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

Observation of the dielectric-waveguide mode of light propagation in p-n junctions

Theoretical considerations of the propagation of electromagnetic energy near a p-n junction (1) show that the “sandwich” formed by having a depletion layer bounded by the p and n regions can act as a dielectric waveguide. (1,2

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

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

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

Cosmological predictions from the Misner brane

Within the spirit of five-dimensional gravity in the Randall-Sundrum scenario, in this paper we consider cosmological and gravitational implications induced by forcing the spacetime metric to satisfy a Misner-like symmetry. We first show that in the resulting Misner-brane framework the Friedmann metric for a radiation dominated flat universe and the Schwarzschild or anti-de Sitter black holes metrics are exact solutions on the branes, but the model cannot accommodate any inflationary solution. The horizon and flatness problems can however be solved in Misner-brane cosmology by causal and noncausal communications through the extra dimension between distant regions which are outside the horizon. Based on a semiclassical approximation to the path-integral approach, we have calculated the quantum state of the Misner-brane universe and the quantum perturbations induced on its metric by brane propagation along the fifth direction. We have then considered testable predictions from our model. These include a scale-invariant spectrum of density perturbations whose amplitude can be naturally accommodated to the required value 10$^{-5}-10^{-6}$, and a power spectrum of CMB anisotropies whose acoustic peaks are at the same sky angles as those predicted by inflationary models, but having much smaller secondary-peak intensities. These predictions seem to be compatible with COBE and recent Boomerang and Maxima measurementsComment: 16 pages, RevTe

Power Spectrum Estimators For Large CMB Datasets

Forthcoming high-resolution observations of the Cosmic Microwave Background (CMB) radiation will generate datasets many orders of magnitude larger than have been obtained to date. The size and complexity of such datasets presents a very serious challenge to analysing them with existing or anticipated computers. Here we present an investigation of the currently favored algorithm for obtaining the power spectrum from a sky-temperature map --- the quadratic estimator. We show that, whilst improving on direct evaluation of the likelihood function, current implementations still inherently scale as the equivalent of the cube of the number of pixels or worse, and demonstrate the critical importance of choosing the right implementation for a particular dataset.Comment: 8 pages LATEX, no figures, corrected misaligned columns in table