371 research outputs found
Quantifying the Classification of Exoplanets: in Search for the Right Habitability Metric
What is habitability? Can we quantify it? What do we mean under the term
habitable or potentially habitable planet? With estimates of the number of
planets in our Galaxy alone running into billions, possibly a number greater
than the number of stars, it is high time to start characterizing them, sorting
them into classes/types just like stars, to better understand their formation
paths, their properties and, ultimately, their ability to beget or sustain
life. After all, we do have life thriving on one of these billions of planets,
why not on others? Which planets are better suited for life and which ones are
definitely not worth spending expensive telescope time on? We need to find sort
of quick assessment score, a metric, using which we can make a list of
promising planets and dedicate our efforts to them. Exoplanetary habitability
is a transdisciplinary subject integrating astrophysics, astrobiology,
planetary science, even terrestrial environmental sciences. We review the
existing metrics of habitability and the new classification schemes of
extrasolar planets and provide an exposition of the use of computational
intelligence techniques to evaluate habitability scores and to automate the
process of classification of exoplanets. We examine how solving convex
optimization techniques, as in computing new metrics such as CDHS and CEESA,
cross-validates ML-based classification of exoplanets. Despite the recent
criticism of exoplanetary habitability ranking, this field has to continue and
evolve to use all available machinery of astroinformatics, artificial
intelligence and machine learning. It might actually develop into a sort of
same scale as stellar types in astronomy, to be used as a quick tool of
screening exoplanets in important characteristics in search for potentially
habitable planets for detailed follow-up targets.Comment: 17 pages, 6 figures, in pres
JSC director's discretionary fund program
The Johnson Space Center Director's Discretionary Fund Program Annual Report provides a brief review of the status of projects undertaken during the 1990 fiscal year. Three space exploration initiative related issues were focused on: regenerative life support, human spacecraft design, and lunar surface habitat. A viable program of life sciences, space sciences, and engineering research has been maintained
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 130, July 1974
This special bibliography lists 291 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1974
Domain adaptation based transfer learning approach for solving PDEs on complex geometries
In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems. © 2022, The Author(s)
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 335)
This bibliography lists 143 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during March, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance
Architectural/Environmental Handbook for Extraterrestrial Design
Handbook on environmental and space utilization criteria for design of extraterrestrial manned spacecraft and shelter
Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning
In this paper we present two examples of recent investigations that we have
undertaken, applying Machine Learning (ML) neural networks (NN) to image
datasets from outer planet missions to achieve feature recognition. Our first
investigation was to recognize ice blocks (also known as rafts, plates,
polygons) in the chaos regions of fractured ice on Europa. We used a transfer
learning approach, adding and training new layers to an industry-standard Mask
R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks
in a training dataset. Subsequently, the updated model was tested against a new
dataset, achieving 68% precision. In a different application, we applied the
Mask R-CNN to recognize clouds on Titan, again through updated training
followed by testing against new data, with a precision of 95% over 369 images.
We evaluate the relative successes of our techniques and suggest how training
and recognition could be further improved. The new approaches we have used for
planetary datasets can further be applied to similar recognition tasks on other
planets, including Earth. For imagery of outer planets in particular, the
technique holds the possibility of greatly reducing the volume of returned
data, via onboard identification of the most interesting image subsets, or by
returning only differential data (images where changes have occurred) greatly
enhancing the information content of the final data stream
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