Suchergebnisse - "kmeans clustering algorithm"

Gas Leak Real-Time Detection and Volume Flow Quantification Based on Infrared Imaging and Advanced Algorithms

Autoren: Man Yan Zhou Li Zheng Dong et al.

Quelle: IEEE Access, Vol 13, Pp 7284-7292 (2025)

Schlagwörter: Gas identification, DeeplabV3+ neural network, Electrical engineering. Electronics. Nuclear engineering, optical flow method, gas quantification, Kmeans clustering algorithm, TK1-9971

Zugangs-URL: https://doaj.org/article/a974fe9a3526408fa200c8050a209b44

Road network partitioning method based on Canopy-Kmeans clustering algorithm

Autoren: Xiaohui Lin Jianmin Xu

Quelle: Archives of Transport, Vol 54, Iss 2, Pp 95-106 (2020)

Schlagwörter: Transportation engineering, T59.5, Automation, TA1001-1280, 0202 electrical engineering, electronic engineering, information engineering, traffic engineering, road network partition, canopy clustering algorithm, kmeans clustering algorithm, macroscopic fundamental diagram, 02 engineering and technology, TA1-2040, Engineering (General). Civil engineering (General)

Zugangs-URL: https://doaj.org/article/86f2c7c22e1a42d58f8c1c48aad405ed
https://ui.adsabs.harvard.edu/abs/2020ArTr...54...95L/abstract
https://paperity.org/p/274584787/road-network-partitioning-method-based-on-canopy-kmeans-clustering-algorithm
http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-7fda764e-a475-4d01-86a1-02897147d6d1/c/at2020_2_lin_road.pdf
http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-7fda764e-a475-4d01-86a1-02897147d6d1

Research on the Optimal Deployment of Expressway Roadside Units under the Fusion Perception of Intelligent Connected Vehicles

Autoren: Peng Wang Youfu Lu Ning Chen et al.

Quelle: Applied Sciences, Vol 13, Iss 15, p 8878 (2023)

Schlagwörter: roadside RSU deployment, traffic flow–information flow coupling theory, WSN node energy loss model, cooperative vehicle infrastructure system, Warshell algorithm, Kmeans clustering algorithm, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/13/15/8878; https://doaj.org/toc/2076-3417; https://doaj.org/article/55f998d0c1d544b0bc5a26178879c5cb

Verfügbarkeit: https://doi.org/10.3390/app13158878
https://doaj.org/article/55f998d0c1d544b0bc5a26178879c5cb

Anomaly electricity detection method based on entropy weight method and isolated forest algorithm

Autoren: Wang Jianyuan Gu Chengcheng Liu Kechen

Quelle: Frontiers in Energy Research, Vol 10 (2022)

Schlagwörter: abnormal power consumption detection, isolation forest algorithm, kmeans clustering algorithm, entropy weight method, principal component dimension 11, General Works

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fenrg.2022.984473/full; https://doaj.org/toc/2296-598X

Zugangs-URL: https://doaj.org/article/8962627f627548b18724e0218acd9570

Community Detection and Visualization in Complex Network by the Density-Canopy-Kmeans Algorithm and MDS Embedding

Autoren: Manzhi Li Hongtao Wang Haixia Long et al.

Quelle: IEEE Access, Vol 7, Pp 120616-120625 (2019)

Schlagwörter: 0301 basic medicine, 03 medical and health sciences, the Density-Canopy-Kmeans clustering algorithm, 0103 physical sciences, community detection, MDS, Network, Electrical engineering. Electronics. Nuclear engineering, 01 natural sciences, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08807341.pdf
https://doaj.org/article/9c682bc40d694b1796371b5e7ffc94a6
https://dblp.uni-trier.de/db/journals/access/access7.html#LiWLXWXY19
https://ieeexplore.ieee.org/document/8807341/

Using machine learning for long-term track bed behaviour analysis and maintenance scheduling optimisation

Autoren: Popov, Konstantin Pankaj Forde, Mike et al.

Weitere Verfasser: Popov, Konstantin Pankaj Forde, Mike et al.

Schlagwörter: Rail traffic levels, KMeans clustering algorithm, machine learning, long-term track bed behaviour analysis, evaluating railway track quality, machine learning algorithms, high-speed line in the UK, single-layer Artificial Neural Network, high-speed line, Autoencoder, long-term track bed behaviour, track geometry data, maintenance scheduling optimisation

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/1842/41621