Matches in SemOpenAlex for { <https://semopenalex.org/work/W3189124218> ?p ?o ?g. }
- W3189124218 endingPage "2570" @default.
- W3189124218 startingPage "2558" @default.
- W3189124218 abstract "Wind energy is of great importance for future energy development. In order to fully exploit wind energy, wind farms are often located at high latitudes, a practice that is accompanied by a high risk of icing. Traditional blade icing detection methods are usually based on manual inspection or external sensors/tools, but these techniques are limited by human expertise and additional costs. Model-based methods are highly dependent on prior domain knowledge and prone to misinterpretation. Data-driven approaches can offer promising solutions but require a massive amount of labeled training data, which are not generally available. In addition, the data collected for icing detection tend to be imbalanced because, most of the time, wind turbines operate under normal conditions. To address these challenges, this article presents a novel deep class-imbalanced semisupervised (DCISS) model for estimating blade icing conditions. DCISS integrates class-imbalanced and semisupervised learning (SSL) using a prototypical network that can rebalance features and measure the similarities between labeled and unlabeled samples. In addition, a channel calibration attention module is proposed to improve the ability to extract features from raw data. The proposed model has been evaluated using the blade icing datasets of three wind turbines. Compared to the classical anomaly detection and state-of-the-art SSL algorithms, DCISS shows significant advantages in terms of accuracy. Compared to five different class-imbalanced loss functions, the proposed DCISS is competitive. The generalization and practicability of the proposed model are further verified in the use case of online estimation." @default.
- W3189124218 created "2021-08-16" @default.
- W3189124218 creator A5007813793 @default.
- W3189124218 creator A5007910454 @default.
- W3189124218 creator A5015017142 @default.
- W3189124218 creator A5055627037 @default.
- W3189124218 creator A5070105853 @default.
- W3189124218 date "2022-06-01" @default.
- W3189124218 modified "2023-10-17" @default.
- W3189124218 title "A Novel Deep Class-Imbalanced Semisupervised Model for Wind Turbine Blade Icing Detection" @default.
- W3189124218 cites W135909296 @default.
- W3189124218 cites W1850325679 @default.
- W3189124218 cites W1975257359 @default.
- W3189124218 cites W1979255715 @default.
- W3189124218 cites W1979574309 @default.
- W3189124218 cites W2019597688 @default.
- W3189124218 cites W2052624719 @default.
- W3189124218 cites W2110459180 @default.
- W3189124218 cites W2164350298 @default.
- W3189124218 cites W2194775991 @default.
- W3189124218 cites W2237307454 @default.
- W3189124218 cites W2296719434 @default.
- W3189124218 cites W2460123945 @default.
- W3189124218 cites W2494043212 @default.
- W3189124218 cites W2551393996 @default.
- W3189124218 cites W2577730019 @default.
- W3189124218 cites W2581983145 @default.
- W3189124218 cites W2625024245 @default.
- W3189124218 cites W2744652752 @default.
- W3189124218 cites W2752782242 @default.
- W3189124218 cites W2765719317 @default.
- W3189124218 cites W2767106145 @default.
- W3189124218 cites W2773552104 @default.
- W3189124218 cites W2783323081 @default.
- W3189124218 cites W2793272596 @default.
- W3189124218 cites W2800423051 @default.
- W3189124218 cites W2807955733 @default.
- W3189124218 cites W2808496542 @default.
- W3189124218 cites W2810417023 @default.
- W3189124218 cites W2886121007 @default.
- W3189124218 cites W2894034629 @default.
- W3189124218 cites W2898246918 @default.
- W3189124218 cites W2923403134 @default.
- W3189124218 cites W2949848919 @default.
- W3189124218 cites W2954120858 @default.
- W3189124218 cites W2963351448 @default.
- W3189124218 cites W2963691377 @default.
- W3189124218 cites W2967791425 @default.
- W3189124218 cites W2981841850 @default.
- W3189124218 cites W2988384045 @default.
- W3189124218 cites W2996972709 @default.
- W3189124218 cites W2998010409 @default.
- W3189124218 cites W2998782644 @default.
- W3189124218 cites W3009883635 @default.
- W3189124218 cites W3135550350 @default.
- W3189124218 cites W317670 @default.
- W3189124218 cites W4254182148 @default.
- W3189124218 doi "https://doi.org/10.1109/tnnls.2021.3102514" @default.
- W3189124218 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34383657" @default.
- W3189124218 hasPublicationYear "2022" @default.
- W3189124218 type Work @default.
- W3189124218 sameAs 3189124218 @default.
- W3189124218 citedByCount "20" @default.
- W3189124218 countsByYear W31891242182022 @default.
- W3189124218 countsByYear W31891242182023 @default.
- W3189124218 crossrefType "journal-article" @default.
- W3189124218 hasAuthorship W3189124218A5007813793 @default.
- W3189124218 hasAuthorship W3189124218A5007910454 @default.
- W3189124218 hasAuthorship W3189124218A5015017142 @default.
- W3189124218 hasAuthorship W3189124218A5055627037 @default.
- W3189124218 hasAuthorship W3189124218A5070105853 @default.
- W3189124218 hasBestOaLocation W31891242182 @default.
- W3189124218 hasConcept C119599485 @default.
- W3189124218 hasConcept C119857082 @default.
- W3189124218 hasConcept C121332964 @default.
- W3189124218 hasConcept C124101348 @default.
- W3189124218 hasConcept C127413603 @default.
- W3189124218 hasConcept C134306372 @default.
- W3189124218 hasConcept C153294291 @default.
- W3189124218 hasConcept C154945302 @default.
- W3189124218 hasConcept C161067210 @default.
- W3189124218 hasConcept C177148314 @default.
- W3189124218 hasConcept C2777212361 @default.
- W3189124218 hasConcept C2778449969 @default.
- W3189124218 hasConcept C2781439067 @default.
- W3189124218 hasConcept C33923547 @default.
- W3189124218 hasConcept C41008148 @default.
- W3189124218 hasConcept C739882 @default.
- W3189124218 hasConcept C78519656 @default.
- W3189124218 hasConcept C78600449 @default.
- W3189124218 hasConceptScore W3189124218C119599485 @default.
- W3189124218 hasConceptScore W3189124218C119857082 @default.
- W3189124218 hasConceptScore W3189124218C121332964 @default.
- W3189124218 hasConceptScore W3189124218C124101348 @default.
- W3189124218 hasConceptScore W3189124218C127413603 @default.
- W3189124218 hasConceptScore W3189124218C134306372 @default.
- W3189124218 hasConceptScore W3189124218C153294291 @default.
- W3189124218 hasConceptScore W3189124218C154945302 @default.