Introduction to Library Trends 42 (3) Winter 1994: Library Finance: New Needs, New Models

published or submitted for publicatio

High temperature coatings for gas bearings

Aluminum oxide and nickel-chrome bonded chrome carbide coatings enhance the performance of gas bearings at temperatures up to 1400 degrees F. A plasma-sprayed aluminum-oxide coating is applied to the bearing surface and a plasma- sprayed 25 percent nickel-chrome bonded chrome carbide coating is applied to the journal surface

A new design tool for feature extraction in noisy images based on grayscale hit-or-miss transforms

The Hit-or-Miss transform (HMT) is a well known morphological transform capable of identifying features in digital images. When image features contain noise, texture or some other distortion, the HMT may fail. Various researchers have extended the HMT in different ways to make it more robust to noise. The most successful, and most recent extensions of the HMT for noise robustness, use rank order operators in place of standard morphological erosions and dilations. A major issue with the proposed methods is that no technique is provided for calculating the parameters that are introduced to generalize the HMT, and, in most cases, these parameters are determined empirically. We present here, a new conceptual interpretation of the HMT which uses a percentage occupancy (PO) function to implement the erosion and dilation operators in a single pass of the image. Further, we present a novel design tool, derived from this PO function that can be used to determine the only parameter for our routine and for other generalizations of the HMT proposed in the literature. We demonstrate the power of our technique using a set of very noisy images and draw a comparison between our method and the most recent extensions of the HMT

Combining case based reasoning with neural networks

This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others

Some effects of digital sampling on orbiter flight control system operation

An entry dynamic stability ground test of the OV102 Space Shuttle Orbiter revealed some small amplitude oscillatory output of the flight control system which could have constrained flight of the STS-1 mission. These limit-cycle-type outputs were attributed to a combination of rigid body motion of the Orbiter on its landing gear and some interesting effects of its digital flight control system. These effects included frequency aliasing and phenomena associated with digital quantitization of low amplitude sensor signals. The digital effects suggest significant improvements possible in future designs