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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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A preliminary study on wellbore flow interpretation of fiber optic vibration signals based on k-means clustering algorithm

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SN APPLIED SCIENCES 2022年第8期4卷 1-11页

作者： Wu, Xianzu Gan, Lixiong Yuan, Shixiong Rui, Deng Yangtze Univ Key Lab Oil & Gas Resources & Explorat Technol Minist Educ Wuhan 430100 Hubei Peoples R China

The wellbore flow analysis of optical fiber vibration signal depends on distributed optical fiber logging. Distributed optical fiber logging technology identifies the fluid in the well through distributed optical fiber acoustic sensor (DAS) and distributed optical fiber temperature sensor (DTS). Distributed optical fiber sensor has the advantages of small underground interference, high efficiency and low cost. In this paper, the wellhead data extracted by the distributed optical fiber acoustic sensor is used to calculate the upper bound of the fluid sound frequency band in the pipe by nonlinear least squares fitting. The k-means clustering algorithm is used to cluster the optical fiber vibration signals in the low frequency band. According to the clustering results, the ratio of the optical fiber signal eigenvalues of each production layers is obtained, and the trend of the ratio of the optical fiber signal eigenvalues of each production layers is judged to be close to the trend of the water absorption intensity. Compared with traditional acoustic logging, the wellbore flow analysis using distributed optical fiber acoustic sensor can quickly determine the production contribution of each layer and the change of fluid phase state in the production cycle. Combined with traditional production logging technology, distributed optical fiber logging shows its reliability and accuracy in data collection, logging interpretation and production application. Starting from the principle of distributed optical fiber acoustic sensing technology, this paper briefly expounds the properties of distributed optical fiber acoustic sensor and the principle of injection profile logging, systematically introduces the processing of distributed optical fiber acoustic data, and emphatically introduces the accuracy of k-means clustering algorithm for analyzing distributed optical fiber acoustic signal and qualitative judgment of production layer, which provides a new idea for judging the accura

关键词： Distributed fiber optic acoustic wave sensor Fiber optic vibration signal Nonlinear least squares fitting k-means clustering algorithm Accuracy

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Application of k-means clustering algorithm to improve effectiveness of the results recommended by journal recommender system

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SCIENTOMETRICS 2022年第6期127卷 3237-3252页

作者： Vara, Narjes Mirzabeigi, Mahdieh Sotudeh, Hajar Fakhrahmad, Seyed Mostafa Shiraz Univ Dept Knowledge & Informat Sci Sch Educ & Psychol Shiraz Iran RICeST Dept Evaluat & Resource Dev Shiraz Iran Shiraz Univ Sch Educ & Psychol Dept Knowledge & Informat Sci Shiraz Iran Shiraz Univ Dept Comp Sci & Engn Sch Elect & Comp Engn Shiraz Iran

This study investigates to evaluate feasibility of k-means clustering algorithm in order to improve effectiveness of the results recommended by RICEST Journal Finder System. More than 15,000 papers published in filed of engineering journals during 2013-2017 were collected from their websites. Their titles, abstracts and keywords were extracted, normalized and processed in order to form the test body. According to the number of papers collected, using Cochran's formula, 400 papers completely relevant to the subject of each journal were randomly and proportionally selected and entered the system as queries in order to receive the journals recommended by the system before and after k-means clustering algorithm and the results were recorded. Finally, effectiveness of the system results was determined at each stage by leave-one-out cross validation method based on precision at k top ranked results. Also, opinions of subject reviewers on relevance of the target journal were investigated through a questionnaire. Results showed that before data clustering, only 40% of target journal was recommended at the first 3 ranks. But after k-means clustering algorithm, in more than 80% of searches, the target journal was retrieved at the first 3 ranks. Also, effectiveness of the recommendations, according to 210 subject reviewers, after k-means clustering algorithm, showed that more than 80% of the recommended journals are completely relevant to the given paper. According to the study results, data clustering can significantly increase effectiveness of the results recommended by journal recommender systems.

关键词： Journal recommender systems Effectiveness Scalability k-means clustering algorithm RICeST journal finder system

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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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Application of Big Data Technology and k-means clustering algorithm in Urban Rail Transit

Application of Big Data Technology and K-Means Clustering Al...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Wu, Huijiao Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350309614

With the continuous development of science and technology, the amount of data generated in life is increasing, and big data technology has now penetrated into all walks of life. This paper focuses on the application of big data technology in urban rail transit. Firstly, the related concepts of big data technology and urban rail transit are introduced in detail. Secondly, it emphasizes the necessity of using big data technology in urban rail transit;Finally, this paper expounds the examples of the application of big data technology in urban rail transit, including key applications such as operation and maintenance, accessibility analysis and passenger flow monitoring. Taking passenger flow monitoring as an example, based on the passenger flow data of Tianjin big data technology, the stations are classified by k-means clustering algorithm. Big data technology can help urban rail transit improve its operational efficiency in many aspects, promote its intelligent development and accelerate the process of urbanization. Faced with the abundance and complexity of data in the era of science and technology, it will become an inevitable trend to assist management decision-making through big data technology. © 2023 IEEE.

关键词： Accessibility model Big data technology k-means clustering algorithm Passenger flow monitoring Urban rail transit

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Research on the Analysis of User Power Consumption Behavior Characteristics based on the Improved k-means clustering algorithm 8

Research on the Analysis of User Power Consumption Behavior ...

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8th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2025

作者： Qin, Qingmei Li, She Wang, Wenhao Nanning University College of Intelligent Manufacturingy China

ISBN: (纸本)9798331517915

With the rapid economic development and the continuous growth of electricity demand, the power grid is facing severe pressure in power supply guarantee. This study utilizes data mining techniques to analyze user power consumption data and proposes a strategy of using the kmeans algorithm for clustering analysis of user power consumption data. Given that the k-means algorithm is sensitive to noisy data, the DBSCAN algorithm is adopted to optimize the clustering analysis strategy. Moreover, evaluation indicators such as CHI, silhouette coefficient, and DBI are used to conduct a comparative analysis of the clustering results of the traditional k - means algorithm and the k - means algorithm improved by DBSCAN. The results show that the latter exhibits more significant superiority when dealing with user power consumption data. By optimizing the algorithm for clustering analysis of user power consumption behavior characteristics, this research aims to better support personalized services in the power industry and the optimization of power grid dispatching. © 2025 IEEE.

关键词： Characteristics of User Power Consumption Behavior clustering Analysis Data Mining DBSCAN clustering algorithm k-means clustering algorithm

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Regional economic development level assessment based on k-means clustering algorithm

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Procedia Computer Science 2025年 262卷 1137-1143页

作者： Yifan Pang Dan Nie School of Economics Dalian University of Finance and Economics Dalian 116622 China

In order to quantitatively analyze regional economy through scientific method, the differences of economic development in different regions are revealed. This paper uses k-means clustering algorithm to divide regional economic data into several groups, and each group represents a region with similar economic development level. On this basis, the index system of regional economic development level including a number of economic indicators is constructed, and the accuracy and consistency of data are ensured through data collection and pre-processing. The experimental results show that the k-means clustering algorithm can effectively divide the regional economy into several groups with different development levels, and the regions within each group have significant similarities in economic development, while there are obvious differences between different groups.

关键词： Regional economy k-means clustering algorithm Development level assessment

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Research on prediction and recommendation of financial stocks based on k-means clustering algorithm optimization

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第5期21卷 1081-1089页

作者： Fang, Zheng Chiao, Chaoshin Nanjing Univ Sci & Technol Sch Econ & Management Nanjing Jiangsu Peoples R China Natl Dong Hwa Univ Dept Finance Hualien Taiwan

The prediction and recommendation of financial stocks are of great values. This study mainly analyzed the application of k-means clustering algorithm in stock forecasting and recommendation. Firstly, it introduced the k-means algorithm briefly and analyzed its advantages and disadvantages. Then, the k-means algorithm was optimized by introducing artificial fish swarm algorithm (AFSA) to obtain kAFSA. Then 100 stocks of listed companies were taken as the research subject and predicted by kAFSA designed in this study. The prediction results were verified through closing price, price earning ratio, earnings per share and return on net assets. The results showed that there were obvious differences between A and B stocks divided by kAFSA, and the differences of B stocks were significantly larger than those of A stocks. It shows that 100 stocks are well divided into high performance stocks and poor performance stocks through clustering, which provides a good reference for investors to invest in stocks and is worth of further application.

关键词： k-means clustering algorithm data mining artificial fish swarm algorithm Stock forecast

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Integrating k-means clustering algorithm for the symbiotic relationship of aesthetic community spatial science

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JOURNAL OF INTELLIGENT SYSTEMS 2023年第1期32卷

作者： Bao, Yuanyang Yu, Qianqian Guangdong Univ Technol Sch Art & Design Guangzhou 510090 Peoples R China Guangdong Univ Technol Sch Art & Design Guangzhou 510090 Peoples R China

In modern society, the scientific symbiotic relationship of aesthetic community space is crucial for the development of cities. This article aimed to explore the symbiotic relationship between aesthetic community space and science, and analyze this relationship based on the k-means clustering algorithm. The article analyzed the spatial characteristics of different types of aesthetic communities, as well as the commonalities and differences between aesthetic communities and science, including the complementary relationship between the two. The k-means clustering algorithm is integrated to classify community spatial features, thereby analyzing and optimizing the relationship between the two. The experiment selected a sample of an aesthetic community and divided it into three different types of spatial features: architectural area, landscape greening area, and other activity areas. This article also compared the clustering results of the k-means algorithm with the k-means algorithm based on three-dimensional grid space. The results showed that the k-means algorithm optimized by the principle of three-dimensional grid space had stronger clustering performance. In the experiment, four different types of clustering algorithms and multiple evaluation indicators were also selected for testing. The data showed that in the clustering of spatial features in building areas, the k-means average clustering error score was controlled at around 13-14 under different missing ratios;the minimum average clustering error score of the characteristic data of the landscape greening area was 12.1. The average clustering error score in spatial feature clustering of other activity areas reached a minimum value of 23.5 when the missing ratio was 10%, which was lower than other algorithms. The overall experimental results indicate that the fused k-means clustering algorithm can effectively partition the aesthetic community space and optimize scientific symbiotic relationships.

关键词： aesthetic community scientific symbiosis k-means clustering algorithm data mining spatial analysis

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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...

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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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