Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.