Andere Kunden interessierten sich auch für
![Pilot Mental Workload Calibration]( "Pilot Mental Workload Calibration")
Pilot Mental Workload Calibration61,99 €![Using Statistical Process Control Methods to Classify Pilot Mental Workload]( "Using Statistical Process Control Methods to Classify Pilot Mental Workload")
Using Statistical Process Control Methods to Classify Pilot Mental Workload60,99 €![Development of a Pilot Candidate Selection Model Using Multivariate Techniques]( "Development of a Pilot Candidate Selection Model Using Multivariate Techniques")
Development of a Pilot Candidate Selection Model Using Multivariate Techniques61,99 €![Use of Multivariate Techniques to Validate and Improve the Current USAF Pilot Candidate Selection Model]( "Use of Multivariate Techniques to Validate and Improve the Current USAF Pilot Candidate Selection Model")
Use of Multivariate Techniques to Validate and Improve the Current USAF Pilot Candidate Selection Model61,99 €![USAF Pilot Perceptions of Workload Assessment in a Combat or High-Threat Environment]( "USAF Pilot Perceptions of Workload Assessment in a Combat or High-Threat Environment")
USAF Pilot Perceptions of Workload Assessment in a Combat or High-Threat Environment60,99 €![An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks]( "An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks")
An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks60,99 €![Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks]( "Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks")
Selection of Psychophysiological Features Across Subjects for Classifying Workload Using Artificial Neural Networks60,99 €
As advances in technology are made, the cockpits of the aircraft in the Air Force inventory have become increasingly complex. Consequently, mental demands on the pilot have risen. In a worst case scenario, the pilots have been so saturated with inputs they have actually forgotten to carry out the fundamentals of flying, such as G-straining maneuvers, resulting in several fatalities. Recent research in this area has involved collecting psychophysioloical features, such as electroencephalography (EEG), heart, eye and respiration measures, in an attempt to identify pilot mental workload. This thesis focuses on feature selection and reduction of the psychopnysiological features and subsequent classification of pilot mental workload on multiple subjects over multiple days. A stepwise statistical technique and the signal-to-noise (SNR) saliency metric were used to reduce the number of features required for classification. Factor analysis was used to compare the variables chosen by the discriminant procedure and the SNR saliency metric as applied to a neural network.