Matches in SemOpenAlex for { <https://semopenalex.org/work/W3097740275> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W3097740275 endingPage "7488" @default.
- W3097740275 startingPage "7488" @default.
- W3097740275 abstract "The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm design of the wood images. The simple linear iterative clustering algorithm is used to improve the initial model; the obtained image features are used as ELM classification inputs. ELM has faster training speed and stronger generalization ability than other similar neural networks, but the random selection of input weights and thresholds degrades the classification accuracy. A genetic algorithm is used here to optimize the initial parameters of the ELM to stabilize the network classification performance. The depth extreme learning machine can extract high-level abstract information from the data, does not require iterative adjustment of the network weights, has high calculation efficiency, and allows CNN to effectively extract the wood defect contour. The distributed input data feature is automatically expressed in layer form by deep learning pre-training. The wood defect recognition accuracy reached 96.72% in a test time of only 187 ms." @default.
- W3097740275 created "2020-11-09" @default.
- W3097740275 creator A5016674135 @default.
- W3097740275 creator A5021297497 @default.
- W3097740275 creator A5032380287 @default.
- W3097740275 creator A5044139442 @default.
- W3097740275 creator A5075286263 @default.
- W3097740275 date "2020-10-24" @default.
- W3097740275 modified "2023-10-12" @default.
- W3097740275 title "Wood Defect Detection Based on Depth Extreme Learning Machine" @default.
- W3097740275 cites W1574066498 @default.
- W3097740275 cites W2000904666 @default.
- W3097740275 cites W2070369244 @default.
- W3097740275 cites W2097117768 @default.
- W3097740275 cites W2301541953 @default.
- W3097740275 cites W2412782625 @default.
- W3097740275 cites W242877468 @default.
- W3097740275 cites W2465628751 @default.
- W3097740275 cites W2605958287 @default.
- W3097740275 cites W2610628308 @default.
- W3097740275 cites W2746841293 @default.
- W3097740275 cites W2751834810 @default.
- W3097740275 cites W2770038176 @default.
- W3097740275 cites W2783015445 @default.
- W3097740275 cites W2801405214 @default.
- W3097740275 cites W2883184371 @default.
- W3097740275 cites W2919115771 @default.
- W3097740275 cites W2920016200 @default.
- W3097740275 cites W2930641970 @default.
- W3097740275 cites W2994590618 @default.
- W3097740275 cites W3014030691 @default.
- W3097740275 cites W3016224420 @default.
- W3097740275 cites W3047236570 @default.
- W3097740275 doi "https://doi.org/10.3390/app10217488" @default.
- W3097740275 hasPublicationYear "2020" @default.
- W3097740275 type Work @default.
- W3097740275 sameAs 3097740275 @default.
- W3097740275 citedByCount "26" @default.
- W3097740275 countsByYear W30977402752021 @default.
- W3097740275 countsByYear W30977402752022 @default.
- W3097740275 countsByYear W30977402752023 @default.
- W3097740275 crossrefType "journal-article" @default.
- W3097740275 hasAuthorship W3097740275A5016674135 @default.
- W3097740275 hasAuthorship W3097740275A5021297497 @default.
- W3097740275 hasAuthorship W3097740275A5032380287 @default.
- W3097740275 hasAuthorship W3097740275A5044139442 @default.
- W3097740275 hasAuthorship W3097740275A5075286263 @default.
- W3097740275 hasBestOaLocation W30977402751 @default.
- W3097740275 hasConcept C108583219 @default.
- W3097740275 hasConcept C134306372 @default.
- W3097740275 hasConcept C138885662 @default.
- W3097740275 hasConcept C153180895 @default.
- W3097740275 hasConcept C154945302 @default.
- W3097740275 hasConcept C177148314 @default.
- W3097740275 hasConcept C2776401178 @default.
- W3097740275 hasConcept C2780150128 @default.
- W3097740275 hasConcept C33923547 @default.
- W3097740275 hasConcept C41008148 @default.
- W3097740275 hasConcept C41895202 @default.
- W3097740275 hasConcept C45347329 @default.
- W3097740275 hasConcept C50644808 @default.
- W3097740275 hasConcept C52622490 @default.
- W3097740275 hasConcept C81363708 @default.
- W3097740275 hasConceptScore W3097740275C108583219 @default.
- W3097740275 hasConceptScore W3097740275C134306372 @default.
- W3097740275 hasConceptScore W3097740275C138885662 @default.
- W3097740275 hasConceptScore W3097740275C153180895 @default.
- W3097740275 hasConceptScore W3097740275C154945302 @default.
- W3097740275 hasConceptScore W3097740275C177148314 @default.
- W3097740275 hasConceptScore W3097740275C2776401178 @default.
- W3097740275 hasConceptScore W3097740275C2780150128 @default.
- W3097740275 hasConceptScore W3097740275C33923547 @default.
- W3097740275 hasConceptScore W3097740275C41008148 @default.
- W3097740275 hasConceptScore W3097740275C41895202 @default.
- W3097740275 hasConceptScore W3097740275C45347329 @default.
- W3097740275 hasConceptScore W3097740275C50644808 @default.
- W3097740275 hasConceptScore W3097740275C52622490 @default.
- W3097740275 hasConceptScore W3097740275C81363708 @default.
- W3097740275 hasIssue "21" @default.
- W3097740275 hasLocation W30977402751 @default.
- W3097740275 hasLocation W30977402752 @default.
- W3097740275 hasOpenAccess W3097740275 @default.
- W3097740275 hasPrimaryLocation W30977402751 @default.
- W3097740275 hasRelatedWork W2059299633 @default.
- W3097740275 hasRelatedWork W2279398222 @default.
- W3097740275 hasRelatedWork W2732542196 @default.
- W3097740275 hasRelatedWork W2738221750 @default.
- W3097740275 hasRelatedWork W2760085659 @default.
- W3097740275 hasRelatedWork W2773120646 @default.
- W3097740275 hasRelatedWork W3011074480 @default.
- W3097740275 hasRelatedWork W3156786002 @default.
- W3097740275 hasRelatedWork W4299822940 @default.
- W3097740275 hasRelatedWork W4312417841 @default.
- W3097740275 hasVolume "10" @default.
- W3097740275 isParatext "false" @default.
- W3097740275 isRetracted "false" @default.
- W3097740275 magId "3097740275" @default.
- W3097740275 workType "article" @default.