FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3136334612> ?p ?o ?g. }

• W3136334612 endingPage "2068" @default.
• W3136334612 startingPage "2054" @default.
• W3136334612 abstract "Structural health monitoring system plays a vital role in smart management of civil engineering. A lot of efforts have been motivated to improve data quality through mean, median values, or simple interpolation methods, which are low-precision and not fully reflected field conditions due to the neglect of strong spatio-temporal correlations borne by monitoring datasets and the thoughtless for various forms of abnormal conditions. Along this line, this article proposed an integrated framework for data augmentation in structural health monitoring system using machine learning algorithms. As a case study, the monitoring data obtained from structural health monitoring system in the Nanjing Yangtze River Tunnel are selected to make experience. First, the original data are reconstructed based on an improved non-negative matrix factorization model to detect abnormal conditions occurred in different cases. Subsequently, multiple supervised learning methods are introduced to process the abnormal conditions detected by non-negative matrix factorization. The effectiveness of multiple supervised learning methods at different missing ratios is discussed to improve its university. The experimental results indicate that non-negative matrix factorization can recognize different abnormal situations simultaneously. The supervised learning algorithms expressed good effects to impute datasets under different missing rates. Therefore, the presented framework is applied to this case for data augmentation, which is crucial for further analysis and provides an important reference for similar projects." @default.
• W3136334612 created "2021-03-29" @default.
• W3136334612 creator A5013194872 @default.
• W3136334612 creator A5025013285 @default.
• W3136334612 creator A5050337942 @default.
• W3136334612 creator A5053487836 @default.
• W3136334612 creator A5054821173 @default.
• W3136334612 creator A5081275566 @default.
• W3136334612 date "2021-03-11" @default.
• W3136334612 modified "2023-09-29" @default.
• W3136334612 title "Investigation on the data augmentation using machine learning algorithms in structural health monitoring information" @default.
• W3136334612 cites W1964176024 @default.
• W3136334612 cites W2039015671 @default.
• W3136334612 cites W2125804487 @default.
• W3136334612 cites W2129181381 @default.
• W3136334612 cites W2131739422 @default.
• W3136334612 cites W2156238897 @default.
• W3136334612 cites W2169534075 @default.
• W3136334612 cites W2470621907 @default.
• W3136334612 cites W2561809584 @default.
• W3136334612 cites W2581569536 @default.
• W3136334612 cites W2726858467 @default.
• W3136334612 cites W2762874775 @default.
• W3136334612 cites W2886162118 @default.
• W3136334612 cites W2888297823 @default.
• W3136334612 cites W2917003284 @default.
• W3136334612 cites W2938181110 @default.
• W3136334612 cites W2963043340 @default.
• W3136334612 cites W2966354462 @default.
• W3136334612 cites W2972813848 @default.
• W3136334612 cites W2978050052 @default.
• W3136334612 cites W2995359231 @default.
• W3136334612 cites W3007545382 @default.
• W3136334612 doi "https://doi.org/10.1177/1475921721996238" @default.
• W3136334612 hasPublicationYear "2021" @default.
• W3136334612 type Work @default.
• W3136334612 sameAs 3136334612 @default.
• W3136334612 citedByCount "21" @default.
• W3136334612 countsByYear W31363346122021 @default.
• W3136334612 countsByYear W31363346122022 @default.
• W3136334612 countsByYear W31363346122023 @default.
• W3136334612 crossrefType "journal-article" @default.
• W3136334612 hasAuthorship W3136334612A5013194872 @default.
• W3136334612 hasAuthorship W3136334612A5025013285 @default.
• W3136334612 hasAuthorship W3136334612A5050337942 @default.
• W3136334612 hasAuthorship W3136334612A5053487836 @default.
• W3136334612 hasAuthorship W3136334612A5054821173 @default.
• W3136334612 hasAuthorship W3136334612A5081275566 @default.
• W3136334612 hasConcept C111919701 @default.
• W3136334612 hasConcept C11413529 @default.
• W3136334612 hasConcept C115961682 @default.
• W3136334612 hasConcept C119857082 @default.
• W3136334612 hasConcept C121332964 @default.
• W3136334612 hasConcept C124101348 @default.
• W3136334612 hasConcept C127413603 @default.
• W3136334612 hasConcept C137800194 @default.
• W3136334612 hasConcept C152671427 @default.
• W3136334612 hasConcept C154945302 @default.
• W3136334612 hasConcept C158693339 @default.
• W3136334612 hasConcept C202444582 @default.
• W3136334612 hasConcept C2776247918 @default.
• W3136334612 hasConcept C33923547 @default.
• W3136334612 hasConcept C41008148 @default.
• W3136334612 hasConcept C42355184 @default.
• W3136334612 hasConcept C62520636 @default.
• W3136334612 hasConcept C66938386 @default.
• W3136334612 hasConcept C9357733 @default.
• W3136334612 hasConcept C9652623 @default.
• W3136334612 hasConcept C98045186 @default.
• W3136334612 hasConceptScore W3136334612C111919701 @default.
• W3136334612 hasConceptScore W3136334612C11413529 @default.
• W3136334612 hasConceptScore W3136334612C115961682 @default.
• W3136334612 hasConceptScore W3136334612C119857082 @default.
• W3136334612 hasConceptScore W3136334612C121332964 @default.
• W3136334612 hasConceptScore W3136334612C124101348 @default.
• W3136334612 hasConceptScore W3136334612C127413603 @default.
• W3136334612 hasConceptScore W3136334612C137800194 @default.
• W3136334612 hasConceptScore W3136334612C152671427 @default.
• W3136334612 hasConceptScore W3136334612C154945302 @default.
• W3136334612 hasConceptScore W3136334612C158693339 @default.
• W3136334612 hasConceptScore W3136334612C202444582 @default.
• W3136334612 hasConceptScore W3136334612C2776247918 @default.
• W3136334612 hasConceptScore W3136334612C33923547 @default.
• W3136334612 hasConceptScore W3136334612C41008148 @default.
• W3136334612 hasConceptScore W3136334612C42355184 @default.
• W3136334612 hasConceptScore W3136334612C62520636 @default.
• W3136334612 hasConceptScore W3136334612C66938386 @default.
• W3136334612 hasConceptScore W3136334612C9357733 @default.
• W3136334612 hasConceptScore W3136334612C9652623 @default.
• W3136334612 hasConceptScore W3136334612C98045186 @default.
• W3136334612 hasFunder F4320321001 @default.
• W3136334612 hasIssue "4" @default.
• W3136334612 hasLocation W31363346121 @default.
• W3136334612 hasOpenAccess W3136334612 @default.
• W3136334612 hasPrimaryLocation W31363346121 @default.
• W3136334612 hasRelatedWork W1654562715 @default.
• W3136334612 hasRelatedWork W2597356648 @default.
• W3136334612 hasRelatedWork W2898616196 @default.

• next