TTK) to distributed-memory parallelism with the Message Passing Interface (MPI). While several recent papers introduced topology-based approaches for distributed-memory environments, these were reporting experiments obtained with tailored, mono-algorithm implementations. In contrast, we describe in this paper a versatile approach (supporting both triangulated domains and regular grids) for the support of topological analysis pipelines, i.e., a sequence of topological algorithms interacting together, possibly on distinct numbers of processes. While developing this extension, we faced several algorithmic and software engineering challenges, which we document in this paper. Specifically, we describe an MPI extension of TTK's data structure for triangulation representation and traversal, a central component to the global performance and generality of TTK's topological implementations. We also introduce an intermediate interface between TTK and MPI, both at the global pipeline level, and at the fine-grain algorithmic level. We provide a taxonomy for the distributed-memory topological algorithms supported by TTK, depending on their communication needs and provide examples of hybrid MPI+thread parallelizations. Detailed performance analyses show that parallel efficiencies range from 20% to 80% (depending on the algorithms), and that the MPI-specific preconditioning introduced by our framework induces a negligible computation time overhead. We illustrate the new distributed-memory capabilities of TTK with an example of advanced analysis pipeline, combining multiple algorithms, run on the largest publicly available dataset we have found (120 billion vertices) on a standard cluster with 64 nodes (for a total of 1536 cores). Finally, we provide a roadmap for the completion of TTK's MPI extension, along with generic recommendations for each algorithm communication category.

关键词： Pipelines Parallel processing Data structures Memory management Topology clustering algorithms Software algorithms Topological data analysis high-performance computing distributed-memory algorithms

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Multi-Objective Deployment Optimization of UAVs for Energy-Efficient Wireless Coverage

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第6期72卷 3587-3601页

作者： Zhu, Xiumin Zhai, Linbo Li, Nianxin Li, Yumei Yang, Feng Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

Recently, Unmanned Aerial Vehicles (UAVs) have attracted much attention due to their flexibility and low cost. However, there are limitations for multiple UAVs such as limited energy and collaborative coverage. To achieve a better coverage performance, each UAV needs to find the optimal position to cover many ground users while saving energy. However, there are trade-offs between coverage utility and energy consumption. In this paper, we study a multi-UAV communication scenario where multi-UAV array is deployed to provide wireless coverage for mobile ground users. Considering the number, 3D positions, and speeds of UAVs, we formulate a Coverage Utility and Energy Multi-objective Optimization Problem (CUEMOP) to simultaneously maximize the total coverage utility and minimize the total energy consumption of UAVs. Due to the complexity and NP-hardness of the formulated CUEMOP, we propose Improved Multi-objective Grey Wolf Optimizer (ImMOGWO) algorithm. In this algorithm, we design the Role Determination (RD) algorithm to cluster the ground users and prepare for initialization of UAV number and position. Hybrid solution initialization (HSI) algorithm is to initialize multi-dimensional variables and overcome algorithm inefficiency caused by random initialization. The Levy flight and Sin Cosine method based on the MOGWO algorithm (LSCMGA) is proposed to increase the diversity of solutions and ensure the convergence effect of the algorithm. Simulation results verify that proposed ImMOGWO algorithm has better performance than some other benchmark methods.

关键词： Autonomous aerial vehicles Optimization clustering algorithms Wireless communication Energy consumption Three-dimensional displays Base stations Multiple UAVs multi-objective optimization problem coverage performance energy efficiency

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Opportunistic Sensing in Task-Oriented Wireless Sensor Network Based on Graph Compressed Sensing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4481-4492页

作者： Wang, Wei Gao, Hefei Xu, Lei Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tra Tianjin 300387 Peoples R China Douyin Vis Co PDI Algorithm Qual Part Beijing 100000 Peoples R China

As artificial intelligence and modern signal processing technologies progress, sensor networks often necessitate not collecting information from all nodes in order to effectively perceive and monitor target areas in practical applications. Such progress sets the foundation for Opportunistic Sensing (OS) which is a method engineered to automatically discover and select sensor nodes for efficient data gathering. In this paper, we propose a novel OS algorithm for optimizing node deployment in task-oriented wireless sensor networks. It can efficiently fuse sensed information by partitioning the network into multiple subnetworks and integrating graph compressed sensing with Restricted Boltzmann Machine techniques. Moreover, we employ the Kullback-Leibler divergence to quantify information distortion induced by OS. We also introduce the brainstorm optimization algorithm to improve sensor selection strategy. Experiments demonstrate that the proposed algorithm can efficiently diminish reconstruction errors and enhance network performance compared with classical and recent baseline methods.

关键词： Wireless sensor networks Task analysis Sensors Optimization Compressed sensing clustering algorithms Monitoring Opportunistic sensing topological potential pool graph compressed sensing restricted Boltzmann machine brainstorm optimization

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Impact of noise in distance-based short text clustering 18

Impact of noise in distance-based short text clustering

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Datta, Abhradeep Sairam, Ashok Singh Indian Institute of Technology Dept. of Mathematics Assam Guwahati India

ISBN: (纸本)9798350391725

Efficient text clustering is essential for applications like recommendation systems, trend analysis, and user profiling, particularly when dealing with data from online repositories such as social media platforms. However, the effectiveness of clustering methods is significantly impacted by the inherent challenges of such data, including sparsity, short text length, and unstructured nature. One of the most critical challenges is the prevalence of noise in the data, manifested through slang, emojis, misspellings, abbreviations, and grammatical errors, which can severely degrade clustering performance. This research examines the robustness of distance-based clustering algorithms against noisy data by systematically introducing noise directly into the text. We introduce two types of noise-attribute noise and class noise for effective noise injection. We analyze the ability of different distance measures to differentiate meaningful content from irrelevant information under these noise-induced conditions. Through a series of experiments, we evaluate the degradation in clustering accuracy of various distance measures in the presence of noise, providing valuable insights into their noise tolerance and practical utility in real-world scenarios. © 2024 IEEE.

关键词： clustering algorithms

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