Honeywell International Inc.

Parag Ravindra Rao

Abhishek Alladi

Aralakuppe Ramegowda Yogesha

Debabrata Pal

Luke M Zeleznak

Mohammed Ibrahim Mohideen

Ke Lin

Shouvik Das

Rafeek Sainudeen

Perumal Kumar

Yogesha Aralakuppe Ramegowda

Srikanth Olety Subramanya

Pachipulusu Lingesh Sumanth

Koyalkar Raman Kishore

Jayakumar S S

A method for verifying data and control couplings of software under test comprises obtaining software architecture and design information for the software; extracting expected control coupling information based on an order in which software component calls are in the software architecture; extracting expected data coupling information from one software component to another software component in a sequence diagram; updating the expected control and data coupling information in an expected control and data coupling sequencing table; deriving actual control coupling information from execution trace data obtained from a requirements based test execution; deriving actual data coupling information from data trace information; and updating the actual control and data coupling information in an actual control and data coupling sequencing table. The method then compares the expected and actual control coupling information, and compares the expected and actual data coupling information, to verify whether software components are interacting with each other as expected.

System and method to measure and verify data and control coupling between software components without code instrumentation

Disclosed are methods, systems, and non-transitory computer-readable medium for managing energy use in a vehicle. For instance, the method may include receiving forecasted data from a first external source, receiving real-time data corresponding to at least one weather parameter at a first location at a first time, and continuously determining whether to perform an adjustment to a control parameter of the vehicle by using a machine learning model that is based on the forecasted data for the at least one weather parameter, the real-time data for the at least one weather parameter, a battery condition of the vehicle, and/or an estimated amount of energy consumed by traveling along a first navigation path.

Systems and methods for managing energy use in automated vehicles

A system for fluid flow verification comprises a video recording device that captures frames of actual fluid or fluid display, and a processor coupled to the recording device. The processor hosts fluid verification modules, including a fluid flow rate verification module, and a fluid color verification module. The fluid flow rate verification module is operative to receive the frames; binarize region of interest, and compute histogram of white/black pixels; count white pixels to detect actual fluid quantity; determine whether actual fluid quantity matches expected fluid quantity; if there is a match, measure fluid change rate; determine whether there is steady fluid flow; if not, report fluid flow rate anomaly. The fluid color verification module is operative to: receive binarized region of interest and histogram; detect fluid color by extracting sample color mask of pixels; determine whether detected fluid color matches expected fluid color; if not, report fluid color anomaly.

System and method for vision based graphical fluid flow anomaly detection for display verification

The disclosure provides a method and system of improvement in the real-time operation of a terminal station in an industrial facility using a machine learning-based self-adaptive system comprising obtaining real-time operations data and historical data stored in a local database or at a cloud-storage. The data relates to input parameters of the terminal station. The method includes inputting the input parameter to a machine learning configurable module of the machine learning-based self-adaptive system and analyzing the input parameter using dynamic machine learning models and algorithms to identify patterns to each of the input parameters. The method further includes evaluating the identified pattern against the real-time operations data obtained from the terminal station and predicting at least one output parameter based on the input parameter and the identified pattern against the real-time operations. Based on the prediction, adjusting the output parameter of the real-time operations data.

Real-time operation of an industrial facility using a machine learning based self-adaptive system

A method of verifying graphical objects using color invariant feature matching is provided. The method comprises determining, for each of a plurality of primary color channels, a color value for each inlier pixel in an actual object for that primary color channel; determining, for each color channel, a color difference factor (CDF) for an expected object by comparing, for each of the plurality of primary color channels, the color value for each inlier pixel in the expected object to the color value for each corresponding inlier pixel in the actual object; determining a false point value for the expected object from the CDF; and determining that the expected object matches the actual object when the false point value for the expected object is lower than the false point value for other expected objects.

System and method for color invariant feature matching

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Bengaluru, India

Parag Ravindra Rao