检索结果-内蒙古大学图书馆

An effective heuristic clustering algorithm for mining multiple critical nodes in complex networks

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2022年 588卷

作者： Wang, Ying Zheng, Yunan Shi, Xuelei Liu, Yiguang Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

Influence maximization is of great significance in complex networks, and many methods have been proposed to solve it. However, they are usually time-consuming or cannot deal with the overlap of spreading. To get over the flaws, an effective heuristic clustering algorithm is proposed in this paper: (1) nodes that have been assigned to clusters are excluded from the network structure to guarantee they do not participate in subsequent clustering. (2) the K-shell (k(s)) and Neighborhood Coreness (NC) value of nodes in the remaining network are recalculated, which ensures the node influence can be adjusted during the clustering process. (3) a hub node and a routing node are selected for each cluster to jointly determine the initial spreader, which balances the local and global influence. Due to the above contributions, the proposed method preferably guarantees the influence of initial spreaders and the dispersity between them. A series of experiments based on Susceptible-Infected-Recovered (SIR) stochastic model confirm that the proposed method has favorable performance under different initial constraints against known methods, including VoteRank, HC, GCC, HGD, and DLS-AHC. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Influence maximization Multiple influential spreaders clustering algorithm Complex networks SIR model

来源：评论

学校读者我要写书评

暂无评论

Automatic filtering processing in off-axis digital holography with clustering algorithm 11

Automatic filtering processing in off-axis digital holograph...

引用

Conference on Holography, Diffractive Optics, and Applications XI

作者： Sun, Qiuya Liu, Yiwei Chen, Hao Wang, Yibo Jiang, Zhuqing Beijing Univ Technol Coll Phys & Optoelect Fac Sci Beijing 100124 Peoples R China

ISBN: (数字)9781510646469

ISBN: (纸本)9781510646469;9781510646452

In this paper, automatic filtering for amplitude and phase reconstruction in off-axis digital holography is developed. A user-friendly interface for automatic filtering is given via program design with MATLAB. The hologram to be processed is input at the front end, and automatic spectrum filtering in Fourier spectrum domain of digital holograms is realized by using clustering algorithm at the back end. The amplitude and phase images are reconstructed from the intercepted spatial-frequency spectrum by using the reconstruction algorithm. This automatic filtering program has high robustness, which can achieve reconstruction imaging for off-axis holograms correctly and effectively in the case of different off-axis angles or different image sizes. For the user interface, upon inputting an off-axis digital hologram and confirming the operation, the reconstructed amplitude and phase images can be quickly output. This user interface has the advantages of simple operation, adjustable parameters and clear feedback. Since K-means clustering is used, this filtering algorithm increases the efficiency in processing experimental data and the reliability of reconstruction imaging. The digital hologram computer-generated is used to simulate filtering processing. The results show that the quality of reconstructed images by using the presented automatic filtering is not inferior to that by conventional manual filtering.

关键词： off-axis digital holography adaptive filtering user interface clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

WSN clustering Routing algorithm Combining Sine Cosine algorithm and Levy Mutation

引用

IEEE ACCESS 2023年 11卷 22654-22663页

作者： Guo, Xiaoling Ye, Yongfei Li, Ling Wu, Renjie Sun, Xinghua Hebei North Univ Sch Informat Sci & Engn Zhangjiakou 075000 Peoples R China

Directed against the disadvantages of relatively short life-cycle and unbalanced energy utilization among nodes in WSN, a clustering routing algorithm combining sine cosine algorithm and Levy mutation is developed. During the cluster head election stage, the amount of cluster heads is dynamically calculated according to the surviving nodes for keeping it at a reasonable value;taking full account of the current energy of nodes, only nodes with high energy can be candidate cluster heads;the fitness function is constructed according to intra-cluster distance, so that the distribution structure within the cluster are relatively uniform;the Sine Cosine algorithm with improved step size search factor is used for cluster head election, and Levy mutation is introduced to realize the variation of population. The group of individuals with the lowest fitness function value is used as final election scheme for current round. In the data transmission phase, for the sake of avoiding long-distance transmission, the relay node is designed to forward data. The proposed algorithm effectively extends network life-cycle and well equalizes the load of network nodes.

关键词： clustering algorithms Routing Wireless sensor networks Relays Heuristic algorithms Data communication Voting clustering algorithm energy balance life cycle Levy mutation sine cosine algorithm WSN

来源：评论

学校读者我要写书评

暂无评论

An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing network

Journal of Engineering Research (Kuwait)

引用

Journal of Engineering Research (Kuwait) 2024年

作者： Li, Hao Wang, Hongwei Wang, Kaiyu Qu, Tonghui Ren, Xunhuan Ma, Jun Belarusian State University of Informatics and Radioelectronics Minsk Belarus Forestry School of materials Engineering Northeast Forestry University HeiLongJiang 150040 China Hangzhou Hikvision Digital Technology Co. Ltd. Hangzhou 310052 China

Mobile crowd-sensing (MCS) is a cutting-edge paradigm that gathers sensory data and generates valuable insights for a multitude of users by utilizing built-in sensors and social applications in mobile devices. This enables a broad spectrum of Internet of Things (IoT) services. We introduce a novel MCS algorithm, Mobile Crowd-sensing Low Energy clustering (MCLEC), which employs advanced clustering techniques to address issues of data oversampling and energy inefficiency prevalent in MCS networks. MCLEC innovatively adjusts clustering radii based on local node density and the proximity of nodes to the cloud server, thus optimizing data transmission paths and reducing energy consumption. A pivotal enhancement in MCLEC is its cluster head election strategy, which prioritizes leaders based on their energy levels and mobility, thereby enhancing network stability and minimizing the frequency of head re-elections. Our comparisons with established algorithms such as LEACH, LEACH-C, LEACH-M, DEEC, and SEP show that MCLEC significantly improves energy efficiency, reduces server load, and prolongs the lifespan of network nodes, establishing it as an effective solution for IoT applications dependent on MCS. Additionally, MCLEC was compared with other novel clustering algorithms including E-FLZSEPFCH, DFLC, ECPF, ACAWT, UCR, CHEF, and Gupta's algorithm. The results indicate that MCLEC also surpasses most of these algorithms in terms of energy consumption and network lifetime. © 2024 The Authors

关键词： clustering algorithm Energy efficient Mobile crowd-sensing Node density

来源：评论

学校读者我要写书评

暂无评论

Vehicle clustering algorithm based on Reliable Node Screening

Vehicle Clustering Algorithm based on Reliable Node Screenin...

引用

第35届中国控制与决策会议

作者： Nan Bao Jianming He Xixia Sun Su Pan School of Internet of Things Nanjing University of Posts and Telecommunications

In order to solve the problem of unstable communication between high-speed moving vehicles in the Internet of Vehicles,a clustering algorithm based on reliable node screening was proposed in this *** algorithm screened the neighbor nodes according to the vehicle direction and the vehicle link survival time,and formed a list of reliable ***,a cluster head election method was proposed based on node reliability *** NS3 and Sumo co-simulation,it is found that the proposed algorithm has better cluster stability compared with the traditional algorithm in the environment of high speed and high vehicle density.

关键词： clustering algorithm reliable node cluster head election cluster stability

来源：评论

学校读者我要写书评

暂无评论

Partition of the workspace for machine tool based on position-dependent modal energy distribution and clustering algorithm

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第3期108卷 943-955页

作者： Peng, Yili Li, Bin Mao, Xinyong Li, Chunhui Liu, Hongqi Peng, Fangyu Huazhong Univ Sci & Technol HUST Natl NC Syst Engn Res Ctr Sch Mech Sci & Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The natural frequencies and damping ratios of machine tool structure vary with the change of the machining position in the machining space. When the stiffness distribution of the whole machine structure is not uniform, some position change will further lead to the change of weak components of the structure. In order to detail the position-dependent dynamics of the machine tool, the change of structure dynamics caused by the change of position is divided into two types: one is both the modal parameters and structural weakness change, and the other is that only the modal parameters change, while the weakness remains unchanged. The entire workspace can be divided into different subareas according to whether the weakness changes. In the same subarea, only the modal parameters change and the weakness remains unchanged. In the different subareas, the weakness of whole machine tool structure changes. The change of structural weakness influences the vibration characteristics of the machine tool and the dominant modes of vibration. Hence, the partition of machining space according to the change of structural weakness is helpful to more accurately analyze the position-dependent dynamics and vibration characteristics of the machine tool. Firstly, this paper presents the method of modal energy distribution to analysis position-dependent structural weakness and the principle of the clustering to divide the workspace. A simulation example is given to verify the effectiveness of the method. Then, the clustering partition of the workspace for a gantry machining center is conducted with the presented method. Finally, the cutting tests are performed to verify the change of the vibration dominant mode of machine tool at different subareas.

关键词： Partition of the workspace Modal energy distribution clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Density Peaks clustering algorithm Based on Local Reachability Density

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 690-697页

作者： Wang, Hanqing Zhou, Bin Zhang, Jianyong Cheng, Ruixue Southeast Univ Sch Energy & Environm Sipailou Rd 2 Nanjing Jiangsu Peoples R China Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough TS1 3BA Cleveland England

A novel clustering algorithm named local reachability density peaks clustering (DPC) which uses local reachability density, improve the performance of the density peaks clustering algorithm (DPC) is proposed in this paper. This algorithm enhances robustness by removing the cutoff distance dc which is a sensitive parameter from the DPC. In addition, anew allocation strategy is developed to eliminate the domino effect, which often occurs in DPC. The experimental results confirm that this algorithm is feasible and effective. (C) 2020 The Authors. Published by Atlantis Press SARL.

关键词： clustering algorithm Density peaks clustering Local reachability density Domino effect

来源：评论

学校读者我要写书评

暂无评论

An Improvement of K-Medoids clustering algorithm Based on Fixed Point Iteration

引用

INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING 2020年第4期16卷 84-94页

作者： Huang, Xiaodi Ren, Minglun Hu, Zhongfeng Hefei Univ Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China

The process of K-medoids algorithm is that it first selects data randomly as initial centers to form initial clusters. Then, based on PAM (partitioning around medoids) algorithm, centers will be sequential replaced by all the remaining data to find a result has the best inherent convergence. Since PAM algorithm is an iterative ergodic strategy, when the data size or the number of clusters are huge, its expensive computational overhead will hinder its feasibility. The authors use the fixed-point iteration to search the optimal clustering centers and build a FPK-medoids (fixed point-based K-medoids) algorithm. By constructing fixed point equations for each cluster, the problem of searching optimal centers is converted into the solving of equation set in parallel. The experiment is carried on six standard datasets, and the result shows that the clustering efficiency of proposed algorithm is significantly improved compared with the conventional algorithm. In addition, the clustering quality will be markedly enhanced in handling problems with large-scale datasets or a large number of clusters.

关键词： clustering algorithm Fixed-Point Iteration FPK-Medoids K-Medoids Parallel Optimizing

来源：评论

学校读者我要写书评

暂无评论

A robust clustering algorithm using spatial fuzzy C-means for brain MR images

引用

EGYPTIAN INFORMATICS JOURNAL 2020年第1期21卷 51-66页

作者： Alruwaili, Madallah Siddiqi, Muhammad Hameed Javed, Muhammad Arshad Jouf Univ Coll Comp & Informat Sci Sakaka Saudi Arabia

Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come with several problems such as noise and other imaging artifacts added during acquisition process. The researchers have actual challenges for segmentation under the consideration of these effects. In medical images, a well-known clustering approach like Fuzzy C-Means widely used for segmentation. The performance of FCM algorithm is fast in noise-free images;however, this method did not consider the spatial context of the image due to which its performance suffers when images corrupted with noise and other imaging relics. In this paper, a weighted spatial Fuzzy C-Means (wsFCM) segmentation method is proposed that considered the spatial information of image. Moreover, a spatial function is also developed that integrate a membership function. In order assess this function, a neighborhood window is established around a pixel and more weights have been assigned to those pixels which have greater correlation with central pixel in local neighborhood. By integration of this spatial function in membership function, the modified membership function strengthens the original membership function in handling the noise and intensity inhomogeneity, which has the ability to preserves and maintains structural information like edges. A comprehensive set of experimentation is performed on publicly accessible simulated and real standard brain MRI datasets. The performance of the proposed method has been compared with existing state-of-the-art methods. The results show that the performance of the proposed method is better and robust in handling noise and intensity inhomogeneity than of the existing works. (C) 2019 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.

关键词： clustering algorithm MRI Fuzzy C-means

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient clustering algorithm in Underwater Sensor Networks Based on Fuzzy C Means and Moth-Flame Optimization Method

引用

IEEE ACCESS 2020年 8卷 97474-97484页

作者： Wang Fei Bai Hexiang Li Deyu Wang Jianjun Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Grain & Oils Informat Ctr Jiangsu Prov Nanjing 210008 Peoples R China

Underwater sensor networks (UWSN) often suffers from the irreplaceable batteries and high delay of long-distance communications, thus one of the most important issues on UWSN is how to extend the lifespan of the network and balance the energy consumption of each node by reducing the transmission distances. Actually, clustering method is one of the main methods to resolve the problem. In the clustered UWSN, the major concerns are obtaining appropriate number of clusters, forming the clusters and selecting an optimal cluster head(CH) with each cluster. This paper proposes a novel hybrid clustering method based on fuzzy c means (FCM) and moth-flame optimization method (MFO) to improve the performance of the network(FCMMFO). The idea is to form energy-efficient clusters by using FCM and then use an optimization algorithm MFO to select the optimal CH within each cluster. The simulation results validate the energy-efficient performance of FCMMFO in comparison with the other existing algorithms. The results clearly show the significant impact of FCMMFO on energy-efficiency in UWSN.

关键词： clustering algorithms Energy consumption Energy efficiency clustering methods Wireless sensor networks Voting Optimization methods UWSN clustering algorithm fuzzy C means moth-flame optimization

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：