Palo Alto Research Center Inc.

United States of America As Represented by the Administrator of Nasa

Other

Bhaskar Saha

Ajay Raghavan

Peter Kiesel

Lars Wilko Sommer

David Eric Schwartz

Alexander Lochbaum

Tobias Staudt

Saroj Kumar Sahu

Anurag Ganguli

Sean Roark Garner

Simon Barber

Julian Schwartz

Ion Matei

Kai F Goebel

Hoe Jin Hah

Geun-Chang Chung

Chang-Jun Bae

Mohamed Alamgir

Bokkyu Choi

Gyu-Ok Hwang

Tomonori Honda

Daniel H Greene

David Mathew Johnson

Shantanu Rane

Johan De Kleer

Daniel Gureasko Bobrow

Jessica Louis Baker Rivest

Daniel Davies

Bernard D Casse

Victor Alfred Beck

Scott Eugene Solberg

Christopher A Paulson

George Daniel

Martin Sheridan

Sylvia Joan Smullin

Tolga Kurtoglu

Clinton J Smith

Linxia Liao

Vedasri Vedharathinam

Kyle Arakaki

William C Janssen

Hoda M A Eldardiry

John Hanley

Jianer Bao

Alexander Feldman

Rui Abreu

Johan Dekleer

Robert Sean McHenry

Stephen Hoover

Abhinav Saxena

Jose R Celaya

The following relates generally to defense mechanisms and security systems. Broadly, systems and methods are disclosed that detect an anomaly in an Embedded Mission Specific Device (EMSD). Disclosed approaches include a meta-material antenna configured to receive a radio frequency signal from the EMSD, and a central reader configured to receive a signal from the meta-material antenna. The central reader may be configured to: build a finite state machine model of the EMSD based on the signal received from the meta-material antenna; and detect if an anomaly exists in the EMSD based on the built finite state machine model.

System and method for remotely detecting an anomaly

A system includes a first optical sensor sensitive to both a parameter of interest, Parameter1, and at least one confounding parameter, Parameter2 and a second optical sensor sensitive only to the confounding parameter. Measurement circuitry measures Min response to light scattered by the first optical sensor, where M=value of Parameter1+K*value of Parameter2. The measurement circuitry also measures Min response to light scattered by the second optical sensor, where M=value of Parameter2. Compensation circuitry determines a compensation factor, K, for the confounding parameter based on measurements of Mand Mtaken over multiple load/unload cycles or over one or more thermal cycles. The compensation factor is used to determine the parameter of interest.

Method and system to separate optically measured coupled parameters

An electrochemical metal alloy identification device employing electrolytes to measure and identify different potentials of alloys is presented. This includes physical structure, disposables, electrical systems, control circuitry, and algorithms to identify alloys.

Alloy identification device

A battery includes a folded bicell battery stack with an embedded fiber optic cable and sensor. A cell casing encloses the bicell stack with at least one fiber optic cable is embedded within the battery. The fiber optic cable includes an internal portion disposed within the cell casing and having at least one optical sensor disposed thereon. An external portion of the fiber optic cable protrudes from the casing. A sealing gasket is disposed at least partially around the fiber optic cable and between the cell sealing edges at a point of entry of the fiber optic cable into the battery.

Embedded fiber optic cables for battery management

Systems and methods for controlling and operating a hybrid vehicle having a high degree of hybridization are disclosed. A power flow control system predicts vehicle power demand to drive the hybrid vehicle based on changing conditions during operation of the hybrid vehicle. The power flow control system controls the power flow so as to provide power to drive the hybrid vehicle based on the predicted vehicle power demand, wherein the predicted vehicle power demand is greater than a maximum.

Control system for hybrid vehicles with high degree of hybridization

A new and/or improved method, apparatus and/or system is disclosed which aids in extending correct behavioral models to include fault modes and in fault mode analysis of components and/or systems in simulated model environments, including, e.g., FMEA and FMECA and diagnostic fault tree generation.

Method for Modelica-based system fault analysis at the design stage

A system detects and/or predicts metal ion plating events of a metal ion energy storage device. The system includes an optical sensor disposed internally within or externally on a metal ion energy storage device wherein the optical sensor has an optical output that changes in response to strain within a metal ion energy storage device. A current sensor senses current through the metal ion energy storage device. Plating detection circuitry measures a wavelength shift in the optical output of the optical sensor and estimates a state of charge (SOC) of the metal ion energy storage device based on the current. An expected wavelength shift is determined from the estimated SOC. A plating event can be detected and/or predicted based on the difference between the expected wavelength shift and the measured wavelength shift.

Detection and/or prediction of plating events in an energy storage device

A method and system for detecting fault in a machine. During operation, the system obtains control signals and corresponding sensor data that indicates a condition of the machine. The system determines consistent time intervals for each of the control signals. During a consistent time interval the standard deviation of a respective control signal is less than a respective predetermined threshold. The system aggregates the consistent time intervals to determine aggregate consistent intervals. The system then maps the aggregate consistent intervals to the sensor data to determine time interval segments for the sensor data. The system may generate features based on the sensor data. Each respective feature is generated from a time interval segment of the sensor data. The system trains a classifier using the features, and applies the classifier to additional sensor data indicating a condition of the machine over a period of time to detect a machine fault.

Device health estimation by combining contextual information with sensor data

Embodiments of the present invention provide a system for estimating a location of a gas leak, based on machine learning from forward gas concentration data provided by an analog or scale model including a gas source. The system improves significantly over previous systems by providing high quality, physically accurate forward modeling data inexpensively. During operation, the system configures an aerosol source at a first location to emit a gaseous aerosol. The system then configures a laser source to illuminate the aerosol with a laser sheet. The system may then obtain an image of a reflection of the laser sheet from the aerosol. The system may then analyze the image to quantify a three-dimensional concentration distribution of the aerosol. The system may then estimate, based on solving an inverse problem and an observed second gas concentration, a second location of a second gas source.

Method and system for analyzing gas leak based on machine learning

One embodiment of the present invention provides an energy-asset control system for utilizing an energy asset to provide one of more modes of operation services. The system includes an economic optimizer configured to identify at least one mode of operation opportunity based on current and/or future market conditions; a prognostics module configured to perform a prognostic analysis associated with the mode of operation opportunity for the energy asset using an existing model, and determine a confidence level associated with the prognostic analysis; and an operation controller. The economic optimizer is further to configured to, in response to the prognostics module determining the confidence level exceeding a predetermined threshold, determine an expected profit of the mode of operation opportunity based on outcomes of the prognostic analysis; and optimize, over a predetermined time period, a usage of the energy asset based on the expected profit of the mode of operation opportunity.

Strategic modeling for economic optimization of grid-tied energy assets

A hybrid vehicle includes at least one axle, an energy storage device disposed within the hybrid vehicle, a fuel consuming engine, a power boosting feature, and a controller. The fuel consuming engine is operably connected to selectively provide power to at least one of the energy storage device and the at least one axle. The engine is capable of providing at least the mean but less than a peak power to drive the hybrid vehicle over a typical route. The power boosting feature is configured to provide the fuel consuming engine with additional power to achieve a desired power to accelerate the hybrid vehicle. The controller is adapted to selectively control power flow to the one or more axles from one or more of the energy storage device, the engine, and the power boosting feature to achieve the desired power.

Hybrid vehicle with power boost

Hybrid vehicle design circuitry quantifies values for utility/disutility variables of a hybrid vehicle design by evaluating a hybrid vehicle model over a collection of drive cycles/routes. The utility/disutility values include at least one of: total time or additional time beyond a reference time needed for the hybrid vehicle design to complete the drive cycles/routes, a fraction or number of the drive cycles/routes for which the hybrid vehicle design fails to achieve a target velocity, and amount of time or distance over which the hybrid vehicle design fails to achieve a target acceleration or the target velocity over the drive cycles/routes. The hybrid vehicle design circuitry calculates one or more specifications of a hybrid vehicle design based on the utility/disutility values.

Systems and methods for hybrid vehicles with a high degree of hybridization

Growing community of inventors

Redwood City, CA, United States of America

Bhaskar Saha