Thursday, October 10, 2019

SwRI, International Team Use Deep Learning to Create Virtual 'Super Instrument'

Results from the model after it has been rearranged to produce spatial results. Global (i.e., image-sized 1 × 1) features are due to linear model applied to standard deviation features; low-frequency (i.e., blocky 15 × 15) features are due to the CNN, which has low spatial resolution; high-frequency details are due to the linear model on average AIA features (i.e., 256 × 256). The linear model has learned a largely correct model mapping AIA to MEGS-A, which is corrected, especially during flaring events, by the CNN.

A study co-written by a Southwest Research Institute scientist describes a new algorithm that combines the capabilities of two spacecraft instruments, which could result in lower cost and higher efficiency space missions. The virtual “super instrument,” is a computer algorithm that utilizes deep learning to analyze ultraviolet images of the Sun, taken by NASA’s Solar Dynamics Observatory, and measure the energy that the Sun emits as ultraviolet light.

“Deep learning is an emerging capability that is revolutionizing the way we interact with data,” said Dr. Andrés Muñoz-Jaramillo, senior research scientist at SwRI. Muñoz-Jaramillo co-authored the study, published this month in Science Advances, alongside collaborators from nine other institutions as part of NASA’s Frontier Development Laboratory. The laboratory is an applied artificial intelligence research accelerator that applies deep learning and machine learning techniques to challenges in space science and exploration.

Deep learning is a type of machine learning that mimics the way the human brain processes information. The result of deep learning is machines accomplishing things that previously required human intelligence, such as translation between foreign languages, driving a vehicle and facial recognition. Things like Netflix suggesting what to watch next, an iPhone unlocking upon sight of its owner’s face and Alexa responding to a vocal request are all results of deep learning.

“All missions beyond Earth have a host of instruments that have been designed with specific capabilities to answer specific scientific questions,” Muñoz-Jaramillo said. “When we combine them into virtual super instruments, we can produce more cost-effective missions with higher scientific impact or use measurements by one instrument to help answer the science questions of another.”

Muñoz-Jaramillo stresses in the study that these virtual super instruments will not make hardware obsolete. They will always require a spacecraft to collect the necessary data for virtualization.

“Deep learning instruments cannot make something out of nothing, but they can significantly enhance the capabilities of existing technology,” he said.

Their virtual super instrument is already in use as part of a Frontier Development Laboratory project for forecasting ionospheric disturbances. Muñoz-Jaramillo is currently working on additional super instruments that combine other capabilities.

“In essence, deep learning involves sophisticated transformation of data,” he said. “We can make these transformations into scientifically useful data and modernize the way we view not just the Sun, but a great number of scientific questions.”

Source: swri.org

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