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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W/49A5GE8
Repositorysid.inpe.br/plutao/2023/06.15.14.34   (restricted access)
Last Update2023:06.20.13.20.11 (UTC) lattes
Metadata Repositorysid.inpe.br/plutao/2023/06.15.14.34.06
Metadata Last Update2024:01.02.17.00.35 (UTC) administrator
DOI10.5540/tcam.2022.024.01.00159
ISSN2676-0029
Labellattes: 5142426481528206 2 PenhaNetoCampShig:2023:DaSeTr
Citation KeyPenhaNetoCampShig:2023:DaSeTr
TitleData Selection for Training the Neural Fuser Applied to Autonomous UAV Navigation
Year2023
Access Date2024, May 17
Secondary TypePRE PI
Number of Files1
Size351 KiB
2. Context
Author1 Penha Neto, Gerson da
2 Campos Velho, Haroldo Fraga de
3 Shiguemori, Elcio Hideiti
Resume Identifier1
2 8JMKD3MGP5W/3C9JHC3
Group1
2 COPDT-CGIP-INPE-MCTI-GOV-BR
Affiliation1 Faculdade de Tecnologia (FATEC)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto de Estudos Avanc¸ados (IEAv)
Author e-Mail Address1 gerson.penha@inpe.br
2 haroldo.camposvelho@gmail.com
3 elcio@ieav.cta.br
JournalTrends in Computational and Applied Mathematics
Volume24
Number1
Pages159-175
History (UTC)2023-06-15 14:34:06 :: lattes -> administrator ::
2023-06-19 22:18:29 :: administrator -> lattes :: 2023
2023-06-20 13:20:12 :: lattes -> administrator :: 2023
2024-01-02 17:00:35 :: administrator -> simone :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsSelf-configured neural network
Unmanned aerial vehicle (UAV)
Cross-validation
k-fold
AbstractOver the past few years, there has been a steady increase in the use of aircraft vehicles, in particular unmanned aerial vehicles (UAV). UAV navigation is generally controlled by a human pilot. But the challenge for the scientific community is to carry out autonomous navigation. Some solutions have been proposed for the UAV autonomous navigation. Studies indicate as a solution to use data fusion and/or image processing navigation. Kalman Filter (KF) can be employed as a data fuser, but the KF has disadvantages. An alternative to the KF is based on artificial intelligence. Here, the KF is replaced by a self-configured neural network. This work investigates a way to select data for training the neural fuser, based on crossvalidation techniques. The results are compared to the data fusion done by a KF.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Data Selection for...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languagept
Target FileMzcHRCYbQHYGM6M7tzNFMnF.pdf
User Grouplattes
Reader Groupadministrator
lattes
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUES5
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.49.40 3
sid.inpe.br/bibdigital/2022/04.03.23.11 1
URL (untrusted data)https://tema.sbmac.org.br/tema
DisseminationPORTALCAPES
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Empty Fieldsalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn lineage mark mirrorrepository month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype typeofwork
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