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3rd International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Zhang, Taomei Chengdu Technological University Sichuan Province Chengdu611730 China

ISBN: (纸本)9798350376173

This paper introduces the method of cluster analysis, and explains the characteristics of ecological economic data through the results of cluster analysis. The k-means algorithm is used to cluster ecological and economic data, and the distribution of ecological and economic data is visually displayed by drawing the K-Means cluster analysis chart. On this basis, the relationship between ecological and economic data and other indicators such as population, GDP and energy is further analyzed. The visualization of ecological economic data through cluster analysis can show its distribution and interrelationship more intuitively, which is helpful for people to understand ecological economic data. The results show that cluster analysis is feasible and practical to interpret and analyze ecological economic data effectively, and can provide valuable reference for ecological economic research. © 2024 IEEE.

关键词： clustering algorithm data visualization ecological economic data

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The computer desktop image compression based on clustering algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第13期34卷 e5892-e5892页

作者： Gui, Dawei Shanxi Business Coll Inst Informat & Intelligent Technol Xian 710119 Shanxi Peoples R China

With the rapid development of computer software and hardware technology and network technology, aiming at the limitations of the traditional image compression standards and the deficiencies of the existing computer desktop image compression methods. By analyzing the characteristics of computer desktop image, according to the characteristics of desktop compression, a compression scheme based on high efficiency video coding (HEVC) and color clustering is proposed, which divides blocks into text/graphics blocks, natural image blocks and mixed blocks based on the features of histogram information and texture information of blocks. In block division and classification, an adaptive dynamic block classification algorithm is proposed which is different from the traditional block partitioning. Compared with the traditional method, the new block classification algorithm can save the code stream and improve the classification accuracy.

关键词： clustering algorithm color clustering compression scheme computer image compression

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On an improved clustering algorithm based on node density for WSN routing protocol

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第4期25卷 3005-3017页

作者： Chang, Luyao Li, Fan Niu, Xinzheng Zhu, Jiahui Univ Elect Sci & Technol China Comp Sci & Engn Chengdu Sichuan Peoples R China

To better collect data in context to balance energy consumption, wireless sensor networks (WSN) need to be divided into clusters. The division of clusters makes the network become a hierarchical organizational structure, which plays the role of balancing the network load and prolonging the life cycle of the system. In clustering routing algorithm, the pros and cons of clustering algorithm directly affect the result of cluster division. In this paper, an algorithm for selecting cluster heads based on node distribution density and allocating remaining nodes is proposed for the defects of cluster head random election and uneven clustering in the traditional LEACH protocol clustering algorithm in WSN. Experiments show that the algorithm can realize the rapid selection of cluster heads and division of clusters, which is effective for node clustering and is conducive to equalizing energy consumption.

关键词： clustering algorithm LEACH protocol WSN

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An Optimal clustering algorithm for Second Use of Retired EV Batteries Using DBSCAN and PCA Schemes Considering Performance Deviation 38

An Optimal Clustering Algorithm for Second Use of Retired EV...

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IEEE Applied Power Electronics Conference and Exposition (APEC)

作者： Lim, Jeyeong Han, Eui-Seong Kim, Dong Hwan Lee, Byoung Kuk Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9781665475396

This paper proposes an optimal clustering algorithm considering performance deviation of parameters and data preprocessing method for reusing retired batteries. The proposed method regroups batteries by considering the density and performance deviation of the retired battery dataset through a clustering algorithm using density-based spatial clustering of applications with noise (DBSCAN). Additionally, the performance of the algorithm was improved through data preprocessing using a principal component analysis (PCA) that prevents the computational complexity and overfitting of clustering algorithm. The feasibility of the proposed algorithm is verified by comparing with general clustering algorithms such as the k-means clustering and Gaussian mixture model.

关键词： battery management system (BMS) second use battery clustering algorithm

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Research on load management strategies based on Bi-kmeans clustering algorithm 3

Research on load management strategies based on Bi-kmeans cl...

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3rd International Conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Zhang, Siwen Zhang, Bo Bai, Yunlong Zhang, Yuzhuo Peng, Wu North China Elecric Power University NCEPU Beijing China State Grid ANHUI Electric Power Co. Ltd Anhui China State Grid Energy Research Institute Co. Ltd Beijing China

ISBN: (纸本)9798350352658

Aiming at the current problem that the formulation of orderly power consumption program does not fully consider the differences in user load characteristics, a load management strategy based on load time series clustering is proposed. Firstly, the typical load curve pattern of a single user is extracted;then the load time series data of different users are clustered by Bikmeans clustering algorithm, so as to divide multiple users into different types of electricity consumption;finally, the load characteristics of different types of users are formulated according to their load characteristics. Finally, load management strategies are formulated according to the load characteristics of different types of users. The actual load data of a regional power grid is analyzed as an example. The results show that the proposed method can more accurately categorize users with different load characteristics than the traditional method, thus more effectively guiding the orderly power consumption management strategy. © 2024 IEEE.

关键词： clustering algorithm load characteristics load management

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A Distance Metric for Uneven Clusters of Unsupervised K-Means clustering algorithm

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IEEE ACCESS 2022年 10卷 86286-86297页

作者： Raeisi, Mostafa Sesay, Abu B. Univ Calgary Dept Elect & Software Engn Calgary AB T2N 1N4 Canada

In this paper, we propose a new distance metric for the K-means clustering algorithm. Applying this metric in clustering a dataset, forms unequal clusters. This metric leads to a larger size for a cluster with a centroid away from the origin, rather than a cluster closer to the origin. The proposed metric is based on the Canberra distances and it is useful for cases that require unequal size clusters. This metric can be used in connected autonomous vehicle wireless networks to classify mobile users such as pedestrians, cyclists, and vehicles. We use a combination of mathematical and exhaustive search to establish its validity as a true distance metric. We compare the K-Means algorithm using the proposed distance metric with five other distance metrics for comparison. These metrics include the Euclidean, Manhattan, Canberra, Chi-squared, and Clark distances. Simulation results depict the effectiveness of our proposed metric compared with the other distance metrics in both one-dimensional and two-dimensional randomly generated datasets. In this paper, we use three internal evaluation measures namely the Compactness, Sum of Squared Errors (SSE), and Silhouette measures. These measures are used to study the proper number of clusters for each of the K-Means algorithms and also select the best run among multiple centroid initializations. The elbow method and the local maximum approach are used alongside the evaluation measures to select the optimal number of clusters.

关键词： Measurement clustering algorithms Classification algorithms Partitioning algorithms Euclidean distance Road traffic Training data Unsupervised learning Canberra distance chi-squared distance clustering algorithm distance metrics Euclidean distance K-means algorithm unsupervised learning

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A Novel Fuzzy Distance-Based Minimum Spanning Tree clustering algorithm for Face Detection

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COGNITIVE COMPUTATION 2022年第4期14卷 1350-1361页

作者： Li, Yang Zhou, Wenju Shanghai Univ Sch Mech & Elect Engn & Automat Shanghai 200444 Peoples R China

Solving a clustering algorithm can usually be simplified into an optimization problem. Using relevant knowledge in graph theory, many optimization problems can be transformed into solving minimum spanning tree problems. Minimal spanning trees are also widely used in areas closely related to cognitive computing such as for face recognition by face cognition and gene data analysis by gene cognition. However, the minimum spanning tree has the shortcoming of the distance between neighbours because of which the minimum spanning tree algorithm cannot cluster unbalanced data. Thus, the face recognition rate is low, and facial expression cognition is difficult. In this paper, a minimum spanning tree algorithm based on fuzzy distance is proposed for the shortcomings of the minimum spanning tree (FCP). First, a relative neighbourhood distance measure is proposed by introducing neighbourhood rough set theory;the neighbourhood matrix is obtained based on the distance. Second, the minimum spanning tree is solved by the prim algorithm and the neighbourhood matrix. Finally, the minimum spanning tree is partitioned to realize clustering of the minimum spanning tree. In this paper, the UCI dataset and Olivetti face database are selected to verify the performance of the algorithm, and the algorithm is evaluated by three evaluation criteria. The experimental results show that the proposed algorithm can not only cluster data of any shape but also deal with unbalanced data containing noise points. Especially in face cognitive computing, the values of ACC, AMI, and ARI can reach 0.852, 0.843, and 0.782, respectively. In this study, the algorithm can obtain very good clustering results for data with good geometric structure, and the overall performance is better than other algorithms. In face recognition detection, the improved cognitive computing of faces makes it possible to accurately recognize different expressions from the same person.

关键词： Minimum spanning tree Fuzzy distance clustering algorithm Face recognition

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Maritime Traffic Route Detection Framework Based on Statistical Density Analysis From AIS Data Using a clustering algorithm

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IEEE ACCESS 2022年 10卷 23355-23366页

作者： Lee, Jeong-Seok Lee, Hyeong-Tak Cho, Ik-Soon Korea Maritime & Ocean Univ Grad Sch Busan 49112 South Korea Korea Maritime & Ocean Univ Ocean Sci & Technol Sch Busan 49112 South Korea Korea Maritime & Ocean Univ Div Nav Convergence Studies Busan 49112 South Korea

Maritime traffic routes by ships navigation vary according to country and geographic characteristics, and they differ according to the characteristics of the ships. In ocean areas adjacent to coasts, regulated routes are present, e.g., traffic separation scheme for ships entering and leaving;however, most ocean areas do not have such routes. Maritime traffic route research has been conducted based on computer engineering to create routes;however, ship characteristics were not considered. Thus, this article proposes a framework to generate maritime traffic routes using statistical density analysis. Here, automatic identification system (AIS) data are used to derive quantitative traffic routes. Preprocessing is applied to the AIS data, and a similar ship trajectory pattern is decomposed into a matrix based on the Hausdorff-distance algorithm and then stored in a database. A similar pattern makes the AIS trajectory simple using the Douglas-Peucker algorithm. In addition, density-based spatial clustering of applications with noise (DBSCAN) is performed to identify the waypoints of vessels then create routes by connecting waypoints. The width of maritime routes created based on a similar ship trajectory is subjected to kernel density estimation analysis (KDE). Then, waypoints evaluation of the main route is performed from the results of KDE 75% and 90% considering the statistical in the total maritime traffic, and the results applied to the targeted ocean area are compared. Finally, the result of KDE 90% of maritime traffic with framework analyzed the safety route, which can be a basis for developing routes for maritime autonomous surface ships.

关键词： Marine vehicles Artificial intelligence Trajectory Oceans clustering algorithms Navigation Safety AIS data clustering algorithm DBSCAN kernel density estimation framework maritime traffic route

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Behavior feature extraction method of college students' social network in sports field based on clustering algorithm

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JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 477-488页

作者： Wang, Yonggang Sun, Haiou Northeast Agr Univ Phys Educ Teaching & Res Dept Harbin 150000 Peoples R China

In order to improve the integrity of the social network behavior feature extraction results for sports college students, this study proposes to be based on the clustering algorithm. This study analyzes the social network information dissemination mechanism in the field of college students' sports, obtains the real-time social behavior data in the network environment combined with the analysis results, and processes the obtained social network behavior data from two aspects of data cleaning and de-duplication. Using clustering algorithm to determine the type of social network user behavior, setting the characteristics of social network behavior attributes, and finally through quantitative and standardized processing, get the results of college students' sports field social network behavior characteristics extraction. The experimental results showed that the completeness of the method feature extraction results improved to 9.93%, and the average extraction time cost was 0.344 s, with high result integrity and obvious advantages in the extraction speed.

关键词： clustering algorithm the sports field of college students social behavior network behavior feature extraction

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Vehicle clustering algorithm based on Reliable Node Screening 35

Vehicle Clustering Algorithm based on Reliable Node Screenin...

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35th Chinese Control and Decision Conference (CCDC)

作者： Bao, Nan He, Jianming Sun, Xixia Pan, Su Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing Peoples R China

ISBN: (纸本)9798350334722

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 paper. The algorithm screened the neighbor nodes according to the vehicle direction and the vehicle link survival time, and formed a list of reliable nodes. Furthermore, a cluster head election method was proposed based on node reliability degree. Through 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

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