检索结果-内蒙古大学图书馆

Analysis and Research on Color Encoding and K-means clustering algorithm in Lingnan Landscape Design

Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Tan, Hongyan School of Fine Arts and Design Zhanjiang University of Science and Technology Guangdong Zhanjiang524000 China

As a prominent visual phenomenon, the color environment influences the landscape design of the Lingnan region in three aspects: shape, color, and texture. The purpose of this paper is to examine the landscape nature of color by analyzing the psychological perception characteristics of color and the evolution of Lingnan's color landscape system. We propose color clustering extraction methods for both single and batch Lingnan landscape images, combining them with color space conversion algorithms like RGB and YIQ to extract colors and segment Lingnan landscape images. We look at the performance parameters of various reconstructed images at various compression ratios. We also draw the performance convergence curves of the clustering algorithm, the JPEG, and the JPEG2000 algorithms to talk about how well they code. Taking the Lingnan region (Guangzhou) as the main object of analysis, we divide the regional color qualities and analyze the evolution and development of landscape color characteristics in the Lingnan region. Most of the Lingnan architectural landscapes have color brightness values in the medium-high range of 5-7. For wall color brightness, most of the brightness class level values are in the high brightness range of 7-10, followed by the medium brightness range of 4-7, and a small portion is in the low brightness range of 1-3. © 2024 Hongyan Tan, published by Sciendo.

关键词： clustering algorithm Color coding Color space transformation Lingnan landscape

来源：评论

学校读者我要写书评

暂无评论

Resource clustering algorithm for cloud data centers

引用

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第5期21卷 1575-1585页

作者： Song, Caili Liang, Bin Li, Jiao Xian Shiyou Univ Comp Sch Xian 710065 Shaanxi Peoples R China

Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider's earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine's use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine's use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine's use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent.

关键词： Cloud data centers clustering algorithm Machine learning Virtual machine deployment

来源：评论

学校读者我要写书评

暂无评论

Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation

引用

JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期30卷 1014-1025页

作者： Chen, Zhuoran Cong, Biao Hua, Zhenxing Cengiz, Korhan Shabaz, Mohammad Jilin Normal Univ Boda Coll Sch Comp & Informat Sci Siping City Peoples R China Jilin Normal Univ Inst Comp Sci Siping City Peoples R China Trakya Univ Dept Elect Elect Engn Edirne Turkey Arba Minch Univ Arba Minch Ethiopia

In synthetic aperture radar (SAR) image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution (OEA) algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex.

关键词： clustering algorithm farmland SAR image segmentation regional algorithms noise interference

来源：评论

学校读者我要写书评

暂无评论

A split-merge clustering algorithm based on the k-nearest neighbor graph

引用

INFORMATION SYSTEMS 2023年 111卷

作者： Wang, Yan Ma, Yan Huang, Hui Wang, Bin Acharjya, Debi Prasanna Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 India

Numerous graph-based clustering algorithms relying on k-nearest neighbor (KNN) have been proposed. However, the performance of these algorithms tends to be affected by many factors such as cluster shape, cluster density and outliers. To address these issues, we present a split-merge clustering algorithm based on the KNN graph (SMKNN), which is based on the idea that two adjacent clusters can be merged if the data points located in the connection layers of the two clusters tend to be consistent in distribution. In Stage 1, a KNN graph is constructed. In Stage 2, the subgraphs are obtained by removing the pivot points from the KNN graph, in which the pivot points are determined by the size of local distance ratio of data points. In Stage 3, the adjacent cluster pairs satisfying the maximum similarity are merged, in which the similarity measure of two clusters is designed with two concepts including external connection edges and internal connection edges. By the experiments on ten synthetic data sets and eight real data sets, we compared SMKNN with two traditional algorithms, two density-based algorithms, nine graph-based algorithms and four neural network based algorithms in accuracy. The experimental results demonstrate a good performance of the proposed clustering method. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： K -nearest neighbor clustering algorithm Split Merge Similarity measure

来源：评论

学校读者我要写书评

暂无评论

A ReCon-BCALoD clustering algorithm for field monitoring data of marine structures

引用

APPLIED OCEAN RESEARCH 2023年第1期138卷

作者： Lyu, Baicheng Li, Da Wu, Wenhua Li, Hui CNOOC Res Inst Engn Res & Design Dept Beijing 100028 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Res Inst Dalian Univ Technol Shenzhen Shenzhen 518057 Peoples R China

Monitoring of the extreme marine environmental loads and hazardous dynamic responses of marine structures is critical to maintaining safe field operations. Field monitoring data from marine structures are high-dimensional with high aggregation and weak modes. Furthermore, monitoring data accumulates rapidly over time, which makes it difficult for conventional clustering algorithms to accurately identify the extreme environmental loads and hazardous response conditions experienced by marine structures during their service life. Cluster analysis is an unsupervised learning algorithm widely used in the fields of machine learning and information recognition. Currently, clustering algorithms are mainly used to calculate and analyze large data clusters, but few studies have focused on small clusters. Therefore, this study proposes a ReCon-BCALoD clustering algorithm suitable for longterm monitoring data of marine structures. The ReCon-BCALoD algorithm consists of up and down processes in which the data are clustered based on the ReCon local densities of data points. Applied to field monitoring data, the ReCon-BCALoD clustering algorithm proved capable of automatically determining cluster quantity and effectively identifying small data clusters. This showed that the ReCon-BCALoD clustering algorithm can be used to identify extreme current profile environment conditions and hazardous response conditions of a soft yoke mooring system. These clustering results can be used to improve the safety of in-service structures and provide valid references for future designs of marine structures.

关键词： Field monitoring clustering algorithm Current profile Soft yoke mooring system

来源：评论

学校读者我要写书评

暂无评论

Research on K-means clustering algorithm Based on MapReduce Distributed Programming Framework

引用

Procedia Computer Science 2023年 228卷 262-270页

作者： Ling Zhang Liaoning Geology Engineering Vocational College Dandong China

As a classical clustering algorithm, K-means algorithm has a profound research background. In the of big data era, K-means algorithms will play a greater advantage, being able to quickly divide similar data into the same cluster. Combining K-means algorithm with MapReduce distributed computing framework and running on Hadoop big data platform can significantly improve the clustering effect. Based on MapReduce framework structure, this paper studies K-means model, including K-means principle, distance calculation, content validity index and external validity index. On this basis, the K-means clustering flow based on MapReduce big data programming framework is proposed, and the execution process of the algorithm flow is described in detail, which provides a guide for the algorithm implementation.

关键词： Mapreduce Distributed Programming Framework K-Means clustering algorithm algorithm Process

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of clustering algorithm and improved algorithm application in shipping congestion

Comparative analysis of clustering algorithm and improved al...

引用

作者： Wang Liyang Jilin University Software Institute

In recent years, due to the lack of reasonable planning of ship operation routes,shipping congestion accidents have been increasing, seriously restricting port development and channel operation, posing a great threat to ship navigation safety and greatly restricting the development prospects of the shipping industry. This paper aims to compare and analyze the application of the clustering algorithm and its improved algorithm in solving port and waterway congestion problems. By analyzing the literature on the use of clustering algorithms to solve shipping congestion problems, and comparing the advantages and disadvantages of multiple clustering algorithms in solving problems, this paper concludes that Partition-based Methods are more suitable for identifying port congestion and the improved Fuzzy DBSCAN algorithm for identifying channel congestion. The research in this paper will help to select clustering algorithms when solving shipping congestion problems in the future.

关键词： Channel Congestion Port Congestion clustering algorithm DBSCAN K-Means

来源：评论

学校读者我要写书评

暂无评论

An Efficient Weighted Fuzzy Possibilistic C-Means clustering algorithm 23

An Efficient Weighted Fuzzy Possibilistic C-Means Clustering...

引用

Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Shuang Xie Haiyan Yu School of Communications and Information Engineering Xi'an University of Posts & Telecommunications China

ISBN: (纸本)9798400707674

The fuzzy possibilistic c-means clustering (FPCM) algorithm is a hybrid clustering algorithm by incorporating fuzzy memberships and possibilistic memberships into the objective function, which can improve the anti-noise ability of the fuzzy c-means clustering (FCM) and the coincident clustering phenomenon of the possibilistic c-means clustering (PCM) algorithm to some extent. However, the FPCM algorithm often obtains poor results for datasets injected with strong long-distance noise, resulting from that these noise points are assigned with too large values for fuzzy memberships. Therefore, this paper introduces a weight parameter into the objective function of the FPCM and proposes an efficient weighted fuzzy possibilistic c-means clustering (EWFPCM) algorithm. The experimental results on several synthetic datasets and color image segmentation show that the proposed EWFPCM algorithm can greatly improve the anti-noise ability of the FPCM, and performs best among compared several clustering algorithms.

关键词： clustering algorithm Fuzzy possibilistic c-means clustering Image segmentation Weight

来源：评论

学校读者我要写书评

暂无评论

An Improved K-PROTOTYPE clustering algorithm and Its Application 23

An Improved K-PROTOTYPE Clustering Algorithm and Its Applica...

引用

Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing

作者： Xin Jing Hao Gao School of Jeneral Education Shanghai Sanda University China SiEn (Qingdao) Integrated Circuits Co. Ltd. China

ISBN: (纸本)9798400709241

The K-means clustering algorithm can be used to process the dataset simply and quickly. In this paper, a weighted dissimilarity measurement formula has been proposed to address the issue of low accuracy and stability when using traditional K-prototype algorithms for clustering. By calculating the aggregation degree of data attributes, the measurement distance between samples has been weighted and calculated. And the maximum-minimum distance method can be used to generate the initial clustering center, avoiding the possibility of randomly produced initial clustering points causing the clustering results to fall into local optimal results. The contour coefficient method was used to select appropriate K values for clustering, which improved the clustering effect. The experimental test results show that the improved K-prototype algorithm has better clustering performance. In order to verify the effectiveness of the improved algorithm, a comparison test was conducted between the improved algorithm and other algorithms using a dataset from UCI. The results showed that the improved K-Prototype algorithm had better clustering performance. Using the improved K-Prototype algorithm to cluster student data and generate student profiles by analyzing the clustering results has certain promotion and application significance.

关键词： clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

An Improved Mean Shift clustering algorithm for LFA Detection 21

An Improved Mean Shift Clustering Algorithm for LFA Detectio...

引用

21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Sun, Wenyue Wang, Changda Jiangsu Univ Dept Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China

ISBN: (纸本)9781665478366

Link flooding attack (LFA) is considered as a new class of DDoS attacks. Traditional DDoS attacks against end nodes, while LFA focuses on target links in the network. LFA is difficult to detect because of large-scale legitimate low-speed traffic flow. In this paper, we propose an improved weighted Euclidean distance Mean Shift algorithm named TD-MS(Time-Delay Mean Shift) and the decision feature P-TI of the LFA detection based on Poisson process for TD-MS. The Weighted coefficient and the decision feature are obtained from the delay rate and the probability of the incremental LFA packet time, respectively. We constructed the software-defined-network(SDN) experimental environment to collect LFA packets through forwarding decisions made by the control plane on the data plane. We first cluster the network traffic flow based on the delay feature of network traffic flow while LFA happening, and then on which verify the existence of LFA according to the decision feature generated from such clustering results. The experimental results show that, the TD-MS algorithm outperforms the existing machine learning methods such as Decision Tree (DT) and Bernoulli Naive Bayesian classifier (BernoulliNB)in the light of both accuracy and efficiency.

关键词： SDN LFA clustering algorithm TD-MS

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：