Matches in SemOpenAlex for { <https://semopenalex.org/work/W2802696543> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W2802696543 endingPage "100" @default.
- W2802696543 startingPage "91" @default.
- W2802696543 abstract "Monitoring the temperature of perishable food along the supply chain using a limited number of temperature sensors per shipment is required for wide-scale implementation of quality-driven distribution. In this work, we propose to leverage the theoretical foundation and generalisation ability of a physical heat transfer model to develop a flexible neural net framework which can predict temperatures in real-time. More specifically, the temperature distribution inside a pallet subjected to different ambient temperatures are generated from a validated heat transfer model, and used to train a neural network. Simulations show that the neural network can predict the temperature distribution inside a pallet with an average error below 0.5 K in a one-sensor-per-pallet scenario when the sensor is properly located inside the pallet. Placing the temperature sensor at the corner of the pallet provides a high information content with strong correlations to the other locations inside the pallet to maximise the accuracy of the temperature estimates. The application of an ensemble operator to combine the predictions from multiple randomly seeded neural networks improved by up to 35% the accuracy of the temperature estimates. Finally, the introduction of small Gaussian noise in the training data is an efficient approach to improve the generalisation ability of the neural network and improved by nearly 45% the accuracy of the temperature prediction in the presence of noisy temperature sensors." @default.
- W2802696543 created "2018-05-17" @default.
- W2802696543 creator A5073587241 @default.
- W2802696543 creator A5089703583 @default.
- W2802696543 date "2018-07-01" @default.
- W2802696543 modified "2023-10-12" @default.
- W2802696543 title "Neural network models for predicting perishable food temperatures along the supply chain" @default.
- W2802696543 cites W1966094435 @default.
- W2802696543 cites W1995966345 @default.
- W2802696543 cites W2000592181 @default.
- W2802696543 cites W2015043754 @default.
- W2802696543 cites W2024581155 @default.
- W2802696543 cites W2058111307 @default.
- W2802696543 cites W2060279468 @default.
- W2802696543 cites W2088273601 @default.
- W2802696543 cites W2093884763 @default.
- W2802696543 cites W2112315732 @default.
- W2802696543 cites W2114049530 @default.
- W2802696543 cites W2149905014 @default.
- W2802696543 cites W2283762936 @default.
- W2802696543 cites W2411736285 @default.
- W2802696543 cites W2528455139 @default.
- W2802696543 cites W2582207185 @default.
- W2802696543 cites W2589280428 @default.
- W2802696543 cites W2590040475 @default.
- W2802696543 cites W2619807422 @default.
- W2802696543 doi "https://doi.org/10.1016/j.biosystemseng.2018.04.016" @default.
- W2802696543 hasPublicationYear "2018" @default.
- W2802696543 type Work @default.
- W2802696543 sameAs 2802696543 @default.
- W2802696543 citedByCount "36" @default.
- W2802696543 countsByYear W28026965432018 @default.
- W2802696543 countsByYear W28026965432019 @default.
- W2802696543 countsByYear W28026965432020 @default.
- W2802696543 countsByYear W28026965432021 @default.
- W2802696543 countsByYear W28026965432022 @default.
- W2802696543 countsByYear W28026965432023 @default.
- W2802696543 crossrefType "journal-article" @default.
- W2802696543 hasAuthorship W2802696543A5073587241 @default.
- W2802696543 hasAuthorship W2802696543A5089703583 @default.
- W2802696543 hasConcept C108713360 @default.
- W2802696543 hasConcept C144133560 @default.
- W2802696543 hasConcept C154945302 @default.
- W2802696543 hasConcept C155373166 @default.
- W2802696543 hasConcept C162853370 @default.
- W2802696543 hasConcept C18903297 @default.
- W2802696543 hasConcept C2992402296 @default.
- W2802696543 hasConcept C37621935 @default.
- W2802696543 hasConcept C39432304 @default.
- W2802696543 hasConcept C41008148 @default.
- W2802696543 hasConcept C50644808 @default.
- W2802696543 hasConcept C86803240 @default.
- W2802696543 hasConceptScore W2802696543C108713360 @default.
- W2802696543 hasConceptScore W2802696543C144133560 @default.
- W2802696543 hasConceptScore W2802696543C154945302 @default.
- W2802696543 hasConceptScore W2802696543C155373166 @default.
- W2802696543 hasConceptScore W2802696543C162853370 @default.
- W2802696543 hasConceptScore W2802696543C18903297 @default.
- W2802696543 hasConceptScore W2802696543C2992402296 @default.
- W2802696543 hasConceptScore W2802696543C37621935 @default.
- W2802696543 hasConceptScore W2802696543C39432304 @default.
- W2802696543 hasConceptScore W2802696543C41008148 @default.
- W2802696543 hasConceptScore W2802696543C50644808 @default.
- W2802696543 hasConceptScore W2802696543C86803240 @default.
- W2802696543 hasLocation W28026965431 @default.
- W2802696543 hasOpenAccess W2802696543 @default.
- W2802696543 hasPrimaryLocation W28026965431 @default.
- W2802696543 hasRelatedWork W2069174614 @default.
- W2802696543 hasRelatedWork W2384691534 @default.
- W2802696543 hasRelatedWork W2496245697 @default.
- W2802696543 hasRelatedWork W2769626444 @default.
- W2802696543 hasRelatedWork W3014996959 @default.
- W2802696543 hasRelatedWork W3081126083 @default.
- W2802696543 hasRelatedWork W3180702083 @default.
- W2802696543 hasRelatedWork W3197246881 @default.
- W2802696543 hasRelatedWork W4213433994 @default.
- W2802696543 hasRelatedWork W222103456 @default.
- W2802696543 hasVolume "171" @default.
- W2802696543 isParatext "false" @default.
- W2802696543 isRetracted "false" @default.
- W2802696543 magId "2802696543" @default.
- W2802696543 workType "article" @default.