Sas Institute Inc.

Hamza Mustafa Ghadyali

Varunraj Valsaraj

Kedar Shriram Prabhudesai

Jonathan Lee Walker

Xunlei Wu

Jorge Manuel Gomes Da Silva

Mohammadreza Nazari

Xingqi Du

Jinxin Yi

Afshin Oroojlooyjadid

Davood Hajinezhad

Alexander Richard Phelps

Bahar Biller

Sanjeev Shyam Heda

Ivan Borges Oliveira

Allen Joseph Langlois

Nabaruna Karmakar

Frederic Combaneyre

Sharmin Pathan

A computing system detects a defective object. An image is received of a manufacturing line that includes objects in a process of being manufactured. Each pixel included in the image is classified as a background pixel class, a non-defective object class, or a defective object class using a trained neural network model. The pixels included in the image that were classified as the non-defective object class or the defective object class are grouped into polygons. Each polygon is defined by a contiguous group of pixels classified as the non-defective object class or the defective object class. Each polygon is classified in the non-defective object class or in the defective object class based on a number of pixels included in a respective polygon that are classified in the non-defective object class relative to a number of pixels included in the respective polygon that are classified in the defective object class.

Manufacturing defective object detection system

A computing system responsive to obtaining original image data, detects a set of data point(s), in the original image data, that indicates an object. The system determines, based on the set of data point(s), a set of pixels associated with the object in the original image data. The system generates an alternative visual identifier for the object that provides a unique identifier for the set of pixels absent in the original image data. The system generates, autonomously from intervention by any user of the computing system, pixel information to conceal feature(s) of the object. The system obtains modified image data comprising the alternative visual identifier. The modified image data further comprises the feature(s) of the object in the original image data visually concealed in the modified image data according to the pixel information. The system outputs an image representation of a trajectory of the object through the modified image data.

Real-time concealed object tracking

A computing system obtains image data representing images. Each of the images is captured at different time points of a physical environment. The physical environment comprises a first object and a second object. The computing system executes a control system to augment the physical environment. The control system detects a group forming in the images. The control system tracks an aspect of a movement, of a given object, in the group. The control system simulates the physical environment and the movement, of the given object, in the group in a simulated environment. The control system evaluates simulated actions in the simulated environment for a predefined objective for the physical environment. The predefined objective is related to an interaction between objects in the group. The control system generates based on evaluated simulated actions and autonomously from involvement by any user of the control system, an indication to augment the physical environment.

Real-time spatial and group monitoring and optimization

A computing system receives historical data. The historical data comprises physical actions taken in an experiment in a physical environment. The experiment comprises user-defined stages. The historical data comprises a recorded outcome, according to user-defined performance indicator(s) related to the user-defined stages, for each physical action taken in the experiment. The system generates, by a discrete event simulator, a computing representation of a simulated environment of the physical environment. The simulated environment comprises processing stages. The system obtains simulation data. The simulation data comprises simulated actions taken by the discrete event simulator. The simulation data comprises a predicted outcome, according to user-defined performance indicator(s) related to the processing stages, for each simulated action taken by the discrete event simulator. The system validates accuracy of the discrete event simulator at predicting the recorded outcome in the experiment. The system trains a computing agent according to a sequential decision-making algorithm.

Discrete event simulation with sequential decision making

A computing system obtains image data capturing first and second objects. The system determines, based on user-identified data points, boundaries of the objects and generates a component of a dataset by computing a first data value related to an attribute of a key point in the first image; and computing a second data value related to an attribute of a key point in the first image. The system generates a second component of the dataset, the second component representing updated relative information between the first and second object by generating predicted changes in the first data value and second data value for the second image. The system computes a third data value and a fourth data value related to respective data points in a first and second polygon in the second image. The generating the updated relative information is based on the predicted changes and computed values.

Object and data point tracking to control system in operation

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Apex, NC, United States of America

Hamza Mustafa Ghadyali