Matches in SemOpenAlex for { <https://semopenalex.org/work/W3008556716> ?p ?o ?g. }
- W3008556716 endingPage "101055" @default.
- W3008556716 startingPage "101055" @default.
- W3008556716 abstract "Kansei evaluation is crucial to the process of Kansei engineering. However, traditional methods are subjective and random. In order to eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency, a novel automatic evaluation and labeling architecture for product Kansei attributes was proposed in this paper based on Convolutional Neural Networks (CNNs). The architecture consists of two modules: (1) Target detection module (Faster R-CNN was taken as an example), (2) Fine-Grained classification module (DFL-CNN was taken as an example). A case study was provided to validate the proposed architecture. The proposed architecture transformed design evaluation tasks into the recognition and classification tasks. The experiments achieved 98.837%, 96.899%, 86.047%, and 81.008% accuracy in the binary, triple, and two five-classification tasks, respectively. Our results proved the feasibility of using computer vision to mimic human vision for the automatic evaluation of Kansei attributes." @default.
- W3008556716 created "2020-03-06" @default.
- W3008556716 creator A5010443535 @default.
- W3008556716 creator A5027124033 @default.
- W3008556716 creator A5042295110 @default.
- W3008556716 creator A5045997631 @default.
- W3008556716 creator A5053056639 @default.
- W3008556716 creator A5083135645 @default.
- W3008556716 date "2020-04-01" @default.
- W3008556716 modified "2023-10-06" @default.
- W3008556716 title "A novel architecture: Using convolutional neural networks for Kansei attributes automatic evaluation and labeling" @default.
- W3008556716 cites W1999209397 @default.
- W3008556716 cites W2050712559 @default.
- W3008556716 cites W2102605133 @default.
- W3008556716 cites W2104657103 @default.
- W3008556716 cites W2130695837 @default.
- W3008556716 cites W2145732253 @default.
- W3008556716 cites W2186193101 @default.
- W3008556716 cites W2194775991 @default.
- W3008556716 cites W2247349754 @default.
- W3008556716 cites W2462457117 @default.
- W3008556716 cites W2480418144 @default.
- W3008556716 cites W2544405876 @default.
- W3008556716 cites W2570343428 @default.
- W3008556716 cites W2601293316 @default.
- W3008556716 cites W2737725206 @default.
- W3008556716 cites W2756269167 @default.
- W3008556716 cites W2788806050 @default.
- W3008556716 cites W2798365843 @default.
- W3008556716 cites W2801875192 @default.
- W3008556716 cites W2803875933 @default.
- W3008556716 cites W2810004461 @default.
- W3008556716 cites W2884367402 @default.
- W3008556716 cites W2885343434 @default.
- W3008556716 cites W2888527098 @default.
- W3008556716 cites W2888784229 @default.
- W3008556716 cites W2896284304 @default.
- W3008556716 cites W2900047055 @default.
- W3008556716 cites W2904331173 @default.
- W3008556716 cites W2905156231 @default.
- W3008556716 cites W2909450332 @default.
- W3008556716 cites W2912675002 @default.
- W3008556716 cites W2915385321 @default.
- W3008556716 cites W2943898222 @default.
- W3008556716 cites W2955805844 @default.
- W3008556716 cites W2963037989 @default.
- W3008556716 cites W3106250896 @default.
- W3008556716 doi "https://doi.org/10.1016/j.aei.2020.101055" @default.
- W3008556716 hasPublicationYear "2020" @default.
- W3008556716 type Work @default.
- W3008556716 sameAs 3008556716 @default.
- W3008556716 citedByCount "20" @default.
- W3008556716 countsByYear W30085567162020 @default.
- W3008556716 countsByYear W30085567162021 @default.
- W3008556716 countsByYear W30085567162022 @default.
- W3008556716 countsByYear W30085567162023 @default.
- W3008556716 crossrefType "journal-article" @default.
- W3008556716 hasAuthorship W3008556716A5010443535 @default.
- W3008556716 hasAuthorship W3008556716A5027124033 @default.
- W3008556716 hasAuthorship W3008556716A5042295110 @default.
- W3008556716 hasAuthorship W3008556716A5045997631 @default.
- W3008556716 hasAuthorship W3008556716A5053056639 @default.
- W3008556716 hasAuthorship W3008556716A5083135645 @default.
- W3008556716 hasBestOaLocation W30085567161 @default.
- W3008556716 hasConcept C107457646 @default.
- W3008556716 hasConcept C111919701 @default.
- W3008556716 hasConcept C119857082 @default.
- W3008556716 hasConcept C123657996 @default.
- W3008556716 hasConcept C142362112 @default.
- W3008556716 hasConcept C153180895 @default.
- W3008556716 hasConcept C153349607 @default.
- W3008556716 hasConcept C154945302 @default.
- W3008556716 hasConcept C2780562538 @default.
- W3008556716 hasConcept C2781297728 @default.
- W3008556716 hasConcept C41008148 @default.
- W3008556716 hasConcept C50644808 @default.
- W3008556716 hasConcept C81363708 @default.
- W3008556716 hasConcept C98045186 @default.
- W3008556716 hasConceptScore W3008556716C107457646 @default.
- W3008556716 hasConceptScore W3008556716C111919701 @default.
- W3008556716 hasConceptScore W3008556716C119857082 @default.
- W3008556716 hasConceptScore W3008556716C123657996 @default.
- W3008556716 hasConceptScore W3008556716C142362112 @default.
- W3008556716 hasConceptScore W3008556716C153180895 @default.
- W3008556716 hasConceptScore W3008556716C153349607 @default.
- W3008556716 hasConceptScore W3008556716C154945302 @default.
- W3008556716 hasConceptScore W3008556716C2780562538 @default.
- W3008556716 hasConceptScore W3008556716C2781297728 @default.
- W3008556716 hasConceptScore W3008556716C41008148 @default.
- W3008556716 hasConceptScore W3008556716C50644808 @default.
- W3008556716 hasConceptScore W3008556716C81363708 @default.
- W3008556716 hasConceptScore W3008556716C98045186 @default.
- W3008556716 hasFunder F4320337504 @default.
- W3008556716 hasLocation W30085567161 @default.
- W3008556716 hasLocation W30085567162 @default.
- W3008556716 hasOpenAccess W3008556716 @default.
- W3008556716 hasPrimaryLocation W30085567161 @default.
- W3008556716 hasRelatedWork W2109790545 @default.