Machine learning techniques for fault isolation and sensor placement

Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance

Perceptual telerobotics

A sensory world modeling system, congruent with a human expert's perception, is proposed. The Experiential Knowledge Base (EKB) system can provide a highly intelligible communication interface for telemonitoring and telecontrol of a real time robotic system operating in space. Paradigmatic acquisition of empirical perceptual knowledge, and real time experiential pattern recognition and knowledge integration are reviewed. The cellular architecture and operation of the EKB system are also examined

Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

Compensating mode conversion due to bend discontinuities through intentional trace asymmetry

In this letter, a comparative analysis is carried out between the mechanism of mode conversion in differential microstrip lines due to bend discontinuities on one side and trace asymmetry on the other side. With the help of equivalent modal circuits, a theoretical basis is provided for the idea to compensate the undesired common mode (CM), due to the presence of the bend, by intentionally designing asymmetric traces. As an application example, the proposed CM-reduction strategy is used in conjunction with another recently-presented wideband CM suppression filter for differential microstrip lines. It is shown that the proposed solution enhances the overall CM-reduction performance of the filter by some decibels, while preserving its transmission properties

MKID development for SuperSpec: an on-chip, mm-wave, filter-bank spectrometer

SuperSpec is an ultra-compact spectrometer-on-a-chip for millimeter and submillimeter wavelength astronomy. Its very small size, wide spectral bandwidth, and highly multiplexed readout will enable construction of powerful multibeam spectrometers for high-redshift observations. The spectrometer consists of a horn-coupled microstrip feedline, a bank of narrow-band superconducting resonator filters that provide spectral selectivity, and Kinetic Inductance Detectors (KIDs) that detect the power admitted by each filter resonator. The design is realized using thin-film lithographic structures on a silicon wafer. The mm-wave microstrip feedline and spectral filters of the first prototype are designed to operate in the band from 195-310 GHz and are fabricated from niobium with at Tc of 9.2K. The KIDs are designed to operate at hundreds of MHz and are fabricated from titanium nitride with a Tc of 2K. Radiation incident on the horn travels along the mm-wave microstrip, passes through the frequency-selective filter, and is finally absorbed by the corresponding KID where it causes a measurable shift in the resonant frequency. In this proceedings, we present the design of the KIDs employed in SuperSpec and the results of initial laboratory testing of a prototype device. We will also briefly describe the ongoing development of a demonstration instrument that will consist of two 500-channel, R=700 spectrometers, one operating in the 1-mm atmospheric window and the other covering the 650 and 850 micron bands.Comment: As submitted, except that "in prep" references have been update

Identifying Mislabeled Training Data

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data

Combining numeric and symbolic learning techniques

Incremental learning from examples in a noisy domain is a difficult problem in Machine Learning. In this paper we divide the task into two subproblems and present a combination of numeric and symbolic approaches that yields robust learning of boolean characterizations. Our method has been implemented in a computer program, and we plot its empirical learning performance in the presence of varying amounts of noise