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Metadados

1. Identificação
Tipo de ReferênciaCapítulo de Livro (Book Section)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/48AF37B
Repositóriosid.inpe.br/mtc-m21d/2023/01.02.16.45
Repositório de Metadadossid.inpe.br/mtc-m21d/2023/01.02.16.45.58
Última Atualização dos Metadados2023:01.04.04.24.50 (UTC) administrator
Chave SecundáriaINPE--/
DOI10.1007/978-3-031-21689-3_37
Chave de CitaçãoMaximianoSantShig:2022:ArNeNe
TítuloArtificial Neural Networks to Analyze Energy Consumption and Temperature of UAV On-Board Computers Executing Algorithms for Object Detection
Ano2022
Data de Acesso08 maio 2024
Tipo SecundárioPRE LI
2. Contextualização
Autor1 Maximiano, Renato de Sousa
2 Santiago Júnior, Valdivino Alexandre de
3 Shiguemori, Elcio Hideiti
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JJB5
ORCID1 0000-0001-5953-3483
Grupo1 CAP-COMP-DIPGR-INPE-MCTI-GOV-BR
2 COPDT-CGIP-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 renato.s.maximiano@gmail.com
2 valdivino.santiago@inpe.br
EditorXavier-Junior, J. C.
Rios, R. A
Título do LivroIntelligent Systems: BRACIS 2022
Editora (Publisher)Springer
Páginas523-538
Histórico (UTC)2023-01-02 16:46:34 :: simone -> administrator :: 2022
2023-01-04 04:24:50 :: administrator -> simone :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveUAVs
artificial neural networks
deep learning
object detection
energy consumption
temperature
ResumoWhen incorporating object detection models into unmanned aerial vehicles (UAVs) on-board computers, two aspects are relevant aspects. Firstly, the energy consumption required by the computer on board the UAV during the mission, since low-cost electric UAVs currently have low flight autonomy. Moreover, during the mission, the computers processor may suffer overheating caused by the running algorithm, which may directly impair the continuity of a given task or burn the computer. In this study, we aim to estimate the energy consumption and make temperature predictions of a computer embedded in UAVs for missions involving object detection. We propose a method, Analyzing Energy Consumption and Temperature of On-board computer of UAVs via Neural Networks (ETOUNN), which uses a multilayer perceptron (MLP) network to estimate the energy consumption and a long short-term memory (LSTM) network for predicting temperature. Our experiment relied on a Raspberry Pi 4 8 GB computer running nine popular models of object detectors (deep neural networks): eight of which are pre-trained models of the YOLO family, and one Mask R-CNN network. Regarding energy consumption, we compared our method to multivariate and simple regression-based on two metrics: mean squared error (MSE) and the R2 regression score function. As for temperature prediction and considering the same metrics, ETOUNN was compared to the Autoregressive Integrated Moving Average (ARIMA), the Neural Basis Expansion Analysis for interpretable Time Series forecasting (N-BEATS), and a gated recurrent unit (GRU) network. In both comparisons, our method presented superior performances, showing that it is a promising strategy.
ÁreaCOMP
Arranjo 1urlib.net > CAP > Artificial Neural Networks...
Arranjo 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Artificial Neural Networks...
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4. Condições de acesso e uso
Idiomaen
Grupo de Usuáriossimone
Visibilidadeshown
Permissão de Leituradeny from all and allow from 150.163
5. Fontes relacionadas
Repositório Espelhourlib.net/www/2021/06.04.03.40.25
Unidades Imediatamente Superiores8JMKD3MGPCW/3F2PHGS
8JMKD3MGPCW/46KUES5
DivulgaçãoBNDEPOSITOLEGAL
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
NotasLecture Notes in Computer Science, 13654
Campos Vaziosarchivingpolicy archivist callnumber city copyholder copyright creatorhistory descriptionlevel documentstage e-mailaddress edition format isbn issn label lineage mark nextedition numberoffiles numberofvolumes parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarymark serieseditor seriestitle session shorttitle size sponsor subject targetfile tertiarymark tertiarytype translator url volume
7. Controle da descrição
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