Ut-battelle, Inc.

Lockheed Martin Energy Research Corporation

Gradient Dynamics LLC

Lee M Hively

Robert K Abercrombie

Vladimir Protopopescu

Donald E Welch

Paul C Gailey

Ray F Holdaway

Esmond G Ng

Scalar-longitudinal waves (SLWs) may be transmitted and/or received. A first apparatus configured to transmit and/or receive SLWs may include a linear first conductor configured to operate as a linear monopole antenna at a first operating frequency. The first apparatus may include a tubular second conductor coaxially aligned with the first conductor and an annular balun configured to cancel most or all return current on an outer surface of the second conductor during operation such that the first conductor transmits or receives SLWs. A second apparatus configured to transmit and/or receive scalar-longitudinal waves may include a bifilar coil formed in an alternating fashion of a first conductor and a second conductor such that an electrical current in the coil will propagate in opposite directions in adjacent turns of the coil thereby cancelling any magnetic field so that during operation the coil transmits or receives SLWs.

Systems, apparatuses, and methods for generating and/or utilizing scalar-longitudinal waves

Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.

Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information

This invention teaches further improvements in methods for forewarning of critical events via phase-space dissimilarity analysis of data from biomedical equipment, mechanical devices, and other physical processes. One improvement involves objective determination of a forewarning threshold (U), together with a failure-onset threshold (U) corresponding to a normalized value of a composite measure (C) of dissimilarity; and providing a visual or audible indication to a human observer of failure forewarning and/or failure onset. Another improvement relates to symbolization of the data according the binary numbers representing the slope between adjacent data points. Another improvement relates to adding measures of dissimilarity based on state-to-state dynamical changes of the system. And still another improvement relates to using a Shannon entropy as the measure of condition change in lieu of a connected or unconnected phase space.

Methods for improved forewarning of condition changes in monitoring physical processes

Sensor modules () including accelerometers () are placed on a physical structure () and tri-axial accelerometer data is converted to mechanical power (P) data () which then processed to provide a forewarning () of a critical event concerning the physical structure (). The forewarning is based on a number of occurrences of a composite measure of dissimilarity (C) exceeding a forewarning threshold over a defined sampling time; and a forewarning signal () is provided to a human observer through a visual, audible or tangible signal. A forewarning of a structural failure can also be provided based on a number of occurrences of (C) above a failure value threshold.

Methods for forewarning of critical condition changes in monitoring civil structures

Brain waves are used as a biometric parameter to provide for authentication and identification of personnel. The brain waves are sampled using EEG equipment and are processed using phase-space distribution functions to compare digital signature data from enrollment of authorized individuals to data taken from a test subject to determine if the data from the test subject matches the signature data to a degree to support positive identification.

Methods for using a biometric parameter in the identification of persons

The invention relates to a method and apparatus for simultaneously processing different sources of test data into informational data and then processing different categories of informational data into knowledge-based data. The knowledge-based data can then be communicated between nodes in a system of multiple computers according to rules for a type of complex, hierarchical computer system modeled on a human brain.

Method and system for knowledge discovery using non-linear statistical analysis and a 1st and 2nd tier computer program

A method and system () for determining vehicle weight to a precision of &#x3c;0.1%, uses a plurality of weight sensing elements (), a computer () for reading in weighing data for a vehicle () and produces a dataset representing the total weight of a vehicle via programming (-) that is executable by the computer () for (a) providing a plurality of mode parameters that characterize each oscillatory mode in the data due to movement of the vehicle during weighing, (b) by determining the oscillatory mode at which there is a minimum error in the weighing data; (c) processing the weighing data to remove that dynamical oscillation from the weighing data; and (d) repeating steps (a)-(c) until the error in the set of weighing data is &#x3c;0.1% in the vehicle weight.

Method and system for reducing errors in vehicle weighing systems

This disclosed invention concerns improvements in forewarning of critical events via phase-space dissimilarity analysis of data from mechanical devices, electrical devices, biomedical data, and other physical processes. First, a single channel of process-indicative data is selected that can be used in place of multiple data channels without sacrificing consistent forewarning of critical events. Second, the method discards data of inadequate quality via statistical analysis of the raw data, because the analysis of poor quality data always yields inferior results. Third, two separate filtering operations are used in sequence to remove both high-frequency and low-frequency artifacts using a zero-phase quadratic filter. Fourth, the method constructs phase-space dissimilarity measures (PSDM) by combining of multi-channel time-serial data into a multi-channel time-delay phase-space reconstruction. Fifth, the method uses a composite measure of dissimilarity (C) to provide a forewarning of failure and an indicator of failure onset.

Methods for improved forewarning of critical events across multiple data channels

This invention teaches further method improvements to forewarn of critical events via phase-space dissimilarity analysis of data from biomedical equipment, mechanical devices, and other physical processes. One improvement involves conversion of time-serial data into equiprobable symbols. A second improvement is a method to maximize the channel-consistent total-true rate of forewarning from a plurality of data channels over multiple data sets from the same patient or process. This total-true rate requires resolution of the forewarning indications into true positives, true negatives, false positives and false negatives. A third improvement is the use of various objective functions, as derived from the phase-space dissimilarity measures, to give the best forewarning indication. A fourth improvement uses various search strategies over the phase-space analysis parameters to maximize said objective functions. A fifth improvement shows the usefulness of the method for various biomedical and machine applications.

Methods for consistent forewarning of critical events across multiple data channels

There is presented a reliable technique for measuring condition change in nonlinear data such as brain waves. The nonlinear data is filtered and discretized into windowed data sets. The system dynamics within each data set is represented by a sequence of connected phase-space points, and for each data set a distribution function is derived. New metrics are introduced that evaluate the distance between distribution functions. The metrics are properly renormalized to provide robust and sensitive relative measures of condition change. As an example, these measures can be used on EEG data, to provide timely discrimination between normal, preseizure, seizure, and post-seizure states in epileptic patients. Apparatus utilizing hardware or software to perform the method and provide an indicative output is also disclosed.

Condition assessment of nonlinear processes

A method and apparatus are provided for the detection, through nonlinear manipulation of data, of an indicator of imminent failure due to crack growth in structural elements. The method is a process of determining energy consumption due to crack growth and correlating the energy consumption with physical phenomena indicative of a failure event. The apparatus includes sensors for sensing physical data factors, processors or the like for computing a relationship between the physical data factors and phenomena indicative of the failure event, and apparatus for providing notification of the characteristics and extent of such phenomena.

Nonlinear structural crack growth monitoring

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated.

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Philadelphia, TN, United States of America

Lee M Hively