检索结果-内蒙古大学图书馆

Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第8期30卷 5034-5046页

作者： Ge, Tong Luo, Xu Wang, Yunhai Sedlmair, Michael Cheng, Zhanglin Zhao, Ying Liu, Xin Deussen, Oliver Chen, Baoquan Shandong Univ Dept Comp Sci Jinan 250100 Shandong Peoples R China Univ Stuttgart D-70174 Stuttgart Germany SIAT Shenzhen VisuCA Key Lab Shenzhen 518055 Guangdong Peoples R China Cent South Univ Changsha 410017 Hunan Peoples R China Beijing Genom Inst BGI Shenzhen Shenzhen 518083 Guangdong Peoples R China Univ Konstanz D-78464 Constance Germany Peking Univ Beijing 100871 Peoples R China

We propose to use optimally ordered orthogonal neighbor-joining (O-3 NJ) trees as a new way to visually explore cluster structures and outliers in multi-dimensional data. Neighbor-joining (NJ) trees are widely used in biology, and their visual representation is similar to that of dendrograms. The core difference to dendrograms, however, is that NJ trees correctly encode distances between data points, resulting in trees with varying edge lengths. We optimize NJ trees for their use in visual analysis in two ways. First, we propose to use a novel leaf sorting algorithm that helps users to better interpret adjacencies and proximities within such a tree. Second, we provide a new method to visually distill the cluster tree from an ordered NJ tree. Numerical evaluation and three case studies illustrate the benefits of this approach for exploring multi-dimensional data in areas such as biology or image analysis.

关键词： Couplings clustering algorithms Layout Visualization Biology Germanium Sorting Neighbor joining leaf ordering orthogonal layout

来源：评论

学校读者我要写书评

暂无评论

Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm

引用

IEEE ACCESS 2024年 12卷 84763-84773页

作者： Zhao, Xiumei Zhong, Xincheng Han, Bing Changzhi Univ Dept Comp Changzhi 046000 Peoples R China

Traditional pattern mining algorithms are based on tree and linked list structures. However, they often only consider a single factor of frequency or utility and have to deal with exponential search spaces as well as generate numerous candidates. Thus, we propose a frequent closed high-utility itemset mining (FCHUIM) algorithm based on Leiden community detection and a compact genetic algorithm (LcGA). This algorithm first employs Leiden community detection to decompose a dataset into several highly related transaction communities and then uses an approximate or exact strategy to mine frequent itemsets. Subsequently, it checks for closures in the mined frequent itemsets and finally employs the compact genetic algorithm to efficiently mine high-utility patterns from the frequent closed itemsets. Experimental results on four real datasets, namely Retail, Chainstore, Chess, Accidents,among which the datatype of the former two is Sparse and that of the latter two is Dense, demonstrate that compared with modified traditional closed high-utility mining algorithms, including CHUI-Miner-S, CLS-Miner-S and the most advanced algorithms for mining frequent closed high-utility mining algorithms called FCHUIM, the average runtime of LcGA-FCHUIM is lower by 40.5% than the optimal contrastive algorithm. Moreover, LcGA-FCHUIM can mine an average of 96% of frequent closed high-utility itemsets, making it an effective algorithm for frequent closed high-utility itemset mining and suitable for most scenarios.

关键词： Itemsets clustering algorithms Data mining Genetic algorithms Partitioning algorithms Accuracy Upper bound Frequent closed high-utility itemset mining (FCHUIM) Leiden community detection compact genetic algorithm (cGA) approximate strategy exact strategy

来源：评论

学校读者我要写书评

暂无评论

Asynchronous Cell-Free Massive MIMO-OFDM: Mixed Coherent and Non-Coherent Transmissions

引用

IEEE COMMUNICATIONS LETTERS 2025年第2期29卷 363-367页

作者： Li, Guoyu Wu, Shaochuan You, Changsheng Zhang, Wenbin Shang, Guanyu Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Peoples R China Southern Univ Sci & Technol SUSTech Dept Elect & Electr Engn Shenzhen 518055 Peoples R China Harbin Inst Technol Innovat Photon & Imaging Ctr Sch Instrumentat Sci & Engn Harbin 150080 Peoples R China

In this letter, we analyze the performance of asynchronous cell-free multiple-input multiple-output orthogonal frequency division multiplexing (CF mMIMO-OFDM) system with mixed coherent and non-coherent transmission approaches. To this end, we first obtain the achievable downlink sum-rate for the mixed coherent and non-coherent transmissions, and then provide a closed-form expression for the case with the maximum ratio precoding. Subsequently, an efficient access point (AP) clustering algorithm is proposed to group APs into a set of coherent clusters. Numerical results demonstrate that the mixed coherent and non-coherent transmissions can effectively improve the sum-rate of CF mMIMO-OFDM systems under asynchronous reception.

关键词： OFDM Channel estimation Uplink Downlink clustering algorithms Precoding Time-frequency analysis Symbols Vectors Central Processing Unit Cell-free massive MIMO mixed coherent and non-coherent transmissions asynchronous reception

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Scheduling in UAV-Assisted Hierarchical Wireless Sensor Networks

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第11期11卷 20194-20206页

作者： Lai, Rui Zhang, Boyuan Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang Univ Sch Automat Sci & Elect Engn Beijing 100000 Peoples R China CSSC Syst Engn Res Inst Beijing 100036 Peoples R China Southern Methodist Univ Dept Comp Sci Dallas TX 75206 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

In emerging applications of the Internet of Things, wireless sensor networks (WSNs) are often utilized to gather, track, and monitor data in remote areas with limited communication infrastructure. Since the majority of WSNs employ sensors powered by batteries, maintaining energy efficiency, and conservation is crucial for ensuring their sustained operations over time. This work designs an Energy-efficient unmanned aerial vehicle (UAV)-assisted hierarchical architecture of WSNs (EUW). EUW supports fast transmission of data collected from WSNs to a cloud server. Based on this architecture, this work first formulates a joint optimization problem for cluster head selection, time slot allocation, and UAV path planning to minimize the weighted sum of energy consumption of WSNs and that of a UAV. Then, a hybrid meta-heuristic algorithm named knowledge transfer-based particle swarm optimization (KTPSO) is designed, which utilizes previous optimization results to increase the convergence speed and find better results. Finally, numerical analysis and evaluation results are shown to demonstrate the efficiency of KTPSO and the proposed UAV-assisted architecture of hierarchical WSNs.

关键词： Wireless sensor networks Energy consumption Autonomous aerial vehicles clustering algorithms Protocols Low earth orbit satellites Satellites Cluster head (CH) selection hybrid meta-heuristic algorithms time slot allocation (TSA) unmanned aerial vehicle (UAV) path planning wireless sensor networks (WSNs)

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Vector Field Streamline clustering Framework for Brain Fiber Tract Segmentation

引用

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2022年第3期14卷 1066-1081页

作者： Xu, Chaoqing Sun, Guodao Liang, Ronghua Xu, Xiufang Zhejiang Univ Technol Dept Comp Sci Hangzhou 310023 Peoples R China Hangzhou Med Coll Sch Med Imaging Hangzhou 310053 Peoples R China

Brain fiber tracts are widely used in studying brain diseases, which may lead to a better understanding of how disease affects the brain. The segmentation of brain fiber tracts assumed enormous importance in disease analysis. In this article, we propose a novel vector field streamline clustering framework for brain fiber tract segmentations. Brain fiber tracts are first expressed in a vector field and compressed using the streamline simplification algorithm. After streamline normalization and regular-polyhedron projection, high-dimensional features of each fiber tract are computed and fed to the improved deep embedded clustering (IDEC) algorithm. We also provide qualitative and quantitative evaluations of the IDEC clustering method and QB clustering method. Our clustering results of the brain fiber tracts help researchers gain perception of the brain structure. This work has the potential to automatically create a robust fiber bundle template that can effectively segment brain fiber tracts while enabling consistent anatomical tract identification.

关键词： Optical fiber networks clustering algorithms Brain Deep learning Streaming media clustering methods Shape Brain fiber tracts deep clustering feature construction streamline simplification vector field

来源：评论

学校读者我要写书评

暂无评论

Feature Analysis and Classification of Collusive Android App-Pairs Using DBSCAN clustering Algorithm 1st

Feature Analysis and Classification of Collusive Android A...

引用

1st EAI International Conference on Safe, Secure, Ethical, Responsible Technologies and Emerging Applications, SAFER-TEA 2023

作者： Mawoh, Roger Yiran Tchakounte, Franklin Ali, Joan Beri Fachkha, Claude Department of Computer Science Faculty of Science University of Buea Buea Cameroon Department of Mathematics and Computer Science Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Cybersecurity with Computational and Artificial Intelligence Research Group Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon College of Engineering and IT University of Dubai Dubai United Arab Emirates

ISBN: (纸本)9783031563959

In this study, we tackle the issue of application collusion, which involves multiple apps working together to achieve malicious goals that they couldn’t achieve individually. The current security model of Android, which focuses on permissions, doesn’t effectively address this threat because it only mitigates risks associated with individual apps. To address this limitation, we carried out an extensive analysis of features to identify the key Android permissions utilized by colluding app-pairs. We propose an approach to classify collusive app-pairs using the DBSCAN clustering algorithm. Our results provide valuable insights into the relationship between specific Android permission sets and the malicious activities performed by colluding app-pairs. We identified 12 permissions as the most important features contributing to the classification of collusive app-pairs. We also identified 4 distinct clusters of colluding Android app-pairs. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

A Complex Fracture Network Modeling Method Based on Dimensionality Enhancement of Microseismic Event Location Information

引用

IEEE ACCESS 2024年 12卷 156160-156171页

作者： Liu, Jianfeng Zhai, Hongyu Mao, Qinghui Gui, Zhixian Wang, Peng Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Wuhan 430100 Peoples R China China Earthquake Adm Inst Geophys Beijing 100081 Peoples R China Yangtze Univ Cooperat Innovat Ctr Unconvent Oil & Gas Minist Educ & Hubei Prov Wuhan 430100 Peoples R China

The modeling method of hydraulic fracturing fracture network combined with microseismic monitoring is currently a hot research topic. In the process of hydraulic fracturing production, previous studies have established a fracture model after fracturing based on the recorded source coordinates, earthquake time, and moment magnitude throughout the entire fracturing cycle, in order to evaluate the fracturing results. However, in the process of reservoir fracturing transformation, real-time evaluation of fracturing effect and provision of fracturing guidance are required. The method of establishing fracture models through long-term data collection is obviously not applicable. Nevertheless, existing research lacks efficient and direct 3D fracture network modeling methods based on microseismic events. To address these challenges, this study introduces a novel three-dimensional fracture network model reconstruction process, DEFME (Dimensionality Enhancement and Fracture Modeling of Microseismic Event Location Information), specifically tailored for datasets containing only the source coordinate information of microseismic events. This innovative process initially incorporates the improved DB-RANSAC (Density-Based Random Sample Consensus) method, introducing new regional microseismic event normal vector constraint information. It transforms the conventional DBSCAN(Density-Based Spatial clustering of Applications with Noise) clustering algorithm into the NC-DBSCAN (Normal Vector Constraint-DBSCAN) method, combining normal vector constraints with density constraints for microseismic event point clustering. Finally, the Alpha-shape method is employed to extract contour information for each fracture, constructing a three-dimensional plane geometric model of the fracture. To validate the efficacy of the proposed processing workflow, synthetic data and public dataset are utilized. The corresponding results indicate that DEFME has successfully overcome noise interference and effic

关键词： Hydraulic systems clustering algorithms Reservoirs Solid modeling Stress measurement Real-time systems Data models Seismic measurements Microseismic monitoring NC-DBSCAN DB-RANSAC hydraulic fracturing fracture modeling

来源：评论

学校读者我要写书评

暂无评论

AI-Empowered Intelligent Search for Path Planning in UAV-Assisted Data Collection Networks

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第21期11卷 34492-34503页

作者： Li, Xueqiang Tao, Ming Yang, Shuling Jan, Mian Ahmad Du, Jun Liu, Lei Wu, Celimuge Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China Univ Sharjah Coll Comp & Informat Dept Comp Sci Sharjah U Arab Emirates Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Shaanxi Peoples R China Univ Electrocommun Meta Networking Res Ctr Tokyo 1828585 Japan

Unmanned aerial vehicle (UAV) assisted data collection has been extensively employed in various application scenarios, e.g., nonterrestrial networks for disaster management, agricultural crop protection, environmental monitoring. However, data collection and transmission model in different applications are not universal, and the timeliness of large-scale data collection and transmission also has been remained as a challenge. To address this issue, artificial intelligence (AI)-empowered intelligent search algorithms for path planning in UAV-assisted data collection networks are investigated in this article. With the constraints, including energy consumption, transmission distances, and full coverage of sensors, a data collection model using UAV in hovering mode is first established for minimizing the flight distances of UAVs, and an adaptive full coverage algorithm (AFCA) is proposed to optimize the Quality of Service through using the model. Subsequently, for optimizing the path planning of UAVs, an intelligent path planning algorithm (IPPA) is proposed through considering the loop and noncrossing characteristics presented by the optimal paths. In six testing cases with different sensor sizes, the experimental results have been shown to demonstrate that the proposed solution outperforms the traditional algorithms.

关键词： Data collection Autonomous aerial vehicles Trajectory Artificial intelligence Internet of Things Energy consumption clustering algorithms Intelligent search path planning unmanned aerial vehicle (UAV)-assisted data collection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：