Autonomous Adaptive Low-Power Instrument Platform (AAL-PIP) for remote high latitude geospace data collection
C. R. Clauer(Virginia Tech), A. J. Ridley(University of Michigan), Todd E. Humphreys(The University of Texas at Austin), Zhonghua Xu(National Institute of Aerospace), H. Kim(Virginia Tech), S. Musko(University of Michigan), K. Deshpande(Virginia Tech), D. R. Weimer(Virginia Tech), G. Crowley(Southwest Research Institute), Jahshan A. Bhatti(The University of Texas at Austin), Randall Nealy(Virginia Tech), Chad Fish(Utah State University)
Cited by 1
Related Papers
Machine learning in manufacturing: advantages, challenges, and applications
|Production & Manufacturing Research|2016|1.3k
Improved ionospheric electrodynamic models and application to calculating Joule heating rates
|Journal of Geophysical Research Atmospheres|2005|746
Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection
|CIRP Annals|2016|571
Models of high‐latitude electric potentials derived with a least error fit of spherical harmonic coefficients
|Journal of Geophysical Research Atmospheres|1995|481
An improved model of ionospheric electric potentials including substorm perturbations and application to the Geospace Environment Modeling November 24, 1996, event
|Journal of Geophysical Research Atmospheres|2001|375