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clustering algorithm-based prediction model for athlete performance grading and injury risks

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Yu, Yang Changchun Univ Sports Teaching & Res Dept 6543 Weixing Rd Changchun 130022 Peoples R China

For athlete performance evaluation and injury risk prediction-which is increasingly crucial-traditional approaches find difficulty handling complex, multidimensional data. We introduce the PerfoRisk-KDB model to precisely estimate athlete performance and injury risk by combining K-means and DBSCAN clustering techniques. By combining these two clustering techniques, the idea of this work surpasses the constraints of a single technique and increases accuracy and robustness for complex and high-dimensional data. This work tests the performance assessment and injury risk prediction of a real athlete dataset against conventional models. Based on tests, the PerfoRisk-KDB model shows good performance on several evaluation criteria and shows good application possibilities.

关键词： clustering algorithm K-means DBSCAN performance grading injury risk prediction

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clustering algorithm for remote information transmission of IoT sensor nodes

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第4-5期24卷 2895-2907页

作者： Mei, Qian Zhang, Peng Si, Zhiyong Zhengzhou Railway Vocat & Tech Coll Big Data Management Ctr Informatizat Off Zhengzhou Peoples R China

Internet of Things development is of great significance for modern society progress. However, the limited information in some areas with incomplete infrastructure restricts Internet of Things development, so the long-distance information transmission task of sensor nodes needs to be put on the agenda. The research introduces beamforming technology for clustering wireless sensor nodes, and proposes a clustering algorithm based on wireless sensor node's energy consumption rate for nodes energy management to achieve remote information sharing and transmission. The results confirm that the success rate of clustering algorithm based on beamforming event triggering increases with node density increasing, and the success rate is infinitely close to 1. In addition, when the sensor node is 120, the average charging delay time based on machine learning energy consumption prediction is only 946 seconds, which is reduced by 521 seconds compared to the Mean-shift algorithm. When sensor node is 120, the algorithm has a successful access count of up to 1288 times. These two clustering algorithms have good clustering performance and significant practical application effects, providing reliable technical support for remote data transmission in the modern Internet of Things.

关键词： clustering algorithm sensor nodes remote transmission beamforming energy consumption prediction

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Low-Energy Resource Classification algorithm for Cross-Regional Cloud Data Centers Based on K-Means clustering algorithm

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第8期20卷 10084-10091页

作者： Liang, Bin Bai, Junqing Xian Shiyou Univ Sch Comp Sci Xian 710065 Peoples R China

As the division of labor in the industry becomes more refined, an increasing number of companies are abandoning infrastructure construction and instead moving their operations to cloud data centers (CBDCs). Cloud service providers are responding to the surge in demand by deploying their own CBDCs worldwide. However, the energy consumption and operation costs of these CBDCs vary depending on the region's environment and policies. To mitigate these costs, cloud service providers often employ resource management algorithms. This article conducts a comprehensive analysis of the cross-regional CBDC model, including establishing virtual machine classification rules based on clustering results. Ultimately, this article proposes a low-energy resource classification algorithm for cross-regional CBDCs based on the K-means clustering algorithm (LCKC). The effectiveness of the LCKC algorithm is compared to that of other algorithms, and the results indicate that it reduces energy consumption in cross-regional CBDCs.

关键词： Cloud computing clustering algorithms Energy consumption Costs Task analysis Data centers Computational modeling Cloud data centers (CBDC) clustering algorithm cross-regional energy consumption optimization resource classification

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Revealing critical failure laws of composite curved beams through network-free renormalization and clustering algorithm

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ENGINEERING STRUCTURES 2025年 330卷

作者： Shen, Zijie Zou, Xionghui Hu, Changfu Gao, Weicheng Liu, Wei Zhou, Guangchun Wu, Zhenyu Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China Harbin Inst Technol Dept Astronaut Sci & Mech Harbin 150001 Peoples R China Commercial Aircraft Corp China Ltd COMAC Shanghai Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Struct Dynam Behav & Control Minist Educ Harbin 150090 Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Smart Prevent & Mitigat Civil Engn Disaste Minist Ind & Informat Technol Harbin 150090 Peoples R China

Curved beams are irregular composite structures commonly used in aircraft and ships. The complexity of their material properties and irregular geometries make it challenging to define their failure accurately based on the failure phenomenon. Moreover, few full-size composite laminate curved beam tests have been reported in previous publications because of their high cost and difficulty. This work proposes a 3-order network-free renormalization method based on the thermodynamic-based failure definition to reveal the failure law of curved beams more accurately, which is suitable for characterizing the stressing state of full-size irregular composite structures. Applying 3-order network-free renormalization and clustering algorithms to eight full-size composite curved beams can reveal the elastoplastic branching (EPB), failure starting (FS), and progressive failure (PF) points. We can also use the phase transition definition of Wilson's theory to verify the stability of phase transition loads. Unlike failure loads based on buckling or fracture phenomena, phase transition loads in composite curved beams are based on catastrophes in the relative deformation distribution, which is more physically significant.

关键词： Composite curved beams Bending condition Network-free renormalization clustering algorithm Phase transition loads

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Memory Priority Scheduling algorithm for Cloud Data Center Based on Machine Learning Dynamic clustering algorithm

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第4期21卷 3485-3492页

作者： Liang, Bin Wu, Di Shiyou Univ Sch Comp Sci Xian 710065 Peoples R China Shaanxi Univ Sci & Technol Haojing Coll Xian 712046 Peoples R China

As the cloud data center (CDC) landscape continues to broaden, CDC resource utilization as the benchmark for assessing scheduling methodologies. Concurrently enhancing both CPU and memory utilization stands as a paramount priority. However, prevalent algorithms tend to solely prioritize CPU utilization while neglecting memory efficiency, ultimately escalating energy expenditure. This article initiates by conducting a comprehensive examination of memory utilization repercussions on CDCs. Subsequently, it facilitates the dynamic clustering of physical machines and virtual machine deployments, ensuring a balanced utilization profile. Furthermore, it introduces the memory priority scheduling algorithm for CDC based on machine learning dynamic clustering algorithm (PMPD). Comparative evaluations against other algorithms underscore the prowess of PMPD in concurrently optimizing CPU and memory utilization, thereby minimizing the number of active PMs and diminishing energy consumption of CDCs.

关键词： Virtual machines clustering algorithms Heuristic algorithms Resource management Energy consumption Scheduling algorithms Data centers Cloud computing Prediction algorithms Machine learning algorithms Cloud data center (CDC) clustering algorithm machine learning virtual machine (VM)

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Detecting and classifying multiple track defects using clustering algorithm

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ENGINEERING STRUCTURES 2025年 334卷

作者： Vishwakarma, Abhilash Dasgupta, Anirvan Racherla, Vikranth Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 West Bengal India

This work proposes an algorithm to detect and classify railway track defects by analysing the axle-box acceleration in the vertical direction. The axle-box acceleration is generated by using an experimentally validated vehicle track model. The vehicle-track model is built in the commercial multibody dynamics software SIMPACK. When the vehicle moves over the track defects, each defect excites a specific band of frequencies. To separate the track defects based on the frequency content the axle-box acceleration is passed through a filter bank to decompose it into its constituents. Mean and standard deviation are calculated for each decomposed signal to form a feature matrix. On the feature matrix, principal component analysis is performed to extract the orthogonal features and select the dominant features. On these dominant features, density-based spatial clustering of application with noise (DBSCAN) is applied to group the data points into defective and perfect clusters. The effect of vehicle speed, axle load, defect features, and epsilon value is analysed for the defect detection algorithm. The track defects are classified by applying the DBSCAN algorithm on selected decomposed signals. The proposed algorithm is found to accurately detect and locate the track defects without being affected by the external parameters.

关键词： Track defects Filter bank clustering algorithm

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Characterization of Vertical Wind Speed Profiles Based on Ward's Agglomerative clustering algorithm

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1437-1449页

作者： M.C.Bueso A.Molina-García A.P.Ramallo-González A.Fernández-Guillamón Department of Applied Mathematics and Statistics Universidad Politécnica de Cartagena30202 CartagenaSpain Department of Automatics Electrical Engineering and Electronic TechnologyUniversidad Politécnica de Cartagena30202 CartagenaSpain Department of Information and Communications Engineering Computer Science FacultyUniversity of Murcia30100 MurciaSpain Universidad de Castilla La-Mancha Campus Universitario02071 AlbaceteSpain

Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee(FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

关键词： clustering algorithm wind speed data management power generation

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A ship trajectory clustering algorithm based on segmentation direction

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OCEAN ENGINEERING 2024年第Part1期313卷

作者： Zhang, Haoda Li, Weifeng Shi, Guoyou Desrosiers, Robert Wang, Xinjian Dalian Maritime Univ Nav Coll Dalian Peoples R China US Coast Guard Natl Maritime Ctr Martinsburg WV USA

With the increase of ship AIS data. Through AIS data mining algorithm, it is possible to extract guidelines for the best path of a specific water area by using trajectory segment clustering. The defects of the current trajectory segment clustering algorithm are mainly reflected in: the lack of direction of the path after clustering;For trajectory segment clustering, the whole trajectory is considered rather than a single trajectory segment. In this paper, the trajectory direction and density are used as a measure of similarity between trajectories, and the trajectory segment clustering algorithm is used to analyze the compressed ship trajectory segment data. The first step is to eliminate clusters that contain too few trajectories, and clusters that have too small a distance value between trajectories. For two nearby clusters in opposite directions, the Hausdorff distance is determined. When the distance is less than the threshold, the trajectory is considered to be a two-way route. Finally, the trajectory clusters after clustering are fused to form a view of the overall traffic flow frame of ships in the water area. The framework can describe the main driving direction of the ship in the water and provide decision-making suggestions for the driver's path planning.

关键词： clustering algorithm Ship traffic flow Ship routing Maritime domain awareness Trajectory segment

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Data Collection in Laser-Powered UAV-Assisted IoT Networks: Phased Scheme Design Based on Improved clustering algorithm

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2024年第1期8卷 482-497页

作者： Li, Dongji Xu, Shaoyi Zhao, Chengyu Wang, Yuanjie Xu, Rongtao Ai, Bo Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing 100029 Peoples R China Zhengzhou Univ Peng Cheng Lab Zhengzhou 450001 Peoples R China Zhengzhou Univ Henan Joint Int Res Lab Intelligent Networking & D Zhengzhou 450001 Peoples R China

Owing to the striking features, such as controllable mobility, low cost, and so on, unmanned aerial vehicles (UAVs) are deemed to be the promising solution to complete data collection tasks of Internet of Things devices (IoTDs). The limited onboard energy, however, undeniably impedes the progress of collecting data. Furthermore, this task is complicated further due to the various amount of data generated by the different types of IoTDs. The goal of this paper is to design an applicable data collection scheme for IoT networks using a laser-powered UAV to maximize system energy efficiency. We propose an improved clustering algorithm called logarithm kernel-based mean shift (LKMS) inspired by the idea behind the mean shift algorithm. Based on the LKMS, we propose a novel algorithm to determine the optimal visiting order and enter points (EPs) of IoTD clusters, paving the way for the following optimization. To manage to solve the variables-coupling and non-convex formulated problem, we artificially divide the entire flying procedure into two phases, the flying and charging (FC) phase as well as the collecting data (CD) phase, depending on whether the UAV is harvesting energy. The block coordinate descent (BCD) and the successive convex approximation (SCA) methods are used to decouple the variables and solve the non-convex subproblems. Simulation results validate the effectiveness of our proposed scheme.

关键词： Data collection laser-powered UAV clustering algorithm trajectory optimization resource allocation

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A multi-process parallel clustering algorithm for resource reconfiguration in cloud manufacturing

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-21页

作者： Liang, Bo Gao, Jie Taiyuan Normal Univ 319 Daxue St Jinzhong 030619 Shanxi Peoples R China Shanxi Key Lab Intelligent Optimizat Comp & Blockc 319 Daxue St Jinzhong 030619 Shanxi Peoples R China Shanxi Vocat Univ Engn Sci & Technol 369 Wenhua St Jinzhong 030619 Shanxi Peoples R China

Resource reconfiguration can integrate and optimize manufacturing resources (MRs) into various resource sets, enhancing overall resource management in cloud manufacturing (CM). However, achieving efficient resource reconfiguration remains difficult due to the large data volumes, heterogeneity, and implicit relationships among MRs in CM. To improve the practicality of MR reconfiguration in cloud manufacturing (CM), a new multi-process parallel clustering algorithm (MPPCA) is proposed to categorize the multi-source heterogeneous MRs into distinct resource sets based on their functional characteristics. MPPCA can divide MRs into multiple processes and cluster them parallel to form several subclasses. Finally, similar subclasses are merged, and MRs with implicit relationships are grouped into the same resource set. In order to ensure the accurate calculation of the subclass radius during the merging process, a skewness indicator is introduced. The indicator is used to evaluate the irregular shape of the data set during the clustering process to ensure that the subclasses after division are quasi-circular. Additionally, merge criteria are proposed to merge similar resource subclasses effectively, exploring implicit relationships between MRs. The feasibility of the proposed method is validated through experiments using MR data from laboratories and artificial datasets, demonstrating that MPPCA successfully achieves resource reconfiguration.

关键词： Cloud manufacturing Resources reconfiguration clustering algorithm

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