Engineering topics

Hahn, S. F.

Publications and source records attributed to Hahn, S. F..

2 records

NASA NTRS1981

Development of a fast ion mass spectrometer for space research

An ion mass spectrometer having a cylindrical E bar x B bar analyzer and either a cylindrical or spherical electrostatic analyzer is described. The instrument has two nested channels and a postacceleration between the electrostatic and E bar x B bar analyzers for wide ranges of energy and mass responses. It is noted that the dual-channel construction not only doubles the data acquisition rate but also permits a reversal of the electrostatic field orientation in the inner channel for lighter ions. The outer channel is then optimized for ions of medium to high mass numbers, resulting in increased mass resolution for such heavy ions. When combined with a cylindrical E bar x B bar analyzer, a 90-deg-deflection angle spherical electrostatic analyzer provides an extremely wide viewing angle. The angular distribution of ions can be derived from such geometry if a position-sensitive resistive anode or a detector array is used at the exit aperture. Details on the principles of operation, the instrument design, and calibration are presented.

Hahn, S. F.↗

NASA NTRS1972

A comparative study of nonparametric methods for pattern recognition

The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal.

Hahn, S. F.↗