Engineering topics

Starks, S. A.

Publications and source records attributed to Starks, S. A..

3 records

NASA NTRS1987

NASA Systems Autonomy Demonstration Program - A step toward Space Station automation

This paper addresses a multiyear NASA program, the Systems Autonomy Demonstration Program (SADP), whose main objectives include the development, integration, and demonstration of automation technology in Space Station flight and ground support systems. The role of automation in the Space Station is reviewed, and the main players in SADP and their roles are described. The core research and technology being promoted by SADP are discussed, and a planned 1988 milestone demonstration of the automated monitoring, operation, and control of a complete mission operations subsystem is addressed.

Starks, S. A.↗

NASA NTRS1976

Classification improvement by optimal dimensionality reduction when training sets are of small size

A computer simulation was performed to test the conjecture that, when the sizes of the training sets are small, classification in a subspace of the original data space may give rise to a smaller probability of error than the classification in the data space itself; this is because the gain in the accuracy of estimation of the likelihood functions used in classification in the lower dimensional space (subspace) offsets the loss of information associated with dimensionality reduction (feature extraction). A number of pseudo-random training and data vectors were generated from two four-dimensional Gaussian classes. A special algorithm was used to create an optimal one-dimensional feature space on which to project the data. When the sizes of the training sets are small, classification of the data in the optimal one-dimensional space is found to yield lower error rates than the one in the original four-dimensional space.

Starks, S. A.↗

NASA NTRS1975

An algorithm for optimal single linear feature extraction from several Gaussian pattern classes

A computational algorithm is presented for the extraction of an optimal single linear feature from several Gaussian pattern classes. The algorithm minimizes the increase in the probability of misclassification in the transformed (feature) space. Numerical results on the application of this procedure to the remotely sensed data from the Purdue Cl flight line as well as LANDSAT data are presented. It was found that classification using the optimal single linear feature yielded a value for the probability of misclassification on the order of 30% less than that obtained by using the best single untransformed feature. Also, the optimal single linear feature gave performance results comparable to those obtained by using the two features which maximized the average divergence.

Starks, S. A.↗