Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Bergstedt, K., Ji, H. (2024) A Novel Method to Train Classification Models for Structure Detection in In Situ Spacecraft Data. Earth and Space Science, 11 (6) doi:10.1029/2023ea002965

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleA Novel Method to Train Classification Models for Structure Detection in In Situ Spacecraft Data
JournalEarth and Space Science
AuthorsBergstedt, K.Author
Ji, H.Author
Year2024 (June)Volume11
Issue6
PublisherAmerican Geophysical Union (AGU)
DOIdoi:10.1029/2023ea002965Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID17432922Long-form Identifiermindat:1:5:17432922:2
GUID0
Full ReferenceBergstedt, K., Ji, H. (2024) A Novel Method to Train Classification Models for Structure Detection in In Situ Spacecraft Data. Earth and Space Science, 11 (6) doi:10.1029/2023ea002965
Plain TextBergstedt, K., Ji, H. (2024) A Novel Method to Train Classification Models for Structure Detection in In Situ Spacecraft Data. Earth and Space Science, 11 (6) doi:10.1029/2023ea002965
In(2024, June) Earth and Space Science Vol. 11 (6) American Geophysical Union (AGU)

See Also

These are possibly similar items as determined by title/reference text matching only.

Finley, Matthew G., Martinez‐Ledesma, Miguel, Paterson, William R., Argall, Matthew R., Miles, David M., Dorelli, John C., Zesta, Eftyhia (2024) Generalized Time‐Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and Data Prioritization Using Principal Components Analysis and Unsupervised Clustering. Earth and Space Science, 11 (9). doi:10.1029/2024ea003753
Yang, Jing, Guo, Lianghui, Tang, Hanhan, Yao, Shuo (2024) An Omnidirectional Filtering Method for Destriping Lunar Satellite Gravity Anomalies Data. Earth and Space Science, 11 (6) doi:10.1029/2023ea003465
Maebius, Sarah, Bennett, K. E., Schwenk, J. (2024) Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin. Earth and Space Science, 11 (12). doi:10.1029/2024ea003798
Broeren, T., Klein, K. G., TenBarge, J. M. (2024) Multi‐Spacecraft Magnetic Field Reconstructions: A Cross‐Scale Comparison of Methods. Earth and Space Science, 11 (3) doi:10.1029/2023ea003369
Gaddes, M., Hooper, A., Albino, F. (2024) Simultaneous Classification and Location of Volcanic Deformation in SAR Interferograms Using a Convolutional Neural Network. Earth and Space Science, 11 (6). doi:10.1029/2024ea003679
Ruan, Renhao, Yang, Wei, Hao, Jialong, Guo, Guangjun (2024) Oven Design for In‐Situ Thermal Extraction of Volatiles From Lunar Regolith. Earth and Space Science, 11 (6) doi:10.1029/2024ea003556
Corrales, M., Hoteit, H., Ravasi, M. (2024) Seis2Rock: A Data‐Driven Approach to Direct Petrophysical Inversion of Pre‐Stack Seismic Data. Earth and Space Science, 11 (2) doi:10.1029/2023ea003301
Mimura, K., Nakamura, K., Yasukawa, K., Sibert, E. C., Ohta, J., Kitazawa, T., Kato, Y. (2024) Applicability of Object Detection to Microfossil Research: Implications From Deep Learning Models to Detect Microfossil Fish Teeth and Denticles Using YOLO‐v7. Earth and Space Science, 11 (1) doi:10.1029/2023ea003122
Üstek, İrem, Arana‐Catania, Miguel, Farr, Alexander, Petrunin, Ivan (2024) Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction. Earth and Space Science, 11 (11). doi:10.1029/2024ea003997
Leroy, S. S., McVey, A. E., Leidner, S. M., Zhang, H., Gleisner, H. (2024) GNSS Radio Occultation Data in the AWS Cloud. Earth and Space Science, 11 (2) doi:10.1029/2023ea003021
Xie, Xu, Hao, Xu, Ji, Longfei (2024) Improvement of Source Spectrum Fall‐Off for Simulating Ground Motion Using Stochastic Green’s Function Method. Earth and Space Science, 11 (10). doi:10.1029/2024ea003597
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Š OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 22, 2025 00:43:29
Go to top of page