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Terramechanics And Machine Learning For The Characterization Of Terrain, Bryan W. Southwell
Terramechanics And Machine Learning For The Characterization Of Terrain, Bryan W. Southwell
Electronic Thesis and Dissertation Repository
An instrumented rover wheel can collect vast amounts of data about a planetary surface. Planetary surfaces are changed by complex geological processes which can be better understood with an abundance of surface data and the use of terramechanics. Identifying terrain parameters such as cohesion and angle of friction hold importance for both the rover driver and the planetary scientist. Knowledge of terrain characteristics can warn of unsafe terrain and flag potential interesting scientific sites. The instrumented wheel in this research utilizes a pressure pad to sense load and sinkage, a string potentiometer to measure slip, and records motor current draw. …
Machine Learning Classification Of Interplanetary Coronal Mass Ejections Using Satellite Accelerometers, Kelsey Doerksen
Machine Learning Classification Of Interplanetary Coronal Mass Ejections Using Satellite Accelerometers, Kelsey Doerksen
Electronic Thesis and Dissertation Repository
Space weather phenomena is a complex area of research as there are many different variables and signatures that are used to identify the occurrence of solar storms and Interplanetary Coronal Mass Ejections (ICMEs), with inconsistencies between databases and solar storm catalogues. The identification of space weather events is important from a satellite operation point of view, as strong geomagnetic storms can cause orbit perturbations to satellites in low-earth orbit. The Disturbance storm time (Dst) and the Planetary K-index (Kp) are common indices used to identify the occurrence of geomagnetic storms caused by ICMEs, among several other signatures that are not …