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Showing 1 to 6 of 6 for “"DBSCAN clustering"”.
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Automatic Extraction of Joint Characteristics from Rock Mass Surface Point Cloud Using Deep Learning
… using the Density-Based Scan with Noise (DBSCAN) clustering algorithm. Subsequently, the orientations of the identified joint surfaces are computed by fitting least-square planes using the Random Sample Consensus (RANSAC). Finally, the joint planes are classified into different joint sets, …
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Modeling Vessel Behaviours By Clustering Ais Data Using Optimized DBSCAN
… we present an enhanced density-based spatial clustering (DBSCAN) method to model vessel behaviors. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis Distance metric that considers the correlations of the points representing the locations of the …
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Load Frequency Control for Electric Water Heater Using DBSCAN Algorithm in real-time
… The approach employs Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to dynamically group water heaters based on their thermal state and operational flexibility, enabling coordinated response to grid frequency deviations.<br/><br/>The methodology integrates …
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Regulating Traffic Flow and Speed on Large Networks: Control and Geographical Self Organizing Map (Geo-SOM) Clustering
… The geographical self organizing maps (GeoSOM) clustering algorithm is applied and tested on the LA network. The clustering goal is to identify a geographically connected region with small density variance. GeoSOM is able to achieve that objective with better performance than the …
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A cryptographic approach to location privacy
The rapid expansion of location-based services (LBS) has driven an escalating demand for personalised and context-aware applications, enriching user experiences across health, weather, and navigation sectors. These services offer valuable insights into various applications using large-scale …
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Integrating Principal Component Analysis and Deep Learning Methods for Data Representation and Image Denoising
… class-relevant structure, as confirmed by DBSCAN and K-Means clustering in the reduced space. To illustrate the properties of PCA, we consider a classical problem of image denoising where four denoisers, including linear PCA, kernel PCA, a shallow autoencoder, and a deep learning model …