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Articles 1 - 3 of 3
Full-Text Articles in Engineering
Visualization And Machine Learning Techniques For Nasa’S Em-1 Big Data Problem, Antonio P. Garza Iii, Jose Quinonez, Misael Santana, Nibhrat Lohia
Visualization And Machine Learning Techniques For Nasa’S Em-1 Big Data Problem, Antonio P. Garza Iii, Jose Quinonez, Misael Santana, Nibhrat Lohia
SMU Data Science Review
In this paper, we help NASA solve three Exploration Mission-1 (EM-1) challenges: data storage, computation time, and visualization of complex data. NASA is studying one year of trajectory data to determine available launch opportunities (about 90TBs of data). We improve data storage by introducing a cloud-based solution that provides elasticity and server upgrades. This migration will save $120k in infrastructure costs every four years, and potentially avoid schedule slips. Additionally, it increases computational efficiency by 125%. We further enhance computation via machine learning techniques that use the classic orbital elements to predict valid trajectories. Our machine learning model decreases trajectory …
Development Of A Statistical Model To Predict Australian Flight Students’ Valuation Of Aviation Safety, Michael Chiu, Nickolai Isaksen, Steven Leib
Development Of A Statistical Model To Predict Australian Flight Students’ Valuation Of Aviation Safety, Michael Chiu, Nickolai Isaksen, Steven Leib
Journal of Aviation/Aerospace Education & Research
This study was a quantitative approach to explore whether certain demographic factors, exposure to safety training, flight experience, and engagement could be used to develop a predictive model for how Australian flight students and early career pilots valued safety. Participants were given an online Likert-scale survey to determine their valuation of safety based on SMS safety sub-cultures, safety training, engagement, as well as provided basic demographic metrics including age, flight experience, gender. In addition, a second group of participants representing local Australian culture were given a survey to determine their safety valuation. Linear regression was used to develop the best …
Automated Elimination Of Eog Artifacts In Sleep Eeg Using Regression Method, Mehmet Dursun, Seral Özşen, Sali̇h Güneş, Bayram Akdemi̇r, Şebnem Yosunkaya
Automated Elimination Of Eog Artifacts In Sleep Eeg Using Regression Method, Mehmet Dursun, Seral Özşen, Sali̇h Güneş, Bayram Akdemi̇r, Şebnem Yosunkaya
Turkish Journal of Electrical Engineering and Computer Sciences
Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine …