Sas Institute Inc.

Hardi Desai

Varunraj Valsaraj

Jonathan Lee Walker

Sanjeev Shyam Heda

Kedar Shriram Prabhudesai

Jonathan James McElhinney

Rik Peter De Ruiter

Andrey Matveenko

Robert Winston Blanchard

Jorge Manuel Gomes Da Silva

Mohammadreza Nazari

Prathaban Mookiah

Weichen Wang

Amirhassan Fallah Dizche

Ruiwen Zhang

Davood Hajinezhad

Afshin Oroojlooyjadid

Xuejun Liao

Hamoon Azizsoltani

Ye Liu

Allen Joseph Langlois

Thomas Tuning

Saurabh Mishra

Priti Upadhyay

Kunind Sahu

Daniele Cazzari

In some examples, a system can access video data collected from one or more image sensors. The system can execute a human detection model to detect a person within a region of interest proximate to the environment based on the video data. The system can also execute an object detection model to detect a presence or absence of personal protection equipment (PPE) based on the video data. The system can associate the PPE with the person to detect an unsafe PPE status. The system can determine that the unsafe PPE status has persisted for a predetermined period of time. In response to determining that the unsafe PPE status has persisted for the predetermined period of time, the system can generate a signal indicating the unsafe PPE status.

Detection of unsafe personal protection equipment status with multi-modal context awareness

In some examples, a system can access video data collected from one or more image sensors, the video data showing a region of interest proximate to a machine. The system can execute an object detection model to detect that a person is within the region of interest proximate to the machine based on the video data. The system can detect a motion status of a component of the machine. The system can execute a pose estimation model on the video data to estimate a pose of the person with respect to the machine. The system can detect a safety rule violation based on the pose of the person with respect to the machine, and the motion status of the machine. The system can transmit a signal to a controller of the machine in response to detecting the safety rule violation.

Detection and mitigation of unsafe behaviors using computer vision

The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

Method and system for obtaining item-based recommendations

A flexible computer architecture for performing digital image analysis is described herein. In some examples, the computer architecture can include a distributed messaging platform (DMP) for receiving images from cameras and storing the images in a first queue. The computer architecture can also include a first container for receiving the images from the first queue, applying an image analysis model to the images, and transmitting the image analysis result to the DMP for storage in a second queue. Additionally, the computer architecture can include a second container for receiving the image analysis result from the second queue, performing a post-processing operation on the image analysis result, and transmitting the post-processing result to the DMP for storage in a third queue. The computer architecture can further include an output container for receiving the post-processing result from the third queue and generating an alert notification based on the post-processing result.

Flexible computer architecture for performing digital image analysis

A computing device trains a machine state predictive model. A generative adversarial network with an autoencoder is trained using a first plurality of observation vectors. Each observation vector of the first plurality of observation vectors includes state variable values for state variables and an action variable value for an action variable. The state variables define a machine state, wherein the action variable defines a next action taken in response to the machine state. The first plurality of observation vectors successively defines sequential machine states to manufacture a product. A second plurality of observation vectors is generated using the trained generative adversarial network with the autoencoder. A machine state machine learning model is trained to predict a subsequent machine state using the first plurality of observation vectors and the generated second plurality of observation vectors. A description of the machine state machine learning model is output.

Automated control of a manufacturing process

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Raleigh, NC, United States of America

Hardi Desai