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Search local date and time: 03/03/2024 21:39.
1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Last Update2012: (UTC) marciana
Metadata Last Update2021: (UTC) administrator
Secondary KeyINPE--PRE/
Citation KeyAzevedoAmbrViei:2012:ApDaMi
TitleApplying Data Mining for Detecting Anomalies in Satellites
Access Date2024, Mar. 03
Secondary TypePRE CI
Number of Files1
Size670 KiB
2. Context
Author1 Azevedo, Denise Rotondi
2 Ambrosio, Ana Maria
3 Vieira, Marco
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 CISUC, Department of Informatics Engineering University of Coimbra Coimbra, Portugal
Conference NameEuropean Dependable Computing Conference, 9 (EDCC).
Conference LocationSibiu
Date8-11 May
PublisherAssociation for Computing Machinery
Publisher CityNew York
Book TitleProceedings
History (UTC)2013-01-21 12:44:42 :: marciana -> administrator :: 2012
2021-02-11 21:05:44 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Version Typepublisher
KeywordsClustering algorithms
Telemetering equipment
AbstractTelemetry data is the only source for identifying/predicting anomalies in artificial satellites. Human specialists analyze these data in real time, but its large volume, makes this analysis extremely difficult. In this experience paper we study the hypothesis of using clustering algorithms to help operators and analysts to perform telemetry analysis. Two real cases of satellite anomalies in Brazilian space missions are considered, allowing assessing and comparing the effectiveness of two clustering algorithms (K-means and Expectation Maximization), which showed to be effective in the case study where several telemetry channels tended to deliver outlier values and, in these cases, could support the satellite operators by allowing the anticipation of anomalies. However for silent problems, where there was just a small variation in a single telemetry, the algorithms were not as efficient.
AreaETES > BDMCI > Fonds > Produção anterior à 2021 > DIDSS > Applying Data Mining...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URL
zipped data URL
Target File06214776.pdf
User Groupmarciana
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
LinkingTrabalho Vinculado à Tese/Dissertação
Next Higher Units8JMKD3MGPCW/446B2HE
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress edition editor electronicmailaddress format issn label lineage mark nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarytype type url volume
7. Description control
e-Mail (login)marciana