Matches in SemOpenAlex for { <https://semopenalex.org/work/W2985374367> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W2985374367 abstract "Abstract Traditional extrapolation performed by machine designers for energy loss estimation results in the decrease of the overall efficiency of electrical machines. Therefore, state‐of‐the‐art techniques need to be developed in order to accurately predict the energy loss in electrical machines for their improved performance. To this end, machine learning techniques have been employed to predict accurate energy loss at different frequencies and induction levels under rotational conditions. Such types of flux exist near the teeth of the stator in synchronous machines. In transformers, rotational flux arises at the bends and corners of the stators. It was observed that the random forest machine learning algorithm has the least mean square error and as such is the most suited algorithm, which can be used for the accurate prediction of energy loss in nonoriented materials." @default.
- W2985374367 created "2019-11-22" @default.
- W2985374367 creator A5001643289 @default.
- W2985374367 creator A5023999988 @default.
- W2985374367 creator A5036505076 @default.
- W2985374367 creator A5050974694 @default.
- W2985374367 creator A5087380351 @default.
- W2985374367 date "2019-11-12" @default.
- W2985374367 modified "2023-09-26" @default.
- W2985374367 title "Energy loss prediction in nonoriented materials using machine learning techniques: A novel approach" @default.
- W2985374367 cites W1582452446 @default.
- W2985374367 cites W1980652621 @default.
- W2985374367 cites W2031007444 @default.
- W2985374367 cites W2082190101 @default.
- W2985374367 cites W2086712112 @default.
- W2985374367 cites W2090213034 @default.
- W2985374367 cites W2336944899 @default.
- W2985374367 cites W2514132609 @default.
- W2985374367 cites W2793657457 @default.
- W2985374367 cites W2805447019 @default.
- W2985374367 cites W2908413206 @default.
- W2985374367 cites W2911964244 @default.
- W2985374367 cites W57033475 @default.
- W2985374367 cites W1655697160 @default.
- W2985374367 cites W2085879700 @default.
- W2985374367 cites W2907258203 @default.
- W2985374367 doi "https://doi.org/10.1002/ett.3797" @default.
- W2985374367 hasPublicationYear "2019" @default.
- W2985374367 type Work @default.
- W2985374367 sameAs 2985374367 @default.
- W2985374367 citedByCount "0" @default.
- W2985374367 crossrefType "journal-article" @default.
- W2985374367 hasAuthorship W2985374367A5001643289 @default.
- W2985374367 hasAuthorship W2985374367A5023999988 @default.
- W2985374367 hasAuthorship W2985374367A5036505076 @default.
- W2985374367 hasAuthorship W2985374367A5050974694 @default.
- W2985374367 hasAuthorship W2985374367A5087380351 @default.
- W2985374367 hasConcept C105795698 @default.
- W2985374367 hasConcept C119599485 @default.
- W2985374367 hasConcept C127413603 @default.
- W2985374367 hasConcept C132459708 @default.
- W2985374367 hasConcept C139945424 @default.
- W2985374367 hasConcept C154945302 @default.
- W2985374367 hasConcept C165801399 @default.
- W2985374367 hasConcept C169258074 @default.
- W2985374367 hasConcept C186370098 @default.
- W2985374367 hasConcept C2776529397 @default.
- W2985374367 hasConcept C33923547 @default.
- W2985374367 hasConcept C41008148 @default.
- W2985374367 hasConcept C66322947 @default.
- W2985374367 hasConcept C78519656 @default.
- W2985374367 hasConceptScore W2985374367C105795698 @default.
- W2985374367 hasConceptScore W2985374367C119599485 @default.
- W2985374367 hasConceptScore W2985374367C127413603 @default.
- W2985374367 hasConceptScore W2985374367C132459708 @default.
- W2985374367 hasConceptScore W2985374367C139945424 @default.
- W2985374367 hasConceptScore W2985374367C154945302 @default.
- W2985374367 hasConceptScore W2985374367C165801399 @default.
- W2985374367 hasConceptScore W2985374367C169258074 @default.
- W2985374367 hasConceptScore W2985374367C186370098 @default.
- W2985374367 hasConceptScore W2985374367C2776529397 @default.
- W2985374367 hasConceptScore W2985374367C33923547 @default.
- W2985374367 hasConceptScore W2985374367C41008148 @default.
- W2985374367 hasConceptScore W2985374367C66322947 @default.
- W2985374367 hasConceptScore W2985374367C78519656 @default.
- W2985374367 hasIssue "2" @default.
- W2985374367 hasLocation W29853743671 @default.
- W2985374367 hasOpenAccess W2985374367 @default.
- W2985374367 hasPrimaryLocation W29853743671 @default.
- W2985374367 hasRelatedWork W2001127778 @default.
- W2985374367 hasRelatedWork W2240965754 @default.
- W2985374367 hasRelatedWork W2940614149 @default.
- W2985374367 hasRelatedWork W3132265167 @default.
- W2985374367 hasRelatedWork W3157910026 @default.
- W2985374367 hasRelatedWork W3186965874 @default.
- W2985374367 hasRelatedWork W4288365262 @default.
- W2985374367 hasRelatedWork W4323021782 @default.
- W2985374367 hasRelatedWork W4362680528 @default.
- W2985374367 hasRelatedWork W4383426745 @default.
- W2985374367 hasVolume "33" @default.
- W2985374367 isParatext "false" @default.
- W2985374367 isRetracted "false" @default.
- W2985374367 magId "2985374367" @default.
- W2985374367 workType "article" @default.