General Electric Company

Lockheed Martin Corporation

Gerald Bowden Wise

Scott Charles Evans

Jeffrey Wayne Eberhard

Achalesh Kumar Pandey

John Michael Lizzi

Rajesh Venkat Subbu

Kareem Sherif Aggour

Andrea Marie Schmitz

Daniel Joseph Cleary

Louis John Hoebel

Vijay Shiv Kumar

Arunvenkataraman Subramanian

Frank William Ripple, Jr

Helena Goldfarb

Jonathan Dehn

Paul W Mettus

Bradley A Culbertson

Michael Dermot Denvir

John Edmund LaFleche

Liviu Nedelescu

A method for controlling a wind turbine includes detecting a plurality of analytic outputs relating to power performance of the wind turbine from a plurality of different analytics. The method also includes analyzing the plurality of analytic outputs relating to power performance of the wind turbine. Further, the method includes generating at least one computer-based model of the power performance of the wind turbine using at least a portion of the analyzed plurality of analytic outputs. Moreover, the method includes training the computer-based model(s) of the power performance of the wind turbine using annotated analytic outputs relating to the power performance of the wind turbine. In addition, the method includes estimating a power magnitude of the wind turbine using the machine-learned computer-based model(s). As such, the method includes implementing a control action when the power magnitude of the wind turbine is outside of a selected range.

Machine-learning model-based analytic for monitoring wind farm power performance

According to some embodiments, system and methods for building a model are provided, comprising a display; a memory storing processor-executable process steps; and a processor to execute the processor-executable process steps to cause the system to: present a user interface on a display, the user interface including one or more user-entry fields to build a model, user-entry fields is associated with a selection of big data or small data for use with the model; receive at least one data source in a user-entry field associated with the model; determine if data in the data source includes big data or small data; and in response to the determination of big data or small data in the data source, execute the model with data from the data source in a big data or small data execution environment. Numerous other aspects are provided.

Multimodal, small and big data, machine tearing systems and processes

The subject matter disclosed herein relates to patient imaging systems, and more specifically, to portable X-ray imaging systems. In a first embodiment, a patient imaging system is presented. The patient imaging system includes an X-ray source configured to emit X-rays and a wireless X-ray detector configured to detect the emitted X-rays and acquire patient image data. The patient imaging system also includes an acquisition control system configured to initialize and prepare the patient imaging system for X-ray emission and detection. The acquisition control system is also configured to receive the acquired patient image data from the X-ray detector, and to non-deterministically control the operation of the X-ray source and the wireless X-ray detector. The patient imaging system also includes one or more user interfaces configured to instruct the acquisition control system when a user is ready for the patient imaging system to initialize, to prepare for X-ray emission and detection, and to begin X-ray emission and detection.

System and method for controlling a portable X-ray system

Systems and methods for airspace demand prediction with improved sector level demand prediction are provided. In one embodiment, an air traffic demand prediction system () operable to predict demand within an airspace divided into sectors includes an expanded route predictor () operable to generate predicted two-dimensional expanded route information () associated with at least one requested flight (), a trajectory modeler () operable to generate predicted four-dimensional expanded route information (), a sector crossing predictor () operable to generate predicted sector crossing information (), a departure time predictor () operable to generate predicted departure time information (), and a demand modeler () operable to generate a demand model (), the demand model () including predicted time intervals associated with the at least one requested flight indicating when it is expected to be present within one or more sectors of the airspace.

Air traffic demand prediction

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Clifton Park, NY, United States of America

Gerald Bowden Wise