dc.creatorHector Jaime, Dulce-Moreno
dc.creatorContreras Contreras, G F
dc.creatorArdila Melo, R
dc.date.accessioned2021-11-26T16:46:47Z
dc.date.accessioned2022-09-28T18:39:04Z
dc.date.available2021-11-26T16:46:47Z
dc.date.available2022-09-28T18:39:04Z
dc.date.created2021-11-26T16:46:47Z
dc.date.issued2019-11-29
dc.identifierhttp://repositorio.ufps.edu.co/handle/ufps/1457
dc.identifier10.1088/1742-6596/1386/1/012070
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3707439
dc.description.abstractThis work takes thermodynamic modelling through computer science for incubation process at domestic birds, that has presented energy consumption significantly high than energy used in processes. Thus, a data analysis was applied upon variables of temperature and relative humidity for heating zones, trying to know how much energy supplied by source was used, as well as, voltage and current variables are measured in the same moment that temperature and relative humidity are acquired. Then, data analysis was done using artificial neural networks models with samples obtained from sensors, where real process is highly time- variant, fixing environment conditions at the moment required. Therefore, with this system has been obtained an air flow of 3.4375 10−2 m3/J using a anemometer respect to electrical energy supplied by fans, giving 9.4818 W of average power using ceramics resistances, and testing an adaptive controller where its variables are fitted using equations obtained from data analysis. In contrast, colombian farmers have decreased economic conditions to maintain them productions due to free trade agreements implemented lastly, indeed this system was developed using open- source software and hardware to avoid costs in acquisition by licensing politicians or periodic subscription to a specific product developed by companies.
dc.languageeng
dc.publisherJournal of Physics: Conference Series
dc.publisherBogota , Colombia
dc.relationJournal of Physics: Conference Series
dc.relationVol.1386 No.1.(2019)
dc.relation7
dc.relation1 (2019)
dc.relation1
dc.relation1386
dc.relationContreras, G. C., Dulcé-Moreno, H. J., & Melo, R. A. (2019, November). Arduino data-logger and artificial neural network to data analysis. In Journal of Physics: Conference Series (Vol. 1386, No. 1, p. 012070). IOP Publishing.
dc.relationJournal of Physics: Conference Series
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rightsContent from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd
dc.sourcehttps://iopscience.iop.org/article/10.1088/1742-6596/1386/1/012070/meta
dc.titleArduino data-logger and artificial neural network to data analysis
dc.typeArtículos de revistas


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