Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Ceillier, Tugdual"'
Autor:
BOURCIER, Jules, Floquet, Thomas, Dashyan, Gohar, Ceillier, Tugdual, Alahari, Karteek, Chanussot, Jocelyn
Publikováno v:
Conference on Artificial Intelligence for Defense (CAID) 2022, DGA Ma\^itrise de l'Information, Nov 2022, Rennes, France
In defense-related remote sensing applications, such as vehicle detection on satellite imagery, supervised learning requires a huge number of labeled examples to reach operational performances. Such data are challenging to obtain as it requires milit
Externí odkaz:
http://arxiv.org/abs/2210.11815
Ensembling is a method that aims to maximize the detection performance by fusing individual detectors. While rarely mentioned in deep-learning articles applied to remote sensing, ensembling methods have been widely used to achieve high scores in rece
Externí odkaz:
http://arxiv.org/abs/2202.10554
Publikováno v:
Conference on Artificial Intelligence for Defense, Nov 2021, Rennes, France
Satellite imagery is now widely used in the defense sector for monitoring locations of interest. Although the increasing amount of data enables pattern identification and therefore prediction, carrying this task manually is hardly feasible. We hereby
Externí odkaz:
http://arxiv.org/abs/2202.04891
Autor:
Imbert, Julie, Dashyan, Gohar, Goupilleau, Alex, Ceillier, Tugdual, Corbineau, Marie-Caroline
Publikováno v:
International Symposium on Geoscience and Remote Sensing (IGARSS), Jul 2021, Brussels, Belgium
The earth observation industry provides satellite imagery with high spatial resolution and short revisit time. To allow efficient operational employment of these images, automating certain tasks has become necessary. In the defense domain, aircraft d
Externí odkaz:
http://arxiv.org/abs/2202.04890
Deep learning has become in recent years a cornerstone tool fueling key innovations in the industry, such as autonomous driving. To attain good performances, the neural network architecture used for a given application must be chosen with care. These
Externí odkaz:
http://arxiv.org/abs/2109.08028
Publikováno v:
Conference on Artificial Intelligence for Defense, Dec 2020, Rennes, France
In machine learning, the term active learning regroups techniques that aim at selecting the most useful data to label from a large pool of unlabelled examples. While supervised deep learning techniques have shown to be increasingly efficient on many
Externí odkaz:
http://arxiv.org/abs/2101.02480
Publikováno v:
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2020, Waikoloa, Hawaii, United States
Detecting and identifying objects in satellite images is a very challenging task: objects of interest are often very small and features can be difficult to recognize even using very high resolution imagery. For most applications, this translates into
Externí odkaz:
http://arxiv.org/abs/2005.13215
Autor:
van Saders, Jennifer L., Ceillier, Tugdual, Metcalfe, Travis S., Aguirre, Victor Silva, Pinsonneault, Marc H., García, Rafael A., Mathur, Savita, Davies, Guy R.
A knowledge of stellar ages is crucial for our understanding of many astrophysical phenomena, and yet ages can be difficult to determine. As they become older, stars lose mass and angular momentum, resulting in an observed slowdown in surface rotatio
Externí odkaz:
http://arxiv.org/abs/1601.02631
Autor:
Tayar, Jamie, Ceillier, Tugdual, García-Hernández, D. A., Troup, Nicholas W., Mathur, Savita, García, Rafael A., Zamora, O., Johnson, Jennifer A., Pinsonneault, Marc H., Mészáros, Szabolcs, Prieto, Carlos Allende, Chaplin, William J., Elsworth, Yvonne, Nidever, David L., Salabert, David, Schneider, Donald P., Serenelli, Aldo, Shetrone, Matthew, Stello, Dennis
We investigate the occurrence rate of rapidly rotating ($v\sin i$$>$10 km s$^{-1}$), low-mass giant stars in the APOGEE-Kepler (APOKASC) fields with asteroseismic mass and surface gravity measurements. Such stars are likely merger products and their
Externí odkaz:
http://arxiv.org/abs/1505.03536
Autor:
Martig, Marie, Rix, Hans-Walter, Aguirre, Victor Silva, Hekker, Saskia, Mosser, Benoit, Elsworth, Yvonne, Bovy, Jo, Stello, Dennis, Anders, Friedrich, García, Rafael A., Tayar, Jamie, Rodrigues, Thaíse S., Basu, Sarbani, Carrera, Ricardo, Ceillier, Tugdual, Chaplin, William J., Chiappini, Cristina, Frinchaboy, Peter M., García-Hernández, D. A., Hearty, Fred R., Holtzman, Jon, Johnson, Jennifer A., Majewski, Steven R., Mathur, Savita, Mészáros, Szabolcs, Miglio, Andrea, Nidever, David, Pan, Kaike, Pinsonneault, Marc, Schiavon, Ricardo P., Schneider, Donald P., Serenelli, Aldo, Shetrone, Matthew, Zamora, Olga
We derive age constraints for 1639 red giants in the APOKASC sample for which seismic parameters from Kepler, as well as effective temperatures, metallicities and [alpha/Fe] values from APOGEE DR12 are available. We investigate the relation between a
Externí odkaz:
http://arxiv.org/abs/1412.3453