FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285248928> ?p ?o ?g. }

• W4285248928 endingPage "68428" @default.
• W4285248928 startingPage "68416" @default.
• W4285248928 abstract "This article evaluates several machine learning methods to substitute the missing light detection and ranging data for better spatial localization of industrial automated guided vehicles. Decision trees and ensemble of trees using bagging or boosting techniques have been considered. Also, the k-nearest neighbors algorithm was analyzed. Most of the algorithms have been evaluated based on multiple criteria and hyper parameter tuning. The analysis of the results was done in a comparative way, multiple regression evaluation metrics being considered. The experiments have shown that the extreme gradient boosting algorithm provides the best results in terms of performance, but with timing and resource allocation drawbacks. On the other hand, a simple decision tree model seems to give good results if a tradeoff between performance and prediction time must be made. The <i>k</i>-nearest neighbors algorithm is also performing pretty well, especially because we are experimenting in a static environment." @default.
• W4285248928 created "2022-07-14" @default.
• W4285248928 creator A5015623422 @default.
• W4285248928 creator A5016132268 @default.
• W4285248928 creator A5032904559 @default.
• W4285248928 creator A5037494040 @default.
• W4285248928 creator A5048332365 @default.
• W4285248928 creator A5067927639 @default.
• W4285248928 date "2022-01-01" @default.
• W4285248928 modified "2023-09-30" @default.
• W4285248928 title "Estimation of Missing LiDAR Data for Accurate AGV Localization" @default.
• W4285248928 cites W1482424785 @default.
• W4285248928 cites W1678356000 @default.
• W4285248928 cites W1680797894 @default.
• W4285248928 cites W1967740178 @default.
• W4285248928 cites W2009985472 @default.
• W4285248928 cites W2011307992 @default.
• W4285248928 cites W2051808767 @default.
• W4285248928 cites W2100358124 @default.
• W4285248928 cites W2122111042 @default.
• W4285248928 cites W2122347864 @default.
• W4285248928 cites W2130422193 @default.
• W4285248928 cites W2167340365 @default.
• W4285248928 cites W2411093439 @default.
• W4285248928 cites W2905384411 @default.
• W4285248928 cites W2911964244 @default.
• W4285248928 cites W2914371697 @default.
• W4285248928 cites W2924078280 @default.
• W4285248928 cites W2926585089 @default.
• W4285248928 cites W2945662945 @default.
• W4285248928 cites W2963339133 @default.
• W4285248928 cites W2976127369 @default.
• W4285248928 cites W2977461652 @default.
• W4285248928 cites W3004178587 @default.
• W4285248928 cites W3011157584 @default.
• W4285248928 cites W3015235064 @default.
• W4285248928 cites W3015488315 @default.
• W4285248928 cites W3017180776 @default.
• W4285248928 cites W3054552769 @default.
• W4285248928 cites W3094159929 @default.
• W4285248928 cites W3102327032 @default.
• W4285248928 cites W3122263456 @default.
• W4285248928 cites W3170657538 @default.
• W4285248928 cites W3179164182 @default.
• W4285248928 cites W3206456343 @default.
• W4285248928 cites W4212883601 @default.
• W4285248928 cites W4229000832 @default.
• W4285248928 doi "https://doi.org/10.1109/access.2022.3185763" @default.
• W4285248928 hasPublicationYear "2022" @default.
• W4285248928 type Work @default.
• W4285248928 citedByCount "7" @default.
• W4285248928 countsByYear W42852489282022 @default.
• W4285248928 countsByYear W42852489282023 @default.
• W4285248928 crossrefType "journal-article" @default.
• W4285248928 hasAuthorship W4285248928A5015623422 @default.
• W4285248928 hasAuthorship W4285248928A5016132268 @default.
• W4285248928 hasAuthorship W4285248928A5032904559 @default.
• W4285248928 hasAuthorship W4285248928A5037494040 @default.
• W4285248928 hasAuthorship W4285248928A5048332365 @default.
• W4285248928 hasAuthorship W4285248928A5067927639 @default.
• W4285248928 hasBestOaLocation W42852489281 @default.
• W4285248928 hasConcept C10229987 @default.
• W4285248928 hasConcept C113238511 @default.
• W4285248928 hasConcept C115051666 @default.
• W4285248928 hasConcept C119857082 @default.
• W4285248928 hasConcept C120136583 @default.
• W4285248928 hasConcept C124101348 @default.
• W4285248928 hasConcept C127313418 @default.
• W4285248928 hasConcept C154945302 @default.
• W4285248928 hasConcept C169258074 @default.
• W4285248928 hasConcept C41008148 @default.
• W4285248928 hasConcept C45942800 @default.
• W4285248928 hasConcept C46686674 @default.
• W4285248928 hasConcept C51399673 @default.
• W4285248928 hasConcept C5481197 @default.
• W4285248928 hasConcept C62649853 @default.
• W4285248928 hasConcept C70153297 @default.
• W4285248928 hasConcept C76155785 @default.
• W4285248928 hasConcept C84525736 @default.
• W4285248928 hasConcept C9357733 @default.
• W4285248928 hasConceptScore W4285248928C10229987 @default.
• W4285248928 hasConceptScore W4285248928C113238511 @default.
• W4285248928 hasConceptScore W4285248928C115051666 @default.
• W4285248928 hasConceptScore W4285248928C119857082 @default.
• W4285248928 hasConceptScore W4285248928C120136583 @default.
• W4285248928 hasConceptScore W4285248928C124101348 @default.
• W4285248928 hasConceptScore W4285248928C127313418 @default.
• W4285248928 hasConceptScore W4285248928C154945302 @default.
• W4285248928 hasConceptScore W4285248928C169258074 @default.
• W4285248928 hasConceptScore W4285248928C41008148 @default.
• W4285248928 hasConceptScore W4285248928C45942800 @default.
• W4285248928 hasConceptScore W4285248928C46686674 @default.
• W4285248928 hasConceptScore W4285248928C51399673 @default.
• W4285248928 hasConceptScore W4285248928C5481197 @default.
• W4285248928 hasConceptScore W4285248928C62649853 @default.
• W4285248928 hasConceptScore W4285248928C70153297 @default.
• W4285248928 hasConceptScore W4285248928C76155785 @default.
• W4285248928 hasConceptScore W4285248928C84525736 @default.

• next