FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3149306041> ?p ?o ?g. }

• W3149306041 endingPage "16" @default.
• W3149306041 startingPage "1" @default.
• W3149306041 abstract "Background/Objective: The primary objective of the present study is to distinguish several visual faults which hinder the performance, reliability and lifetime of photovoltaic (PV) modules. Research question: Conventional fault detection techniques require specific operating conditions which also consumed a lot of time, manpower and expenditure. Innovative techniques and technological advancements in the highly paced world expect instant results. Advanced and automatic fault diagnosis is such a process that delivers instant results and guarantees an extended lifetime for numerous critical photovoltaic module (PVM) components. Hypothesis: This study performs an automatic detection of faults in PVM with convolutional neural networks (CNN) that accurately classifies various faults based on the images captured from unmanned aerial vehicles (UAVs). Methodology: Dataset creation is one of the primary constraints when it comes to working with CNN. To overcome this drawback, a data augmentation method is adopted to enlarge the dataset from the limited number of available aerial images of PVM. These augmented images are fed into an automatic fault detection CNN model for deep feature extraction and classification. Results and Conclusion: The presented method exhibits an increase in the accuracy and performance of PVM health monitoring when compared with other conventional solutions. The performances of uniform and non-uniform datasets are also presented. Various pre-trained models like VGG16 and ResNet50 are compared with the proposed solution for performance evaluation. The results demonstrate that the overall classification accuracy of the proposed model for uniform and non-uniform datasets was found to be 95.07% and 94.14% respectively with lesser training time and number of epochs." @default.
• W3149306041 created "2021-04-13" @default.
• W3149306041 creator A5032534129 @default.
• W3149306041 creator A5084510048 @default.
• W3149306041 date "2021-03-29" @default.
• W3149306041 modified "2023-10-18" @default.
• W3149306041 title "Convolutional Neural Network based Automatic Detection of Visible Faults in a Photovoltaic Module" @default.
• W3149306041 cites W2012028491 @default.
• W3149306041 cites W2087800201 @default.
• W3149306041 cites W210314881 @default.
• W3149306041 cites W2467186333 @default.
• W3149306041 cites W2473336663 @default.
• W3149306041 cites W2539370512 @default.
• W3149306041 cites W2610398407 @default.
• W3149306041 cites W2612672733 @default.
• W3149306041 cites W2616219098 @default.
• W3149306041 cites W2729649855 @default.
• W3149306041 cites W2760217853 @default.
• W3149306041 cites W2770482526 @default.
• W3149306041 cites W2795975941 @default.
• W3149306041 cites W2806915479 @default.
• W3149306041 cites W2825063406 @default.
• W3149306041 cites W2890747436 @default.
• W3149306041 cites W2895777600 @default.
• W3149306041 cites W2903249843 @default.
• W3149306041 cites W2912052330 @default.
• W3149306041 cites W2916091221 @default.
• W3149306041 cites W2971194575 @default.
• W3149306041 cites W2980326480 @default.
• W3149306041 cites W2993737498 @default.
• W3149306041 cites W3006296545 @default.
• W3149306041 cites W3008241461 @default.
• W3149306041 cites W3011781975 @default.
• W3149306041 cites W3047717592 @default.
• W3149306041 cites W3087284879 @default.
• W3149306041 cites W3090941434 @default.
• W3149306041 cites W3093291673 @default.
• W3149306041 cites W3140681322 @default.
• W3149306041 cites W2899022642 @default.
• W3149306041 doi "https://doi.org/10.1080/15567036.2021.1905753" @default.
• W3149306041 hasPublicationYear "2021" @default.
• W3149306041 type Work @default.
• W3149306041 sameAs 3149306041 @default.
• W3149306041 citedByCount "16" @default.
• W3149306041 countsByYear W31493060412022 @default.
• W3149306041 countsByYear W31493060412023 @default.
• W3149306041 crossrefType "journal-article" @default.
• W3149306041 hasAuthorship W3149306041A5032534129 @default.
• W3149306041 hasAuthorship W3149306041A5084510048 @default.
• W3149306041 hasConcept C108583219 @default.
• W3149306041 hasConcept C111919701 @default.
• W3149306041 hasConcept C119599485 @default.
• W3149306041 hasConcept C121332964 @default.
• W3149306041 hasConcept C127313418 @default.
• W3149306041 hasConcept C127413603 @default.
• W3149306041 hasConcept C138885662 @default.
• W3149306041 hasConcept C152745839 @default.
• W3149306041 hasConcept C153180895 @default.
• W3149306041 hasConcept C154945302 @default.
• W3149306041 hasConcept C163258240 @default.
• W3149306041 hasConcept C165205528 @default.
• W3149306041 hasConcept C172707124 @default.
• W3149306041 hasConcept C175551986 @default.
• W3149306041 hasConcept C2776401178 @default.
• W3149306041 hasConcept C41008148 @default.
• W3149306041 hasConcept C41291067 @default.
• W3149306041 hasConcept C41895202 @default.
• W3149306041 hasConcept C43214815 @default.
• W3149306041 hasConcept C52622490 @default.
• W3149306041 hasConcept C62520636 @default.
• W3149306041 hasConcept C79403827 @default.
• W3149306041 hasConcept C81363708 @default.
• W3149306041 hasConcept C98045186 @default.
• W3149306041 hasConceptScore W3149306041C108583219 @default.
• W3149306041 hasConceptScore W3149306041C111919701 @default.
• W3149306041 hasConceptScore W3149306041C119599485 @default.
• W3149306041 hasConceptScore W3149306041C121332964 @default.
• W3149306041 hasConceptScore W3149306041C127313418 @default.
• W3149306041 hasConceptScore W3149306041C127413603 @default.
• W3149306041 hasConceptScore W3149306041C138885662 @default.
• W3149306041 hasConceptScore W3149306041C152745839 @default.
• W3149306041 hasConceptScore W3149306041C153180895 @default.
• W3149306041 hasConceptScore W3149306041C154945302 @default.
• W3149306041 hasConceptScore W3149306041C163258240 @default.
• W3149306041 hasConceptScore W3149306041C165205528 @default.
• W3149306041 hasConceptScore W3149306041C172707124 @default.
• W3149306041 hasConceptScore W3149306041C175551986 @default.
• W3149306041 hasConceptScore W3149306041C2776401178 @default.
• W3149306041 hasConceptScore W3149306041C41008148 @default.
• W3149306041 hasConceptScore W3149306041C41291067 @default.
• W3149306041 hasConceptScore W3149306041C41895202 @default.
• W3149306041 hasConceptScore W3149306041C43214815 @default.
• W3149306041 hasConceptScore W3149306041C52622490 @default.
• W3149306041 hasConceptScore W3149306041C62520636 @default.
• W3149306041 hasConceptScore W3149306041C79403827 @default.
• W3149306041 hasConceptScore W3149306041C81363708 @default.
• W3149306041 hasConceptScore W3149306041C98045186 @default.
• W3149306041 hasLocation W31493060411 @default.

• next