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Research on Hybrid Data Clustering algorithm for Wireless Communication Intelligent Bracelets

MOBILE NETWORkS & APPLICATIONS 2023年第5期28卷 1762-1771页

作者： Sun, Jian-zhao Yang, kun Wozniak, Marcin Henan Inst Econ & Trade Sch Comp Engn Zhengzhou 450000 Henan Peoples R China Zhengzhou 7 Peoples Hosp Dept Med Zhengzhou 450000 Henan Peoples R China Silesian Tech Univ Fac Appl Math PL-44100 Gliwice Poland

Wireless communication smart bracelet data include motion data, sleep time data, heart rate and blood pressure data and positioning data, etc. These data have diversity and high complexity, and there are interconnections or interactions between the data, which have high clustering difficulty. To this end, a new data clustering algorithm is studied for wireless communication smart bracelets. The k-medoids algorithm is used to calculate the intra-cluster, inter-cluster, or overall similarity to complete the initial clustering of the bracelet data. Setting the clustering evaluation index can determine the optimal number of clusters. The data objects that are closely surrounded and relatively dispersed are selected as the initial clustering centers and combined with the new index IXB to complete the improvement of the data clustering algorithm. The test results show that the accuracy, recall, and F1 of the research algorithm for clustering the heart rate monitoring dataset, temperature monitoring dataset, energy consumption dataset, and sleep monitoring dataset are higher than 97%, which indicates that the data clustering effect of the algorithm is good.

关键词： Wireless communication smart bracelet k-medoids algorithm Internal evaluation index Clustering center

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Contextual data classification for a ubiquitous intelligent environment

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SN APPLIED SCIENCES 2020年第4期2卷 1-10页

作者： Yaici, Malika Sabri, Salima Azni, Wissam Boudjemil, Faiza Univ Bejaia Lab LTII Bejaia Algeria Univ Bejaia Lab LMI Bejaia Algeria Univ Bejaia Comp Dept Bejaia Algeria

Recognition of activities from sensors is a key paradigm of ubiquitous computing. Activity recognition systems can be used to label large sets of data. Variability in human activities, sensor deployment characteristics, and application domains has led to the development of best practices and methods to improve the robustness of activity recognition systems. Classification is one of the most important steps in making the recognition process more expressive and reducing uncertainty, thus minimizing representation. The k-medoid algorithm is simple but effective for grouping data according to the similarity that the samples present between them without the need to know each sample's membership class. In this paper, we propose a classification technique based on unsupervised partitioning algorithm, which allows recognizing activities and overcomes the problem of supervision.

关键词： Context data classification Activity recognition Clustering k-medoids algorithm

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Prediction of Instability Separation Modes and Its Application in Practical Dynamic Security Region

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Transactions of Tianjin University 2007年第3期13卷 157-162页

作者：曾沅余贻鑫王红梅闵亮张沛 LEES T Key Laboratory of Power System Simulation and Control of Ministry of Education Tianjin University Electrical Power Research Institute

The transient critical boundary of dynamic security region (DSR) can be approximated by a few hyper planes correlated with instability separation modes. A method to fast predict instability separation modes is proposed for DSR calculation in power injection space. The method identifies coherent generation groups by the developed k-medoids algorithm, taking a similarity matrix derived from the reachability Grammian as the index. As an experimental result, reachability Grammian matrices under local injections are approximately invariant. It indicates that the generator coherency identifications are nearly consistent for different injections. Then instability separation modes can be predicted at the normal operating point, while average initial acceleration is considered as the measure of the critical generator group to amend the error. Moreover, based on these predicted instability separation modes, a critical point search strategy for DSR calculation is illustrated in the reduced injection space of the critical generators. The proposed method was evaluated using New England Test System, and the computation accuracy and speed in determining the practical DSR were improved.

关键词： instability separation mode dynamic security region k-medoids algorithm reachability Grammian

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A new grading for easy and concise description of functional status after spinal cord lesions

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SPINAL CORD 2012年第1期50卷 42-50页

作者： Bluvshtein, V. Front, L. Itzkovich, M. Benjamini, Y. Galili, T. Gelernter, I. Aidinoff, E. Hart, J. Tesio, L. Biering-Sorensen, F. Weeks, C. Laramee, M. T. Craven, C. Hitzig, S. L. Glaser, E. Zeilig, G. Aito, S. Scivoletto, G. Mecci, M. Chadwick, R. J. El Masry, W. S. Osman, A. Glass, C. A. Silva, P. Soni, B. M. Gardner, B. P. Savic, G. Bergstroem, E. M. Catz, A. Loewenstein Hosp & Rehabil Ctr Dept Spinal Rehabil & Adm IL-43100 Raanana Israel Tel Aviv Univ IL-69978 Tel Aviv Israel Univ Milan IRCCS Dept Phys Med & Rehabil Ist Auxol Italiano Milan Italy Univ Copenhagen Rigshosp Ctr Neurosci Clin Spinal Cord Injuries DK-2100 Copenhagen Denmark GF Strong Spinal Cord Injury Program Vancouver BC Canada Inst Readaptat Gingras Lindsay De Montreal CRIR Montreal PQ Canada Toronto Rehab Spinal Cord Rehabil Program Toronto ON Canada Neurol Rehab Ctr Greifswald Germany Chaim Sheba Med Ctr IL-52621 Tel Hashomer Israel Careggi Hosp Spinal Unit Florence Italy Univ Roma Tor Vergata Spinal Cord Unit IRCCS S Lucia Fdn PhD Sch Adv Technol Rehabil Med Rome Italy N England Spinal Cord Injuries Ctr Middlesbrough Cleveland England Robert Jones & Agnes Hunt Orthopaed Hosp Midlands Ctr Spinal Injuries Oswestry SY10 7AG Shrops England NW Reg Spinal Injuries Ctr Southport Qld Australia Stoke Mandeville Hosp Natl Spinal Injuries Ctr Aylesbury HP21 8AL Bucks England

Background: Disability scales do not enable the transmission of concise, meaningful and daily function description for clinical purposes. Study design: Cross-sectional statistical analysis of 328 patients' Spinal Cord Independence Measure (SCIM) III item scores (SIS). Objective: To develop a concise and clinically interpretable data-based characterization of daily task accomplishment for patients with spinal cord lesions (SCLs). Setting: Multi-center study at 13 spinal units in 6 countries. Methods: Patients were grouped into clusters characterized by smaller differences between the patients' SIS within the clusters than between their centers, using the k-medoides algorithm. The number of clusters (k) was chosen according to the percent of SIS variation they explained and the clinical distinction between them. Results: Analysis showed that k=8 SIS clusters offer a good description of the patient population. The eight functional clusters were designated as A-H, each cluster (grade) representing a combination of task accomplishments. Higher grades were usually (but not always) associated with patients implementing more difficult tasks. Throughout rehabilitation, the patients' functional grade improved and the distribution of patients with similar functional grades within the total SCIM III score deciles remained stable. Conclusions: A new classification based on SIS clusters enables a concise description of overall functioning and task accomplishment distribution in patients with SCL. A software tool is used to identify the patients' functional grade. Findings support the stability and utility of the grades for characterizing the patients' functional status. Spinal Cord (2012) 50, 42-50;doi:10.1038/sc.2011.84;published online 2 August 2011

关键词： spinal cord injury spinal cord lesion SCIM classification k-medoids algorithm multi-center study

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Evaluation of driving risk at different speeds

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INSURANCE MATHEMATICS & ECONOMICS 2019年 88卷 108-119页

作者： Gao, Guangyuan Wathrich, Mario V. Yang, Hanfang Renmin Univ China Ctr Appl Stat Beijing 100872 Peoples R China Renmin Univ China Sch Stat Beijing 100872 Peoples R China Swiss Fed Inst Technol Dept Math Risk Lab CH-8092 Zurich Switzerland

Telematics car driving data describes drivers' driving characteristics. This paper studies the driving characteristics at different speeds and their predictive power for claims frequency modeling. We first extract covariates from telematics car driving data using k-medoids clustering and principal components analysis. These telematics covariates are then used as explanatory variables for claims frequency modeling, in which we analyze their predictive power. Moreover, we use these telematics covariates to challenge the classical covariates usually used in practice. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Telematics data v-a heatmap k-medoids algorithm Principal components analysis Generalized additive model Variable selection Poisson regression Claims frequency modeling

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Skewness and access kurtosis as denoised mixed features-based k-medoids for cooperative spectrum sensing

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PHYSICAL COMMUNICATION 2022年 54卷

作者： Radhi, Ali A. Akkar, Hanan A. R. Abdullah, Hikmat N. Univ Technol Baghdad Dept Elect Engn Baghdad Iraq Al Nahrain Univ Dept Informat & Commun Engn Baghdad Iraq

A traditional cooperative spectrum sensing system based on the energy features of random matrix theory does not function well when the signal-to-noise ratio is low in the Gaussian noise channel. In this paper, the poor performance of this system is improved. A novel blind cooperative spectrum sensing scheme based on new denoised mixed-features exploiting the k-medoids algorithm has been developed. In the first place, a new mathematical formula is proposed to extract denoised mixed -features and decrease the amount of noise in the received signals of each secondary user. This formula is expressed as the square root of the sum of the squares of the third and fourth-highest order moments of skewness (S-k) and access kurtosis (k-u-3). In the fusion center, two new denoised mixed-features methods are proposed. The first method is based on calculating the Average values of S-k and k-u-3 of the denoised mixed features vectors, we named it the ASkU3 method. The second method is based on calculating the Maximum values of S-k and k-u-3 of the denoised mixed features vectors, we named it the MSkU3 method. In comparison to the existing energy feature-based methods, the denoised mixed-features-based on highest-order moments avoid the computational complexity associated with threshold estimation. Furthermore, they mitigate the effect of noise uncertainty and improve sensing performance by reducing the overlap between clusters of the k-medoids algorithm. Then, the k-medoids method makes use of the newly formed methods in order to train the classifier effectively depending on whether the primary user is present or not. Finally, several energy-feature-based methods classified by the clustering algorithm are compared in this study. Moreover, the theoretical and practical performances of the denoised mixed-features methods, ASkU3 and MSkU3, are very well matched. The simulation findings show that the proposed methods have improved the effectiveness of sensing at low signal-to-noise ra

关键词： Skewness and kurtosis moments Denoised mixed features k-medoids algorithm

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Identifying and Optimizing Relocation Regions in One-Way Carsharing System 20

Identifying and Optimizing Relocation Regions in One-Way Car...

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20th COTA International Conference of Transportation Professionals (CICTP) - Advanced Transportation, Enhanced Connection

作者： Li, kunyun Chen, Xiaohong Cai, Xi Tongji Univ Coll Transportat Engn Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Rd Shanghai 201804 Peoples R China Shanghai SAIC Mobil Technol & Serv Co Ltd China Overseas Int Ctr 15 FTower BSouth Huangpi Rd Shanghai 200025 Peoples R China

ISBN: (数字)9780784482933

ISBN: (纸本)9780784482933

The imbalance between vehicle supply and user demand in the spatial-temporal dimension is the most relevant problem in a station-based one-way carsharing system (OWCS). To compensate for this disequilibrium, vehicle relocations are highly important. Previous studies have proposed the simulation-optimization model for relocation operations. Little attention has been paid to the clustering problem for intra-regional relocations in OWCS. Based on the k-medoids algorithm and the optimization theory, this paper proposed a two-stage clustering algorithm of dynamically dividing all OWCS stations into a certain number of economic relocation regions, so that there is operational feasibility for dispatchers and a balance between supply and demand within those relocation regions. In the case study, the algorithm has been tested on the largest electric vehicle Car-sharing corporation, EVCARD, in Jiading district, Shanghai. Stations are divided into 15 relocation regions. Results show that it is feasible for dispatchers in management. It also indicates that the supply of each region is greater than the demand, and the dispatcher only needs to relocate vehicles within the region.

关键词： Carsharing system Vehicle relocation k-medoids algorithm The optimization model

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Genetic algorithm and Simulated Annealing based Approaches to Categorical Data Clustering

Genetic Algorithm and Simulated Annealing based Approaches t...

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IEEE Region 10 Colloquium/3rd International Conference on Industrial and Information Systems

作者： Saha, Indrajit Mukhopadhyay, Anirban Tech Acad Dept Informat Technol Adisaptagram 712121 W Bengal India Univ Kalyani Dept Comp Sci & Engn Kalyani W Bengal India

ISBN: (纸本)9781424428052

Categorical data clustering has been gaining significant attention from researchers, because most of the real life data sets are categorical in nature. In contrast to numerical domain, no natural ordering can be found among the elements of a categorical domain. Hence no inherent distance measure, like the Euclidean distance, would work to compute the distance between two categorical objects. In this article, genetic algorithm and simulated annealing based categorical data clustering algorithm has been proposed. The performance of the proposed algorithm has been compared with that of different well known categorical data clustering algorithms and demonstrated for a variety of artificial and real life categorical data sets.

关键词： Genetic algorithm based Clustering Simulated Annealing based Clustering k-medoids algorithm Minkowski Score Wilcoxons rank sum test

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A MACHINE LEARNING APPROACH TO RE-CLASSIFICATION OF CLIMATE ZONES BASED ON MULTIPLE RAIN FEATURES OVER INDIA 39

A MACHINE LEARNING APPROACH TO RE-CLASSIFICATION OF CLIMATE ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Das, Saurabh Chatterjee, Chandrani Chakraborty, Swastika IIT Indore Discipline Astron Astrophys & Space Engn Khandwa Rd Indore 453552 India Sikkim Manipal Inst Technol Elect & Commun Engn Dept Rangpo Sikkim India

ISBN: (纸本)9781538691540

Climate change studies require a large number of parameters to be handled simultaneously and machine-learning techniques are now-a-days widely used to solve this kind of situation. One of the interesting problem in climate studies to identify the homogeneous regions for trend analysis as it is rather impractical to study the time series for each measurement points. The concept of spatial clustering is applied to the TRMM satellite data to improve the classification of homogeneous rain zone. Instead of relying on the rain rate only, a number of spatial rain features are included in re-classification of the regions. The present approach clearly improves the decadal trend detection as opposed to the existing climatology as evident from the preliminary results presented in this paper.

关键词： Clustering k-medoids algorithm Climate change Monsoon rain Indian region

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Application of Genetic algorithm for Logistics Based on Multi-Agent System

Application of Genetic Algorithm for Logistics Based on Mult...

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27th International Conference on Information Networking (ICOIN)

作者： Song, Ho kuen kim, kwang Myung kim, kyung Tae Youn, Hee Yong Sungkyunkwan Univ Coll Informat & Commun Engn Suwon South Korea

ISBN: (纸本)9781467357425;9781467357401

Logistics is a crucial ingredient in the operation of an organization. Here how to efficiently deliver the supplies to the consumers in the field under the given condition is the key issue. The general logistics system is usually too complex to be modeled mathematically, or the models are overly computation intensive to be applied in real-time environment. This paper proposes a new method for logistics based on multi-agent system using modified A* algorithm to find the best route between the agents. In addition, genetic algorithm and k-medoids algorithm are used for selecting and clustering the agents to maximize the throughput of logistics. Computer simulation verifies the efficiency of the proposed scheme for a complicated application domain of military logistics.

关键词： Multi-Agent Logistics Distributed Simulation Genetic algorithm k-medoids algorithm A* algorithm

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