检索结果-内蒙古大学图书馆

Trust and grey model based data aggregation algorithm

INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2021年第4期35卷 258-266页

作者： Wang, Jun Wang, Ni Li, Li Du, Yansui Shenyang Univ Chem Technol Sch Comp Sci & Technol Shenyang 110142 Peoples R China Party Sch Liaoning Prov Party Comm Shenyang 110004 Liaoning Peoples R China

The reliability of data aggregation is an important problem in wireless sensor networks. Therefore, trust and grey model based data aggregation algorithm (TGDA) is proposed. The algorithm combines technologies such as trust mechanism, data prediction and data aggregation. Before initiating data aggregation, the cluster heads will detect the abnormal nodes whose trust value is lower than a certain value, and add them to the blacklist. According to the high time dependence of the data collected by sensor nodes, the grey model is used to predict the missing data of the abnormal nodes. Finally, the trusted data are aggregated. Simulation results show that compared with other algorithms, the algorithm can effectively improve the security and accuracy of data aggregation under the premise of ensuring the network life cycle.

关键词： trust mechanism data prediction grey model data aggregation time dependence time series abnormal nodes weight mechanism clustering algorithm wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Image denoising based on BCOLTA: Dataset and study

引用

IET IMAGE PROCESSING 2021年第3期15卷 624-633页

作者： Han, Lili Li, Shujuan Liu, Xiuping Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Shaanxi Peoples R China Xian Polytech Univ Sch Elect Informat Xian Shaanxi Peoples R China

Robot deburring is an effective method for improving the surface quality of the high-voltage copper contact. The first step of robot deburring is to acquire the burr images. We propose a new burr mathematical model and build a real burr image dataset for burr image denoising. In order to improve burr image denoising effects of the high-voltage copper contact, this study proposes an online burr image denoising algorithm, that is, block cosparsity overcomplete learning transform algorithm (BCOLTA). The penalty term and the condition number are affected by the burr parameter. The clustering and transform alternate minimisation algorithms are adopted to achieve lower computational cost and better denoising effect. In addition, BCOLTA also has a good adaptibility to inherent noise images, especially in Gaussian noise. Compared with other traditional and deep learning algorithms by no reference and full reference image quality assessment methods, BCOLTA has state-of-the-art denoising effects and computational complexity on dealing with burr images. This research will play an important role in the intelligent manufacturing field.

关键词： robot deburring clustering algorithm burr image denoising condition number intelligent manufacturing Production engineering computing Optimisation techniques transform alternate minimisation algorithm intelligent manufacturing systems Computer vision and image processing techniques production engineering computing Optical, image and video signal processing Integral transforms Mathematical analysis pattern clustering burr parameter BCOLTA Industrial applications of IT Manufacturing systems learning (artificial intelligence) image denoising high-voltage copper contact minimisation Optimisation transforms block cosparsity overcomplete learning transform algorithm Gaussian noise penalty term Other topics in statistics

来源：评论

学校读者我要写书评

暂无评论

Compression damage identification of stitched 2D-C/SiC based on acoustic emission pattern recognition

引用

JOURNAL OF ASIAN CERAMIC SOCIETIES 2021年第3期9卷 1354-1363页

作者： Zhang, Yongzhen Tong, Xiaoyan Lyu, Peng Tan, Zhiyong Yao, Leijiang Li, Bin Northwestern Polytech Univ Natl Key Lab Sci & Technol Uav Xian 710072 Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian Peoples R China Xian Inst Space Radio Technol Xian Peoples R China Sci & Technol Space Phys Lab Beijing Peoples R China

Compression tests of stitched 2D-C/SiC at room temperature were implemented and monitored by acoustic emission (AE). The t-Distributed Stochastic Neighbor Embedding(t-SNE) and the ward-link hierarchical clustering (WLHC) algorithm were used to perform the pattern recognition of the AE data. AE signals were divided into four clusters and labeled by matrix cracking in fiber bundle, matrix cracking between fiber bundles, fiber cluster breakage, interlaminar delamination & friction respectively, according to their physical foundation. It is found: (1) the compression failure process of stitched C/SiC experiences the initial damage stage with a few low-energy AE signals, the damage development stage beginning with high-energy fiber cluster breakage, and the damage acceleration stage in which fiber cluster breakage and interlaminar damage develop rapidly;(2) the macroscopic fracture generally originates from the sparse location of the stitches, and the spacing and uniformity of the stitches have a great impact on the form of fiber breakage and the area of individual delamination damage rather than the direction of the stitches;(3) fiber cluster breakage and interlaminar delamination & friction are the main factors of stitched 2D-C/SiC compressive failure modes, the sum of the corresponding AE events accounts for about 60% of all AE events.

关键词： Stitched C/SIC acoustic emission compressive failure pattern recognition clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

An Energy Optimization clustering Scheme for Multi-Hop Underwater Acoustic Cooperative Sensor Networks

引用

IEEE ACCESS 2020年 8卷 89171-89184页

作者： Yu, Weijian Chen, Yougan Wan, Lei Zhang, Xiaokang Zhu, Peibin Xu, Xiaomei Xiamen Univ Minist Educ Key Lab Underwater Acoust Commun & Marine Informa Xiamen 361005 Peoples R China Xiamen Univ Coll Ocean & Earth Sci Xiamen 361102 Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen 518000 Peoples R China Xiamen Univ Sch Informat Xiamen 351105 Peoples R China

In this paper, we propose an improved energy optimization clustering algorithm (EOCA) for the multi-hop underwater acoustic cooperative sensor networks (UWA-CSN) due to the limited energy supply of the underwater sensor nodes. The proposed EOCA scheme considers multiple factors, such as the number of neighbor nodes, the residual energy of each node, the motion of the sensor nodes caused by the ocean currents, and the distance between the sink node and each underwater sensor node. All these factors are considered within the framework of the multi-hop cooperative communication. Moreover, in the proposed energy optimization scheme, we define a residual energy based maximum effective communication range (reE-MECR) according to the residual energy of each underwater sensor node, which can adaptively control the energy consumption of data transmission for each hop. Compared with the existing clustering scheme, the experimental results demonstrate that the proposed EOCA scheme can not only prolong the life time of the multi-hop UWA-CSN, but also keep a good communication and networking performance, including the package delivery ratio, the energy consumption efficiency, and the network coverage area. The proposed scheme can obtain both the clustering benefit and the cooperative communication benefit for the multi-hop UWA-SN in terms of energy saving in underwater environments.

关键词： Underwater acoustic networks cooperative communications clustering algorithm energy optimization

来源：评论

学校读者我要写书评

暂无评论

Research on clustering-Cobweb Hybrid algorithm of Low-voltage Power Line Based on Cluster Head Selection 14

Research on Clustering-Cobweb Hybrid Algorithm of Low-voltag...

引用

14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xu, Binghan Sun, Yunlian Wang, Huayou Zhang, Xiaodi Ye, Liangpeng Yi, Shimin Xie, Xinlin Fu, Bin Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China Guangdong Power Grid Co Ltd Guangzhou Peoples R China Guangdong Power Grid Co Ltd Qingyuan Power Supply Bur Qingyuan Peoples R China

ISBN: (纸本)9781538694909

Due to the complexity of communication channel and nodes' communication range limit, the gateway nodes in power line communication network cannot directly communicate with all the child nodes. It is necessary to adopt the networking technology. Firstly, the gateway node establishes communication link to some child nodes, then these sub-nodes act as relays and continue to network, gradually expanding the communication range until all nodes join the network. In order to improve the reliability of power line communication, this paper proposes an improved clustering-cobweb algorithm based on the study of clustering-cobweb algorithm and the topology characteristics of low-voltage power line network. The improved algorithm adds an optimization step that each new node chooses the best cluster head node when joining the network. It can be proved through simulation that comparing with the original algorithm, the improved algorithm optimizes the network's structure and improves the reliability of the power line communication by reducing the average communication distance.

关键词： PLC network optimization clustering algorithm artificial cobweb

来源：评论

学校读者我要写书评

暂无评论

clustering-based force-directed algorithms for 3D graph visualization

引用

JOURNAL OF SUPERCOMPUTING 2020年第12期76卷 9654-9715页

作者： Lu, Jiawei Si, Yain-Whar Univ Macau Dept Comp & Informat Sci Ave Univ Taipa Macau Peoples R China

Force-directed algorithm is one of the most commonly used methods for visualization of 2D graphs. These algorithms can be applied to a plethora of applications such as data visualization, social network analysis, crypto-currency transactions, and wireless sensor networks. Due to their effectiveness in visualization of topological data, various force-directed algorithms for 2D graphs were proposed in recent years. Although force-directed algorithms for 2D graphs were extensively investigated in research community, the algorithms for 3D graph visualization were rarely reported in the literature. In this paper, we propose four novel clustering-based force-directed (CFD) algorithms for visualization of 3D graphs. By using clustering algorithms, we divide a large graph into many smaller graphs so that they can be effectively processed by force-directed algorithms. In addition, weights are also introduced to further enhance the calculation for clusters. The proposed CFD algorithms are tested on 3 datasets with varying numbers of nodes. The experimental results show that proposed algorithms can significantly reduce edge crossings in visualization of large 3D graphs. The results also reveal that CFD algorithms can also reduce Kamada and Kawai energy and standardized variance of edge lengths in 3D graph visualization.

关键词： 3D graphs Visualization clustering algorithm Force-directed algorithm

来源：评论

学校读者我要写书评

暂无评论

Mining event logs for knowledge discovery based on adaptive efficient fuzzy Kohonen clustering network

引用

KNOWLEDGE-BASED SYSTEMS 2020年 209卷

作者： Pan, Yue Zhang, Limao Li, Zhiwu Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

As a digital representation of building projects, Building Information Modeling (BIM) can accumulate large volumes of log data containing hidden knowledge for deep exploration. However, such ever-increasing logs are likely to suffer from high complexity, inaccuracy, and uncertainty, which will inevitably raise challenges in uncovering latent and meaningful patterns. In order to yield satisfactory clustering quality and efficiency for better design process management, a novel clustering-based BIM event log mining approach is put forward in this paper. For one thing, a hybrid clustering algorithm named adaptive efficient fuzzy Kohonen clustering network (AEFKCN) is developed with a modified learning rate to accelerate the convergence. For another, a new clustering validity index (CVI) only relying on boundary points is designed to reduce computational complexity. An experiment is conducted in a 4 GB realistic BIM design event log dataset to validate the effectiveness of the proposed method. It begins from extracting a set of features associated with designers' engagement and efficiency and ends up retrieving inherent insights into the person's design behavioral patterns. Moreover, the cluster analysis can significantly distinguish the design productivity at different time periods into the high, medium, and low level, which presents a unique opportunity in understanding and assessing design productivity objectively. Practically, our method can support data-driven decision making for managers to strategically schedule personalized work for different designers, aiming to boost design efficiency and smooth the design process. (C) 2020 Elsevier B.V. All rights reserved.

关键词： clustering algorithm clustering validity index Event log mining Behavioral pattern discovery Decision making

来源：评论

学校读者我要写书评

暂无评论

Application of fuzzy C-means clustering method in the analysis of severe medical images

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期38卷 3635-3645页

作者： Ning, Yuwen Shi, Xiaoyuan Yin, Jingong Xie, Duowen Fourth Mil Med Univ Dept Mil Prevent Med Xian Shaanxi Peoples R China Lanzhou Univ Second Hosp Dept Med Record Lanzhou Gansu Peoples R China Lanzhou Univ Second Hosp Dept Intens Care Unit Med Record2 Lanzhou Gansu Peoples R China

Medical image processing is an interdisciplinary subject of integrated medical imaging, mathematics, computer science and other disciplines. With high spatial resolution, high signal-to-noise ratio and high resolution of soft tissue, the technology can accurately locate the target areas of interest in medical images, thus providing useful information for clinicians to formulate disease treatment plans. These techniques include digital subtraction angiography, magnetic resonance imaging, computed tomography, ultrasound imaging and positron emission tomography. The purpose of this paper is to study the application of fuzzy C-means clustering in image analysis of critical medicine. This paper discusses the classification effect, clustering process, iteration times and running time of different algorithms, and the segmentation effect of different algorithms. By designing parameters and carrying out simulation experiments, the traditional clustering algorithm and improved local adaptive method are compared, and the problem of long coding time of traditional image compression algorithm is solved. The simulation results under the same working environment show that the coding speed of the algorithm is about five times faster than that of the traditional image compression algorithm without affecting the signal-to-noise ratio and compression rate, which proves the superiority of the algorithm.

关键词： Medical image processing fuzzy C-means algorithm clustering algorithm medical image analysis

来源：评论

学校读者我要写书评

暂无评论

Research on parallel data processing of data mining platform in the background of cloud computing

引用

JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期30卷 479-486页

作者： Bu, Lingrui Zhang, Hui Xing, Haiyan Wu, Lijun Shandong Lab Vocat & Tech Coll Jinan 250022 Shandong Peoples R China

The efficient processing of large-scale data has very important practical value. In this study, a data mining platform based on Hadoop distributed file system was designed, and then K-means algorithm was improved with the idea of max-min distance. On Hadoop distributed file system platform, the parallelization was realized by MapReduce. Finally, the data processing effect of the algorithm was analyzed with Iris data set. The results showed that the parallel algorithm divided more correct samples than the traditional algorithm;in the single-machine environment, the parallel algorithm ran longer;in the face of large data sets, the traditional algorithm had insufficient memory, but the parallel algorithm completed the calculation task;the acceleration ratio of the parallel algorithm was raised with the expansion of cluster size and data set size, showing a good parallel effect. The experimental results verifies the reliability of parallel algorithm in big data processing, which makes some contributions to further improve the efficiency of data mining.

关键词： Cloud computing data mining parallel processing Hadoop platform clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

clustering algorithm for Heterogeneous Sensor Network Based on Energy Matrix

Clustering Algorithm for Heterogeneous Sensor Network Based ...

引用

International Conference on Measurement, Instrumentation and Automation (ICMIA 2012)

作者： Dang, Xiaochao Hao, Zhanjun Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9783037855461

Aiming at the energy heterogeneous wireless sensor network, this paper proposes This paper proposes the remaining energy prediction model based on energy matrix and a novel clustering algorithm. The Kalman filtering algorithm was introduced into the model, in which anode, according to energy prediction mechanism, independently makes it s decision to compete for becoming a master cluster head in order to improve energy efficiency. Simulation results show that the algorithm can balance the network energy consumption, improve the utilization efficiency of node energy and prolong the network lifetime.

关键词： wireless sensor network energy matrix clustering algorithm heterogeneous

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：