FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3047717419> ?p ?o ?g. }

• W3047717419 endingPage "110386" @default.
• W3047717419 startingPage "110386" @default.
• W3047717419 abstract "Abstract This paper introduces a vision-based deep learning approach that enables the detection and recognition of occupants’ activities within building spaces. The data can feed into building energy management systems through the establishment of occupancy heat emission profiles, which can help minimise unnecessary heating, ventilation, and air-conditioning (HVAC) energy loads and effectively manage indoor conditions. The proposed demand-driven method can enable HVAC systems to adapt and make a timely response to dynamic changes of occupancy, instead of using “static” or fixed occupancy operation schedules, historical load, and time factor. Based on a convolutional neural network, the model was developed to enable occupancy activity detection using a camera. Training data was obtained from online image sources and captured images of various occupant activities in office spaces. Tests were performed by real-time live detection and predictions of occupancy activities in buildings. Initial activities response includes sitting, standing, walking, and napping. Average detection accuracy of 80.62% was achieved. The detection formed the real-time occupancy heat emission profiles known as the Deep Learning Influenced Profile. Along with typical ‘scheduled’ office occupancy profiles, a building energy simulation (BES) tool was used to further assess the framework. An office space in Nottingham, UK was selected to test the proposed method and modelled using building simulation. Using the deep learning detection method, the results showed that the occupancy heat gains could be represented more accurately in comparison to using static office occupancy profiles. The accurate detection of occupants and their activities can also be used to effectively estimate CO2 concentration. The information can be useful for modulating ventilation systems leading to better indoor environmental quality. Overall, this initial approach of the study showed the capabilities of this framework for detecting occupancy activities and providing reliable predictions of building internal gains." @default.
• W3047717419 created "2020-08-13" @default.
• W3047717419 creator A5014366665 @default.
• W3047717419 creator A5014917862 @default.
• W3047717419 creator A5034291041 @default.
• W3047717419 creator A5072860557 @default.
• W3047717419 creator A5079954759 @default.
• W3047717419 date "2020-11-01" @default.
• W3047717419 modified "2023-10-16" @default.
• W3047717419 title "A vision-based deep learning approach for the detection and prediction of occupancy heat emissions for demand-driven control solutions" @default.
• W3047717419 cites W1973579937 @default.
• W3047717419 cites W1992714701 @default.
• W3047717419 cites W1995367824 @default.
• W3047717419 cites W2035993355 @default.
• W3047717419 cites W2051607409 @default.
• W3047717419 cites W2092061346 @default.
• W3047717419 cites W2520874944 @default.
• W3047717419 cites W2726019568 @default.
• W3047717419 cites W2735687456 @default.
• W3047717419 cites W2737408310 @default.
• W3047717419 cites W2741280287 @default.
• W3047717419 cites W2748041936 @default.
• W3047717419 cites W2752271148 @default.
• W3047717419 cites W2767253745 @default.
• W3047717419 cites W2767389317 @default.
• W3047717419 cites W2778221222 @default.
• W3047717419 cites W2778730799 @default.
• W3047717419 cites W2790918434 @default.
• W3047717419 cites W2791379861 @default.
• W3047717419 cites W2796394553 @default.
• W3047717419 cites W2803818130 @default.
• W3047717419 cites W2804243520 @default.
• W3047717419 cites W2806050787 @default.
• W3047717419 cites W2806252395 @default.
• W3047717419 cites W2809417902 @default.
• W3047717419 cites W2836487175 @default.
• W3047717419 cites W2884283518 @default.
• W3047717419 cites W2885580664 @default.
• W3047717419 cites W2893103709 @default.
• W3047717419 cites W2895294973 @default.
• W3047717419 cites W2901645090 @default.
• W3047717419 cites W2903415051 @default.
• W3047717419 cites W2903925216 @default.
• W3047717419 cites W2910292836 @default.
• W3047717419 cites W2910531128 @default.
• W3047717419 cites W2911545037 @default.
• W3047717419 cites W2911794652 @default.
• W3047717419 cites W2912342488 @default.
• W3047717419 cites W2918964466 @default.
• W3047717419 cites W2923708863 @default.
• W3047717419 cites W2937445681 @default.
• W3047717419 cites W2955808496 @default.
• W3047717419 cites W2963937706 @default.
• W3047717419 cites W2965758185 @default.
• W3047717419 cites W2969000828 @default.
• W3047717419 cites W2969536608 @default.
• W3047717419 cites W2972795770 @default.
• W3047717419 cites W2994209110 @default.
• W3047717419 cites W3104996215 @default.
• W3047717419 cites W759590682 @default.
• W3047717419 doi "https://doi.org/10.1016/j.enbuild.2020.110386" @default.
• W3047717419 hasPublicationYear "2020" @default.
• W3047717419 type Work @default.
• W3047717419 sameAs 3047717419 @default.
• W3047717419 citedByCount "39" @default.
• W3047717419 countsByYear W30477174192020 @default.
• W3047717419 countsByYear W30477174192021 @default.
• W3047717419 countsByYear W30477174192022 @default.
• W3047717419 countsByYear W30477174192023 @default.
• W3047717419 crossrefType "journal-article" @default.
• W3047717419 hasAuthorship W3047717419A5014366665 @default.
• W3047717419 hasAuthorship W3047717419A5014917862 @default.
• W3047717419 hasAuthorship W3047717419A5034291041 @default.
• W3047717419 hasAuthorship W3047717419A5072860557 @default.
• W3047717419 hasAuthorship W3047717419A5079954759 @default.
• W3047717419 hasConcept C119857082 @default.
• W3047717419 hasConcept C127413603 @default.
• W3047717419 hasConcept C133731056 @default.
• W3047717419 hasConcept C154945302 @default.
• W3047717419 hasConcept C160331591 @default.
• W3047717419 hasConcept C170154142 @default.
• W3047717419 hasConcept C171146098 @default.
• W3047717419 hasConcept C2775924081 @default.
• W3047717419 hasConcept C2778215892 @default.
• W3047717419 hasConcept C2987858997 @default.
• W3047717419 hasConcept C39432304 @default.
• W3047717419 hasConcept C41008148 @default.
• W3047717419 hasConceptScore W3047717419C119857082 @default.
• W3047717419 hasConceptScore W3047717419C127413603 @default.
• W3047717419 hasConceptScore W3047717419C133731056 @default.
• W3047717419 hasConceptScore W3047717419C154945302 @default.
• W3047717419 hasConceptScore W3047717419C160331591 @default.
• W3047717419 hasConceptScore W3047717419C170154142 @default.
• W3047717419 hasConceptScore W3047717419C171146098 @default.
• W3047717419 hasConceptScore W3047717419C2775924081 @default.
• W3047717419 hasConceptScore W3047717419C2778215892 @default.
• W3047717419 hasConceptScore W3047717419C2987858997 @default.
• W3047717419 hasConceptScore W3047717419C39432304 @default.

• next