Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predictmore »
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Geoscientific Model Development
- Page Range or eLocation-ID:
- 4115 to 4131
- Sponsoring Org:
- National Science Foundation
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Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application
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Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds
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On average, the normalized moment prediction root mean square error was reduced by 14.58 % (
) and 36.79 % ( $$p=0.012$$ ) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. $$p<0.001$$ Conclusions
The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.
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