FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3182409818> ?p ?o ?g. }

• W3182409818 endingPage "585" @default.
• W3182409818 startingPage "575" @default.
• W3182409818 abstract "In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small." @default.
• W3182409818 created "2021-07-19" @default.
• W3182409818 creator A5006402801 @default.
• W3182409818 creator A5050353996 @default.
• W3182409818 creator A5062495552 @default.
• W3182409818 creator A5080921048 @default.
• W3182409818 creator A5088576825 @default.
• W3182409818 date "2021-09-30" @default.
• W3182409818 modified "2023-10-17" @default.
• W3182409818 title "Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime" @default.
• W3182409818 cites W1008365285 @default.
• W3182409818 cites W1595159159 @default.
• W3182409818 cites W2037770017 @default.
• W3182409818 cites W2038780961 @default.
• W3182409818 cites W2062613656 @default.
• W3182409818 cites W2270330859 @default.
• W3182409818 cites W2342249984 @default.
• W3182409818 cites W2744383426 @default.
• W3182409818 cites W2773515741 @default.
• W3182409818 cites W2790109047 @default.
• W3182409818 cites W2794386528 @default.
• W3182409818 cites W2855289976 @default.
• W3182409818 cites W2887925021 @default.
• W3182409818 cites W2889358542 @default.
• W3182409818 cites W2893678326 @default.
• W3182409818 cites W2898280479 @default.
• W3182409818 cites W2899283552 @default.
• W3182409818 cites W2911964244 @default.
• W3182409818 cites W2920061516 @default.
• W3182409818 cites W2969434896 @default.
• W3182409818 cites W2997797281 @default.
• W3182409818 cites W3011714719 @default.
• W3182409818 cites W3034724350 @default.
• W3182409818 cites W3043051009 @default.
• W3182409818 cites W3048989976 @default.
• W3182409818 cites W3084200341 @default.
• W3182409818 cites W3084417819 @default.
• W3182409818 cites W3099878876 @default.
• W3182409818 cites W3102476541 @default.
• W3182409818 cites W3103145119 @default.
• W3182409818 cites W3124855205 @default.
• W3182409818 cites W3131523205 @default.
• W3182409818 cites W3174658763 @default.
• W3182409818 cites W4237168912 @default.
• W3182409818 doi "https://doi.org/10.17531/ein.2021.3.19" @default.
• W3182409818 hasPublicationYear "2021" @default.
• W3182409818 type Work @default.
• W3182409818 sameAs 3182409818 @default.
• W3182409818 citedByCount "2" @default.
• W3182409818 countsByYear W31824098182022 @default.
• W3182409818 crossrefType "journal-article" @default.
• W3182409818 hasAuthorship W3182409818A5006402801 @default.
• W3182409818 hasAuthorship W3182409818A5050353996 @default.
• W3182409818 hasAuthorship W3182409818A5062495552 @default.
• W3182409818 hasAuthorship W3182409818A5080921048 @default.
• W3182409818 hasAuthorship W3182409818A5088576825 @default.
• W3182409818 hasBestOaLocation W31824098181 @default.
• W3182409818 hasConcept C105795698 @default.
• W3182409818 hasConcept C111919701 @default.
• W3182409818 hasConcept C11413529 @default.
• W3182409818 hasConcept C119857082 @default.
• W3182409818 hasConcept C12267149 @default.
• W3182409818 hasConcept C141404830 @default.
• W3182409818 hasConcept C154945302 @default.
• W3182409818 hasConcept C169258074 @default.
• W3182409818 hasConcept C33923547 @default.
• W3182409818 hasConcept C41008148 @default.
• W3182409818 hasConcept C50644808 @default.
• W3182409818 hasConcept C83546350 @default.
• W3182409818 hasConcept C98045186 @default.
• W3182409818 hasConceptScore W3182409818C105795698 @default.
• W3182409818 hasConceptScore W3182409818C111919701 @default.
• W3182409818 hasConceptScore W3182409818C11413529 @default.
• W3182409818 hasConceptScore W3182409818C119857082 @default.
• W3182409818 hasConceptScore W3182409818C12267149 @default.
• W3182409818 hasConceptScore W3182409818C141404830 @default.
• W3182409818 hasConceptScore W3182409818C154945302 @default.
• W3182409818 hasConceptScore W3182409818C169258074 @default.
• W3182409818 hasConceptScore W3182409818C33923547 @default.
• W3182409818 hasConceptScore W3182409818C41008148 @default.
• W3182409818 hasConceptScore W3182409818C50644808 @default.
• W3182409818 hasConceptScore W3182409818C83546350 @default.
• W3182409818 hasConceptScore W3182409818C98045186 @default.
• W3182409818 hasIssue "3" @default.
• W3182409818 hasLocation W31824098181 @default.
• W3182409818 hasOpenAccess W3182409818 @default.
• W3182409818 hasPrimaryLocation W31824098181 @default.
• W3182409818 hasRelatedWork W2004826645 @default.
• W3182409818 hasRelatedWork W2789583223 @default.
• W3182409818 hasRelatedWork W2915047625 @default.
• W3182409818 hasRelatedWork W2955796858 @default.
• W3182409818 hasRelatedWork W3011239835 @default.
• W3182409818 hasRelatedWork W3135818052 @default.
• W3182409818 hasRelatedWork W3185760728 @default.
• W3182409818 hasRelatedWork W4200112873 @default.
• W3182409818 hasRelatedWork W4224941037 @default.
• W3182409818 hasRelatedWork W4312534362 @default.
• W3182409818 hasVolume "23" @default.

• next