Abstract: A method, apparatus, system for managing a maintenance for a component in a vehicle. Sensor data is received for the vehicle. Target parameters are predicted using behavior machine learning models trained using first training data to predict the target parameters for a normal behavior of the component operating in a tolerance, wherein the target parameters characterize behavior of the component. Prediction metrics are determined from predicted values for the target parameters predicted by behavior machine learning models and actual values for the target parameters. Whether the component will fall out of the tolerance after a time period is predicted using the prediction metrics and a maintenance machine learning model trained using second training data to predict whether the maintenance is needed for the component, wherein the second training data comprises historical prediction metrics determined for the target parameters.

Abstract: A computing device predicts whether a given component currently installed on a vehicle should be removed from the vehicle, and if so, identifies the location of the given component on the vehicle. The prediction is based on a prediction model that is generated by the computing device using maintenance data associated with the maintenance of components currently installed on, and/or previously removed from, the vehicle, as well as removal data associated with the previous removal of the same type of components from the vehicle. Information associated with the prediction is then output graphically to a display so that an operator can effect the removal of the component.

Abstract: A phased approach to predicting whether a component currently installed on a vehicle should be removed is provided. In a first phase, a prediction model is created using historical data and sample values that are associated with a vehicle component previously removed from the vehicle. In a second phase, the output of the prediction model is used to predict whether the selected vehicle component currently installed on the vehicle should be removed. Based on the prediction, a graphical user interface (GUI) is generated for output to a user. The GUI visually indicates the vehicle component predicted to be removed and provides a recommendation for addressing the predicted removal of the component.

Abstract: A capacitor structure and a forming method thereof are provided. The capacitor structure includes a substrate and a bottom electrode composite layer on the substrate. The bottom electrode composite layer includes a first electrode layer and a second electrode layer on the first electrode layer. An oxidation rate of a material of the second electrode layer is lower than an oxidation rate of a material of the first electrode layer. The capacitor structure also includes a dielectric structure layer on the bottom electrode composite layer.

Abstract: Aspects of the present disclosure provide a method and apparatus for predicting noncompliance conditions of rotating machinery in aerial vehicles. Embodiments include receiving records related to historical noncompliance conditions of a plurality of rotating machines. The records include deceleration time values related to the plurality of rotating machines. Embodiments include generating, based at least on the deceleration time values and the historical noncompliance conditions, a predictive model. Embodiments include receiving sensor data related to a rotating machine of an aerial vehicle. Embodiments include determining, based on the sensor data, a series of deceleration time values for the rotating machine. Embodiments include using the predictive model to determine whether a noncompliance condition is predicted to occur for the rotating machine based on the series of deceleration time values.

Abstract: A method for performing an operation on a flight sensor parameter includes receiving instructions indicating a description of a flight sensor parameter and an operation to be performed on the flight sensor parameter. The method includes generating executable code based on the instructions indicating the description and the operation. The method includes, after generating the executable code, associating the executable code with flight sensor data from one or more sensors. The flight sensor parameter includes a variable of the flight sensor data. The method further includes executing the executable code at a processor using the flight sensor data as an input to generate an output that indicates results of the operation.

Abstract: Disclosed herein is a method that comprises detecting that a rate of decrease in fuel quantity for an aircraft exceeds a previously-determined baseline rate of decrease in fuel quantity for the aircraft by a threshold rate during a corresponding segment of flight of the aircraft. The method also comprises determining that a fuel jettison setting for the aircraft is enabled in response to the detected increase in the rate of decrease in fuel quantity. The method further comprises determining that an angle of one or more flaps of the aircraft satisfies a threshold angle. The method additionally comprises generating an alert indicating possible fuel contamination of the one or more flaps due to jettisoned fuel.

Abstract: A method includes obtaining, at a processor, first data associated with operation of a vehicle. The first data includes sensor data from sensor(s) onboard the vehicle and indicates one or more parameter values of a first parameter measured by the sensor(s) and one or more associated timestamps. The method includes determining, by the processor, a first amount of storage space associated with storing a first portion of the first data in accordance with a first storage scheme and a second amount of storage space associated with storing the first portion in accordance with a second storage scheme that is different than the first storage scheme. The method further includes storing the first portion of the first data in a memory in accordance with the first storage scheme based on the first amount of storage space satisfying a first threshold that is based on the second amount of storage space.

Abstract: Aspects of the present disclosure provide a method and apparatus for predicting noncompliance conditions of rotating machinery in aerial vehicles. Embodiments include receiving records related to historical noncompliance conditions of a plurality of rotating machines. The records include deceleration time values related to the plurality of rotating machines. Embodiments include generating, based at least on the deceleration time values and the historical noncompliance conditions, a predictive model. Embodiments include receiving sensor data related to a rotating machine of an aerial vehicle. Embodiments include determining, based on the sensor data, a series of deceleration time values for the rotating machine. Embodiments include using the predictive model to determine whether a noncompliance condition is predicted to occur for the rotating machine based on the series of deceleration time values.

Abstract: Disclosed herein is a method that comprises detecting that a rate of decrease in fuel quantity for an aircraft exceeds a previously-determined baseline rate of decrease in fuel quantity for the aircraft by a threshold rate during a corresponding segment of flight of the aircraft. The method also comprises determining that a fuel jettison setting for the aircraft is enabled in response to the detected increase in the rate of decrease in fuel quantity. The method further comprises determining that an angle of one or more flaps of the aircraft satisfies a threshold angle. The method additionally comprises generating an alert indicating possible fuel contamination of the one or more flaps due to jettisoned fuel.

Abstract: A method includes obtaining data associated with operation of an aircraft and determining a first operational phase of the aircraft based on the data. The method includes identifying a candidate operational phase transition from the first operational phase to a candidate operational phase based on a first portion of the data satisfying a first condition associated with the candidate operational phase, the first portion of the data corresponding to a first time. The method includes evaluating a second portion of the data based on a second condition associated with the candidate operational phase, the second portion of the data corresponding to a second time that is subsequent to the first time. The method further includes, based on the second condition being satisfied, generating an operational phase transition indication that indicates an occurrence of an operational phase transition to the candidate operational phase at the first time.

Abstract: The present disclosure discloses a resistive random access memory (RRAM) and a method for manufacture the RRAM. The method includes: providing a bottom interconnection layer; forming a bottom dielectric layer above the bottom interconnection layer, the bottom dielectric layer comprising a via through the bottom dielectric layer that exposes a portion of the bottom interconnection layer; and forming a bottom electrode layer in the via, the bottom electrode layer including a first electrode selectively grown above the bottom interconnection layer. The bottom electrode layer manufactured in such a way provides improved filling capability of the bottom electrode layer in the via.

Abstract: A method and system operable to perform the method is provided for control of an autonomous or unmanned system. The method includes obtaining a mission model, wherein the mission model comprises a goal and one or more assets that are used to accomplish the goal; producing, by a first hardware processor, a plan goal graph (PGG) model based on the mission model; transforming, by a second hardware processor, the PGG model into a Bayesian Network (BN) model; computing a feasibility to execute a plan and an achievability of accomplishing the goal; and providing control instructions to the one or more assets to be used to accomplish the goal.

Abstract: A method includes obtaining, at a processor, first data associated with operation of a vehicle. The first data includes sensor data from sensor(s) onboard the vehicle and indicates one or more parameter values of a first parameter measured by the sensor(s) and one or more associated timestamps. The method includes determining, by the processor, a first amount of storage space associated with storing a first portion of the first data in accordance with a first storage scheme and a second amount of storage space associated with storing the first portion in accordance with a second storage scheme that is different than the first storage scheme. The method further includes storing the first portion of the first data in a memory in accordance with the first storage scheme based on the first amount of storage space satisfying a first threshold that is based on the second amount of storage space.

Abstract: A method and system operable to perform the method is provided for control of an autonomous or unmanned system. The method includes obtaining a mission model, wherein the mission model comprises a goal and one or more assets that are used to accomplish the goal; producing, by a first hardware processor, a plan goal graph (PGG) model based on the mission model; transforming, by a second hardware processor, the PGG model into a Bayesian Network (BN) model; computing a feasibility to execute a plan and an achievability of accomplishing the goal; and providing control instructions to the one or more assets to be used to accomplish the goal.

Abstract: A method includes obtaining data associated with operation of an aircraft and determining a first operational phase of the aircraft based on the data. The method includes identifying a candidate operational phase transition from the first operational phase to a candidate operational phase based on a first portion of the data satisfying a first condition associated with the candidate operational phase, the first portion of the data corresponding to a first time. The method includes evaluating a second portion of the data based on a second condition associated with the candidate operational phase, the second portion of the data corresponding to a second time that is subsequent to the first time. The method further includes, based on the second condition being satisfied, generating an operational phase transition indication that indicates an occurrence of an operational phase transition to the candidate operational phase at the first time.

Abstract: A method, apparatus, and scheduling system for managing a mix of time-critical messages and general messages. Messages, which are referenced by message identifiers stored in a transmission queue buffer, are scheduled for transmission using a schedule configuration table. The schedule configuration table defines an order for processing a mix of time-critical messages and general messages. The messages are scheduled according to the characteristics for time-critical messages and general messages defined in columns of configuration tables and status tables. The messages that have been scheduled are transmitted from an end system in which the time-critical messages are prioritized during transmission over the general messages.

Abstract: A method includes obtaining data associated with operation of a vehicle and determining a first operational phase of the vehicle based on the data. The method includes identifying a candidate operational phase transition from the first operational phase to a candidate operational phase based on a first portion of the data satisfying a first condition associated with the candidate operational phase, the first portion of the data associated with a first time. The method includes evaluating a second portion of the data based on a second condition associated with the candidate operational phase, the second portion of the data associated with a second time that is subsequent to the first time. The method further includes, based on the second condition being satisfied, generating an operational phase transition indication associated with the first time and that indicates an operational phase transition to the candidate operational phase.

Abstract: The present disclosure discloses a resistive random access memory (RRAM) and a method for manufacture the RRAM. The method includes: providing a bottom interconnection layer; forming a bottom dielectric layer above the bottom interconnection layer, the bottom dielectric layer comprising a via through the bottom dielectric layer that exposes a portion of the bottom interconnection layer; and forming a bottom electrode layer in the via, the bottom electrode layer including a first electrode selectively grown above the bottom interconnection layer. The bottom electrode layer manufactured in such a way provides improved filling capability of the bottom electrode layer in the via.

Abstract: A system for determining a worst case latency for a specific information flow that is part of a plurality of information flows and a worst case backlog for a specific queue that is part of a plurality of queues is disclosed. The plurality of information flows and plurality of queues are part of a configuration. The system performs operations including determining a maximum busy period length for the configuration. The operations include determining a set of candidate starting times for the configuration based on the maximum busy period length. The operations further include determining a maximum layout for a plurality of information flows within the configuration. The operations include updating the worst case latency and the worst case backlog based on the maximum layout. Finally, the operations include determining the worst case latency for the specific information flow and the worst case backlog for a specific queue.