Matches in SemOpenAlex for { <https://semopenalex.org/work/W4225647516> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W4225647516 endingPage "9" @default.
- W4225647516 startingPage "1" @default.
- W4225647516 abstract "Series arc fault is the main cause of electrical fire. Because of the complex load types and the randomness of arc fault in low-voltage distribution system, it is difficult to obtain fault data for data-driven machine learning algorithm to achieve higher accuracy. Therefore, an arc fault detection model based on residual network (ResNet) is proposed from the perspective of computer vision, and an appropriate data enhancement method is given. We first analyze the time-domain current signals of different load arc faults by multilayer discrete wavelet analysis. The obtained five-layer discrete wavelet detail values are fused into a matrix, and the coefficient matrix is then converted into a phase space image (RGB color space) using a colormap index. The phase space feature map is made into a classification dataset according to the load and fault type, which is input into ResNet in the form of three channels (RGB) for convolution and classification recognition. Then, aiming at the over-fitting phenomenon of neural network caused by small sample, a data enhancement method based on wavelet compression reconstruction is proposed. Finally, we set up different datasets to compare our method with typical neural network regularization methods. The results show that our method effectively solves the over-fitting phenomenon of deep network ResNet152 and improves the accuracy of ResNet 50/101/152 to 97.91%, 96.30%, and 97.69%, respectively." @default.
- W4225647516 created "2022-05-05" @default.
- W4225647516 creator A5007322337 @default.
- W4225647516 creator A5045458929 @default.
- W4225647516 creator A5058125509 @default.
- W4225647516 creator A5064374180 @default.
- W4225647516 date "2022-01-01" @default.
- W4225647516 modified "2023-09-29" @default.
- W4225647516 title "Series Arc Fault Detection Based on Wavelet Compression Reconstruction Data Enhancement and Deep Residual Network" @default.
- W4225647516 cites W2121302592 @default.
- W4225647516 cites W2163458461 @default.
- W4225647516 cites W2194775991 @default.
- W4225647516 cites W2471080557 @default.
- W4225647516 cites W2556945219 @default.
- W4225647516 cites W2572569838 @default.
- W4225647516 cites W2607037079 @default.
- W4225647516 cites W2903983890 @default.
- W4225647516 cites W2944303778 @default.
- W4225647516 cites W2963045696 @default.
- W4225647516 cites W3010981032 @default.
- W4225647516 cites W3020942003 @default.
- W4225647516 cites W3046966979 @default.
- W4225647516 cites W3090996479 @default.
- W4225647516 cites W3115226550 @default.
- W4225647516 cites W3131860858 @default.
- W4225647516 cites W3148072096 @default.
- W4225647516 cites W3191546606 @default.
- W4225647516 doi "https://doi.org/10.1109/tim.2022.3158990" @default.
- W4225647516 hasPublicationYear "2022" @default.
- W4225647516 type Work @default.
- W4225647516 citedByCount "8" @default.
- W4225647516 countsByYear W42256475162022 @default.
- W4225647516 countsByYear W42256475162023 @default.
- W4225647516 crossrefType "journal-article" @default.
- W4225647516 hasAuthorship W4225647516A5007322337 @default.
- W4225647516 hasAuthorship W4225647516A5045458929 @default.
- W4225647516 hasAuthorship W4225647516A5058125509 @default.
- W4225647516 hasAuthorship W4225647516A5064374180 @default.
- W4225647516 hasConcept C11413529 @default.
- W4225647516 hasConcept C127313418 @default.
- W4225647516 hasConcept C153180895 @default.
- W4225647516 hasConcept C154945302 @default.
- W4225647516 hasConcept C155512373 @default.
- W4225647516 hasConcept C155777637 @default.
- W4225647516 hasConcept C165205528 @default.
- W4225647516 hasConcept C175551986 @default.
- W4225647516 hasConcept C196216189 @default.
- W4225647516 hasConcept C41008148 @default.
- W4225647516 hasConcept C47432892 @default.
- W4225647516 hasConcept C50644808 @default.
- W4225647516 hasConcept C83665646 @default.
- W4225647516 hasConceptScore W4225647516C11413529 @default.
- W4225647516 hasConceptScore W4225647516C127313418 @default.
- W4225647516 hasConceptScore W4225647516C153180895 @default.
- W4225647516 hasConceptScore W4225647516C154945302 @default.
- W4225647516 hasConceptScore W4225647516C155512373 @default.
- W4225647516 hasConceptScore W4225647516C155777637 @default.
- W4225647516 hasConceptScore W4225647516C165205528 @default.
- W4225647516 hasConceptScore W4225647516C175551986 @default.
- W4225647516 hasConceptScore W4225647516C196216189 @default.
- W4225647516 hasConceptScore W4225647516C41008148 @default.
- W4225647516 hasConceptScore W4225647516C47432892 @default.
- W4225647516 hasConceptScore W4225647516C50644808 @default.
- W4225647516 hasConceptScore W4225647516C83665646 @default.
- W4225647516 hasFunder F4320321001 @default.
- W4225647516 hasLocation W42256475161 @default.
- W4225647516 hasOpenAccess W4225647516 @default.
- W4225647516 hasPrimaryLocation W42256475161 @default.
- W4225647516 hasRelatedWork W1502966458 @default.
- W4225647516 hasRelatedWork W1577789985 @default.
- W4225647516 hasRelatedWork W2005597290 @default.
- W4225647516 hasRelatedWork W2112061901 @default.
- W4225647516 hasRelatedWork W2123869488 @default.
- W4225647516 hasRelatedWork W2144834862 @default.
- W4225647516 hasRelatedWork W2170982247 @default.
- W4225647516 hasRelatedWork W2542738961 @default.
- W4225647516 hasRelatedWork W3217351357 @default.
- W4225647516 hasRelatedWork W4280495727 @default.
- W4225647516 hasVolume "71" @default.
- W4225647516 isParatext "false" @default.
- W4225647516 isRetracted "false" @default.
- W4225647516 workType "article" @default.