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检索条件"主题词=K-means clustering algorithm"

共 398 条记录，以下是31-40 订阅

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Inferring Station Numbers in Metro Trips Using Mobile Magnetometer Sensor via an Unsupervised k-means clustering algorithm 8

Inferring Station Numbers in Metro Trips Using Mobile Magnet...

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8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Hosseini, Seyed Hassan Gentile, Guido Varghese, ken koshy Miristice, Lory Michelle Bresciani Univ Roma La Sapienza Dept Civil Construct & Environm Engn Rome Italy

ISBN: (纸本)9781665455305

Transport mode detection in urban areas with the help of mobile phones is not anymore, a challenging problem. Most studies via machine and deep learning models using GPS and inertial mobile sensors can distinguish different modes with various prediction accuracies. This paper presents a new way to count the number of stations in metro trips using a magnetometer sensor where GPS, internet, and wireless positioning are unavailable. The primary source for this investigation was recorded via mobile magnetometer sensor of metro riders. We first find contextual features that can effectively recognize acceleration state according to the 3D magnetometer data and then classification with a k-means unsupervised method into different classes. Finally, we present a station counter algorithm to count the number of metro stations in metro-based trips. Results from experiments in Rome and Stockholm metro systems show that our final algorithm can count the number of stations with an accuracy of 86 % where there is no internet, GPS, and WiFi access.

关键词： metro station detection magnetometer sensor mobile data k-means clustering algorithm

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An Evaluation Strategy of Energy Storage Construction for Industrial Users Based on k-means clustering algorithm

An Evaluation Strategy of Energy Storage Construction for In...

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IEEE International Conference on Computing, Communications and IoT Applications (IEEE ComComAp)

作者： Zhao, Yangyang Deng, Boya Chen, Hui Zhang, Hualu Shi, Jie Xu, Zhengwei Zhu, Xingyang Han, Guangjie State Grid Suzhou City Energy Res Inst Co Ltd Suzhou Peoples R China State Grid Suzhou Power Supply Co Ltd Suzhou Peoples R China NARI Informat & Commun Technol Co Ltd Nanjing Peoples R China Hohai Univ Dept Informat & Commun Syst Changzhou Jiangsu Peoples R China

ISBN: (纸本)9781728119731

Energy storage refers to a series of related technologies that achieve energy reserve and release by some ways when needed. Energy storage technology plays an important role in peak shaving and valley filling, improving the equipment utilization efficiency, enhancing the system safety and so on. It has great significances to realize the transformation of energy structure and the change of electricity production or consumption patterns for promoting the overall development of energy industry through the research of user-side energy storage technology. In this paper, we analyze the historical power consumption data of industrial users by k-means clustering algorithm to design a user type automatic evaluation model, which can assist large industrial users to judge whether industrial users are suitable for energy storage construction or not, it can help enterprises save manpower and financial resources. In addition, we design a method to calculate the potential value of users, which can reflect the degree of suitability for equipping energy storage device. Simulation results show that the proposed model can correctly obtain the users' typical load curve and power usage type through clustering historical power load data.

关键词： k-means clustering algorithm Energy storage Peak shaving and valley filling

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THE ESTIMATION OF THE LAMB WAVE PHASE VELOCITY DISPERSION CURVES BASE ON k- means clustering algorithm 15

THE ESTIMATION OF THE LAMB WAVE PHASE VELOCITY DISPERSION CU...

引用

15th Symposium on Piezoelectrcity, Acoustic Waves and Device Applications (SPAWDA)

作者： Gao, Xiang Li, Chen-xu Jiao, Jing-pin Feng, Ze-wen Beijing Univ Technol Fac Mat & Mfg Beijing 100049 Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China

ISBN: (纸本)9781665403153

Lamb waves are widely used in the localization and quantification of structural damage for plate-type structure. The propagation velocity and dispersion characteristics of Lamb waves are very important calculation parameters. The method to estimate the dispersion curves of Lamb waves is proposed based on k-means clustering algorithm. Three piezoelectric transducers with different center frequencies experimentally transmit the Lamb wave signals, and laser vibrometer is used to receive signals at different intervals in the direction of wave propagation. The phase velocity of lamb waves is estimated by many pairs of adjacent signals peaks. Use Euclidean distance as the index of data evaluation to find the data center, which is the phase velocity to be solved. And the dispersion curves are further estimated and inverted. The proposed method has been experimentally verified in isotropic-aluminum plate. The relative error of the actual phase velocity of lamb waves compared with all measured clustering centers is less than 1%, and the overall average relative error is less than 0.4%.

关键词： Lamb waves Phase velocity Dispersion curves k-means clustering algorithm

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Decision Support System with k-means clustering algorithm for Detecting the Optimal Store Location Based on Social Network Events

Decision Support System with K-Means Clustering Algorithm fo...

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IEEE European Technology and Engineering Management Summit (E-TEMS)

作者： Hamada, Mohamed Ahmed Naizabayeva, Lyazat IITU Int IT Univ Informat Syst Dept Alma Ata Kazakhstan

ISBN: (纸本)9781728109039

Nowadays, the business market is more complicated and comprises many challenges;it became more competitive and surrounded by high-risk patterns. Seeking for new technologies and adopting innovation is becoming an important and crucial issue to eliminate the complexity of the decision-making process and failure probability. Decision support system (DSS) is a computerized system that encompasses mathematical and analytical models, knowledge base and a user interface to help managers for making better decisions. This research aims to develop a decision support system based on k-means clustering algorithm to detect the optimal store location through social network events. Also, this research explains how to extract data from one social network channel "Instagram" using the "Octoparse API" as a web data extraction tool. k-means algorithm identifies k-number of centroids, and allocates every data point to the nearest cluster. As a result, we analyzed 12754 posts started on the 1st of January 2019. Cleaned data are transformed using Minimax and k-means algorithms. As an output, we have got json format data file with centres which are placed on the map to provide a better understanding. The Result of this research is a visualized map pointed with places to define the optimal location of a specific store at the selected region. The practical value of this DSS tool is to help users to make a more valuable and accurate decision which lead to a decrease in the probability of ineffective business decision and minimize the business losses.

关键词： classification model decision support system machine learning optimal store location social network events k-means clustering algorithm

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Dynamic Equivalent Modeling of Induction Motors based on k-means clustering algorithm

Dynamic Equivalent Modeling of Induction Motors based on K-m...

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IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia)

作者： Lin, Xueshan Wu, Hongbin Liu, Xin Xu, Bin Hefei Univ Technol Sch Elect Engn & Automat Hefei Anhui Peoples R China State Grid Anhui Elect Power Res Inst Hefei Anhui Peoples R China

ISBN: (纸本)9781538674345

It is unrealistic to establish the model for analyzing dynamic characteristic of each induction motor. In order to ensure that the equivalent induction motor models impose similar impact on the electric system compared with the original induction motors group, the critical slip scr and the load rate kL of the induction motor were taken as the clustering indicators in this paper. Induction motors group was clustered based on the k-means algorithm to get different cluster numbers. Then the optimal clustering result was obtained by the F statistics. After the clustering, induction motors in different classes were equalized and the accurate dynamic equivalent models were established. The simulation curves were obtained after comparing the established equivalent models with the original models and the classic models in the short circuit fault condition through the example system. The simulation results proved that the method proposed in this paper were reliable.

关键词： Dynamic equivalence electromotor group k-means clustering algorithm modeling

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Optimization Method for Predictive Models Based on ARIMA Time Series and k-means clustering algorithm 36

Optimization Method for Predictive Models Based on ARIMA Tim...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Ban, Rui Deng, Yuanyuan Shenyang Jianzhu Univ Sch Comp Sci & Engn Shenyang 110168 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper proposes a method for optimizing prediction models based on the ARIMA time series and the k-means clustering algorithm to address the challenge of solving world puzzle games. The method aims to offer players a diverse range of challenges by anticipating and predicting imbalances in difficulty and content in advance. To achieve this, the paper first establishes an ARIMA time series model to fit the data and calculate the goodness of fit. Subsequently, employing keywords such as vowels, consonants, word composition, and usage, the k-means clustering algorithm is applied to compute the clustering center of the data and generate a clustering result graph. Finally, different regression models were established for the characteristics of various difficulty samples, and the words were digitally encoded. The least squares method was used for fitting. Faced with different attempts, a reasonable prediction interval was obtained based on the analysis results. Experimental results indicate that, in comparison to traditional algorithms, the proposed method demonstrates higher accuracy in predicting word attribute classifications. The findings of this research offer an effective approach for game developers to manage puzzle game difficulty, enabling better balancing of game challenges and enhancing the gaming experience and engagement for players.

关键词： ARIMA time series model k-means clustering algorithm linear regression least square method

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Research on Improved k-means clustering algorithm

Research on Improved K-means Clustering Algorithm

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Zhang, Yinsheng Shan, Huilin Li, Jiaqiang Zhou, Jie Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China

ISBN: (纸本)9783037853122

The traditional k-means clustering algorithm prematurely plunges into a local optimum because of sensitive selection of the initial cluster center. Hierarchical clustering algorithm can be used to generate the initial cluster center of k-means clustering algorithm. The geometric features of input data can achieve a good distribution by means of pretreatment and feature extraction and selection. In the learning of fuzzy neural network, Java language is used to write source code of the algorithm. The experimental results show that new algorithm has improved the clustering quality effectively.

关键词： Pattern Recognition Hierarchical clustering k-means clustering algorithm Java

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Research on English Teaching Quality Evaluation based on Fuzzy Comprehensive Evaluation based on k-means clustering algorithm 3

Research on English Teaching Quality Evaluation based on Fuz...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Zhou, Yun Yuxi Normal University of Yunnan Province Yunnan Province 653100 China

ISBN: (纸本)9798350319125

With the acceleration of globalization, English plays an increasingly important role in international communication. Therefore, the improvement of English teaching quality has become an important issue that educators pay attention to. In order to make a comprehensive, objective and scientific evaluation of English teaching, this study adopts k-means clustering fuzzy comprehensive evaluation method, and analyzes the data of teachers, students and teaching environment, so as to provide targeted suggestions for improvement of English teaching © 2023 IEEE.

关键词： /b13>fuzzy comprehensive evaluation English language teaching k-means clustering algorithm Quality evaluation

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School-Based Management Performance Efficiency Modeling and Profiling using Data Envelopment Analysis and k-means clustering algorithm 4

School-Based Management Performance Efficiency Modeling and ...

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4th IEEE International Conference on Computer and Communication Systems (ICCCS)

作者： Tibay, Jona P. Ambat, Shaneth C. Lagman, Ace C. AMA Univ Sch Grad Studies Quezon City Philippines FEU Inst Technol Manila Philippines

ISBN: (纸本)9781728113227

Organizations are challenged to achieve effective and competent results, rising to imminent importance of measuring the performance efficiency. Data Envelopment Analysis (DEA) is an approach that measures performance efficiency of organizations. It is a non-parametric method, which uses linear programming to calculate efficiency in a given set of decision-making units (DMUs). It has widespread application in identifying efficiency and discovering benchmark. In the study, it utilized DEA in identifying School-Based Management (SBM) performance efficiency of one (1) division comprising of elementary and secondary schools under Department of Education (DepEd) in the Philippines. Efficient schools were used as benchmark for improvement of inefficient schools. The schools had also undergone clustering, which is the process of grouping in accordance to similar characteristics. k-means clustering algorithm was used to group the schools according to their respective profile. k-means clustering is a simple unsupervised learning algorithm that follows a simple procedure of classifying a given data set into a number of clusters. The study also encompasses the development of an application system that utilizes data from DEA and k-means clustering algorithm. The application system also provided recommendations to help inefficient schools improve.

关键词： Data Envelopment Analysis (DEA) k-means clustering algorithm Performance Efficiency

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The k-means clustering algorithm based on density and ant colony

The K-means clustering algorithm based on density and ant co...

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International Conference on Neural Networks and Signal Processing

作者： Peng, YQ Hou, XD Liu, S Hebei Univ & Technol Sch Comp Sci & Engn Tianjin 300130 Peoples R China

ISBN: (纸本)0780377028

The ant algorithm is a new evolutional method, k-means and the density-cluster are familiar cluster analysis. In this paper, we proposed a new k-means algorithm based on density and ant theory, which resolved the problem of local minimal by the random of ants and handled the initial parameter sensitivity of k-means. In addition it combined idea of density and made the ants searching selectable. With the experiments it was proved that the algorithm we proposed improved the efficiency and precision of cluster.

关键词： data mining pattern clustering sensitivity ant colony density k-means clustering algorithm density-cluster cluster analysis parameter sensitivity

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