Functional reasoning in diagnostic problem solving

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field

Using decision-tree classifier systems to extract knowledge from databases

One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described

CMB Polarization Experiments

We discuss the analysis of polarization experiments with particular emphasis on those that measure the Stokes parameters on a ring on the sky. We discuss the ability of these experiments to separate the $E$ and $B$ contributions to the polarization signal. The experiment being developed at Wisconsin university is studied in detail, it will be sensitive to both Stokes parameters and will concentrate on large scale polarization, scanning a $47^o$ degree ring. We will also consider another example, an experiment that measures one of the Stokes parameters in a $1^o$ ring. We find that the small ring experiment will be able to detect cosmological polarization for some models consistent with the current temperature anisotropy data, for reasonable integration times. In most cosmological models large scale polarization is too small to be detected by the Wisconsin experiment, but because both $Q$ and $U$ are measured, separate constraints can be set on $E$ and $B$ polarization.Comment: 27 pages with 12 included figure

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base

Using output to evaluate and refine rules in rule-based expert systems

The techniques described provide an effective tool which knowledge engineers and domain experts can utilize to help in evaluating and refining rules. These techniques have been used successfully as learning mechanisms in a prototype adaptive diagnostic expert system and are applicable to other types of expert systems. The degree to which they constitute complete evaluation/refinement of an expert system depends on the thoroughness of their use

The Cosmic Microwave Background and Inflation Parameters

We review the currrent cosmic parameter determinations of relevance to inflation using the WMAP-1year, Boomerang, CBI, Acbar and other CMB data. The basic steps in the pipelines which determine the bandpowers from the raw data from which these estimations are made are summarized. We forecast how the precision is likely to improve with more years of WMAP in combination with future ground-based experiments and with Planck. We address whether the current data indicates strong breaking from uniform acceleration through the relatively small region of the inflaton potential that the CMB probes, manifest in the much-discussed running spectral index or in even more radical braking/breaking scenarios. Although some weak ``anomalies'' appear in the current data, the statistical case is not there. However increased precision, at the high multipole end and with polarization measurements, will significantly curtail current freedom.Comment: 24 pages, 10 figures, 2 tables, Int. J. Theor. Phys. 2004, ed. E. Verdaguer, "Peyresq Physics 8", "The Early Universe: Confronting theory with observations" (June 21-27, 2003

Solidification processing of alloys using an applied electric field

A method is provided for obtaining an alloy having an ordered microstructure which comprises the steps of heating the central portion of the alloy under uniform temperature so that it enters a liquid phase while the outer portions remain solid, applying a constant electric current through the alloy during the heating step, and solidifying the liquid central portion of the alloy by subjecting it to a temperature-gradient zone so that cooling occurs in a directional manner and at a given rate of speed while maintaining the application of the constant electric current through the alloy. The method of the present invention produces an alloy having superior characteristics such as reduced segregation. After subsequent precipitation by heat-treatment, the alloys produced by the present invention will have excellent strength and high-temperature resistance