Matches in SemOpenAlex for { <https://semopenalex.org/work/W4327831947> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W4327831947 endingPage "548" @default.
- W4327831947 startingPage "536" @default.
- W4327831947 abstract "Aiming at the problem that traditional machine learning methods rely on manual feature extraction in DC–DC circuit soft faults, it is important to effectively obtain the fault characteristics of DC–DC circuit soft faults. In this study, combining the advantages of one-dimensional convolutional neural network (1DCNN) and gated logic unit (GRU), a deep learning model for fault identification of time series signals is proposed to realize soft fault diagnosis of DC–DC circuits. In this study, a 1DCNN-GRU network model is constructed, and 1DCNN can directly perform automatic feature extraction on the data, while the GRU makes up for the shortcomings of CNN in processing time series data, thereby ensuring the comprehensiveness of the extracted features. For the hyperparameter problem in the network model, the powerful optimization ability of the gray wolf optimization algorithm is used to automatically search for the best hyperparameters in the 1DCNN-GRU network, and then the optimized 1DCNN-GRU network model is used for comprehensive feature learning. In order to meet the needs of deep learning for large data samples, the overlapping sampling method is used to enrich the data sample set. Experimental results show that the proposed method achieves 99.62% accuracy in DC–DC circuit fault diagnosis, and still maintains good robustness in noisy environment." @default.
- W4327831947 created "2023-03-19" @default.
- W4327831947 creator A5007618502 @default.
- W4327831947 creator A5012278873 @default.
- W4327831947 creator A5015936991 @default.
- W4327831947 creator A5087124805 @default.
- W4327831947 date "2023-04-01" @default.
- W4327831947 modified "2023-10-05" @default.
- W4327831947 title "DC–DC circuit fault diagnosis based on GWO optimization of 1DCNN-GRU network hyperparameters" @default.
- W4327831947 cites W2061438946 @default.
- W4327831947 cites W2167320299 @default.
- W4327831947 cites W2290883490 @default.
- W4327831947 cites W2789290713 @default.
- W4327831947 cites W2794081072 @default.
- W4327831947 cites W2795765414 @default.
- W4327831947 cites W2915514405 @default.
- W4327831947 cites W2979980326 @default.
- W4327831947 cites W2980567411 @default.
- W4327831947 cites W2982172966 @default.
- W4327831947 cites W3002656377 @default.
- W4327831947 cites W3117800707 @default.
- W4327831947 cites W3145770415 @default.
- W4327831947 cites W3215184780 @default.
- W4327831947 cites W4205391559 @default.
- W4327831947 cites W4225843926 @default.
- W4327831947 doi "https://doi.org/10.1016/j.egyr.2023.03.069" @default.
- W4327831947 hasPublicationYear "2023" @default.
- W4327831947 type Work @default.
- W4327831947 citedByCount "2" @default.
- W4327831947 countsByYear W43278319472023 @default.
- W4327831947 crossrefType "journal-article" @default.
- W4327831947 hasAuthorship W4327831947A5007618502 @default.
- W4327831947 hasAuthorship W4327831947A5012278873 @default.
- W4327831947 hasAuthorship W4327831947A5015936991 @default.
- W4327831947 hasAuthorship W4327831947A5087124805 @default.
- W4327831947 hasBestOaLocation W43278319471 @default.
- W4327831947 hasConcept C104317684 @default.
- W4327831947 hasConcept C11413529 @default.
- W4327831947 hasConcept C153180895 @default.
- W4327831947 hasConcept C154945302 @default.
- W4327831947 hasConcept C185592680 @default.
- W4327831947 hasConcept C41008148 @default.
- W4327831947 hasConcept C50644808 @default.
- W4327831947 hasConcept C52622490 @default.
- W4327831947 hasConcept C55493867 @default.
- W4327831947 hasConcept C63479239 @default.
- W4327831947 hasConcept C81363708 @default.
- W4327831947 hasConcept C8642999 @default.
- W4327831947 hasConceptScore W4327831947C104317684 @default.
- W4327831947 hasConceptScore W4327831947C11413529 @default.
- W4327831947 hasConceptScore W4327831947C153180895 @default.
- W4327831947 hasConceptScore W4327831947C154945302 @default.
- W4327831947 hasConceptScore W4327831947C185592680 @default.
- W4327831947 hasConceptScore W4327831947C41008148 @default.
- W4327831947 hasConceptScore W4327831947C50644808 @default.
- W4327831947 hasConceptScore W4327831947C52622490 @default.
- W4327831947 hasConceptScore W4327831947C55493867 @default.
- W4327831947 hasConceptScore W4327831947C63479239 @default.
- W4327831947 hasConceptScore W4327831947C81363708 @default.
- W4327831947 hasConceptScore W4327831947C8642999 @default.
- W4327831947 hasLocation W43278319471 @default.
- W4327831947 hasOpenAccess W4327831947 @default.
- W4327831947 hasPrimaryLocation W43278319471 @default.
- W4327831947 hasRelatedWork W1964120219 @default.
- W4327831947 hasRelatedWork W2144059113 @default.
- W4327831947 hasRelatedWork W2146076056 @default.
- W4327831947 hasRelatedWork W2767651786 @default.
- W4327831947 hasRelatedWork W2811390910 @default.
- W4327831947 hasRelatedWork W2913302899 @default.
- W4327831947 hasRelatedWork W3003836766 @default.
- W4327831947 hasRelatedWork W4304182771 @default.
- W4327831947 hasRelatedWork W4312376745 @default.
- W4327831947 hasRelatedWork W4385415357 @default.
- W4327831947 hasVolume "9" @default.
- W4327831947 isParatext "false" @default.
- W4327831947 isRetracted "false" @default.
- W4327831947 workType "article" @default.