Engineering topics

McKenzie, Frederick

Publications and source records attributed to McKenzie, Frederick.

4 records

NASA NTRS2012

A Systematic Approach for Real-Time Operator Functional State Assessment

A task overload condition often leads to high stress for an operator, causing performance degradation and possibly disastrous consequences. Just as dangerous, with automated flight systems, an operator may experience a task underload condition (during the en-route flight phase, for example), becoming easily bored and finding it difficult to maintain sustained attention. When an unexpected event occurs, either internal or external to the automated system, the disengaged operator may neglect, misunderstand, or respond slowly/inappropriately to the situation. In this paper, we discuss an approach for Operator Functional State (OFS) monitoring in a typical aviation environment. A systematic ground truth finding procedure has been designed based on subjective evaluations, performance measures, and strong physiological indicators. The derived OFS ground truth is continuous in time compared to a very sparse estimation of OFS based on an expert review or subjective evaluations. It can capture the variations of OFS during a mission to better guide through the training process of the OFS assessment model. Furthermore, an OFS assessment model framework based on advanced machine learning techniques was designed and the systematic approach was then verified and validated with experimental data collected in a high fidelity Boeing 737 simulator. Preliminary results show highly accurate engagement/disengagement detection making it suitable for real-time applications to assess pilot engagement.

Zhang, Guangfan↗

NASA NTRS2011

Imbalanced Learning for Functional State Assessment

This paper presents results of several imbalanced learning techniques applied to operator functional state assessment where the data is highly imbalanced, i.e., some function states (majority classes) have much more training samples than other states (minority classes). Conventional machine learning techniques usually tend to classify all data samples into majority classes and perform poorly for minority classes. In this study, we implemented five imbalanced learning techniques, including random undersampling, random over-sampling, synthetic minority over-sampling technique (SMOTE), borderline-SMOTE and adaptive synthetic sampling (ADASYN) to solve this problem. Experimental results on a benchmark driving lest dataset show thai accuracies for minority classes could be improved dramatically with a cost of slight performance degradations for majority classes,

Li, Feng↗

NASA NTRS2011

A Systematic Approach for Engagement Analysis Under Multitasking Environments

An overload condition can lead to high stress for an operator and further cause substantial drops in performance. On the other extreme, in automated systems, an operator may become underloaded; in which case, it is difficult for the operator to maintain sustained attention. When an unexpected event occurs, either internal or external to the automated system, a disengaged operation may neglect, misunderstand, or respond slowly/inappropriately to the situation. In this paper, we discuss a systematic approach monitor for extremes of cognitive workload and engagement in multitasking environments. Inferences of cognitive workload ar engagement are based on subjective evaluations, objective performance measures, physiological signals, and task analysis results. The systematic approach developed In this paper aggregates these types of information collected under the multitasking environment and can provide a real-time assessment or engagement.

Zhang, Guangfan↗

NASA NTRS2011

EEG Artifact Removal Using a Wavelet Neural Network

!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

Nguyen, Hoang-Anh T.↗