Public Engagement in Nuclear: Medicine, Mining, Meeting Climate Change Commitments

This project explores methods of engagement and communication that bridge the science, public and policy gap in respect of the nuclear ecology: medicine, mining, and meeting climate change commitments (through nuclear energy small modular reactors (SMRs)). Saskatchewan, the home of uranium mining and where scientists helped develop the world’s first cobalt-60 nuclear medicine scanning machines, is an ideal location for this inter and transdisciplinary exploration.

MBSE for Modeling, Evaluation, and Optimization of Modular Robotic Systems: Case Study on AIS

Since 1960, System engineering has been used as an approach for multidisciplinary and concurrent design of complex systems. It relies on a system-centered thinking to solve problems and different design process models have been used for system engineering such as V-model. Model-based System engineering (MBSE) was developed to replace documents with models.AIS is developing mobile robots for different markets. Mobile robots are very complex systems in nature with a large number of interacting components.

Investigation of Water-in-Oil Emulsion on CSI Solvent Dissolution and Ex-solution Performance for Heavy Oil

This research work will establish a systematic workflow for analyzing transient equilibrium foamy oil phase behavior by coupling the CCEC tests, depletion rate and presence of water-in-oil emulsion which are seldom performed for heavy oil. It will provide a strong connection and comparison with previously studies which was conducted in the absence of water-in-oil emulsion. It will create a strong connection between phase behavior with fluid properties, operating conditions and kinetics.

StreamSight: Deep Learning Techniques for Managing Contaminants in Residential Curbside Recycling

This project works to classify contaminants found in residential curb-side recycling. This is done automatically using computer vision techniques. As recycling is tipped into the recycling truck, cameras take pictures of the recycling and computer vision software works to identify contaminants found in the load. With municipalities equipped with this fine-grained data, they will have the ability to produce targeted education campaigns to improve the recycling process and reduce contamination found in recycling.

Unmanned Aerial Vehicle Swarm Collaboration for Weed Control in Field Crops is building solutions to minimize chemical consumption while maintaining weed control through Intelligent UAV based application. has working survey drones that can fly a field, capture images and use AI to map weeds to be sprayed later. also has “See & spray” drones that can fly a field, identify weeds and spray them. We now need to scale our capabilities through drone swarming. The required speed and coverage will require an autonomous and collaborative swarm of drones (or a combination of more capable drones and/or more efficient field coverage).

Anomaly detection using AI/ML for Network Correction

Anomaly detection or outlier detection is a technique to identify rare items, observations or events which are differing significantly from most of the data or do not conform to the expected behavior of the system. Typically, anomalous data cause numerous problems in the computer networking and communication system. This project aims to develop an advanced anomaly detection algorithm by utilizing state-of-the-art machine learning and artificial intelligence techniques and combining it with existing anomaly detection techniques.

A Realistic Machine Learning-based Model for Failure Prediction and Propagation in Smart Grid Networks

Cyber-Physical Systems (CPS) combine communication and information technology functions to the physical components of a system for purposes of monitoring, controlling, and automation. The power grid is becoming one of the largest CPS, where grid components are controlled based on the synergies in the cyberspace. CPS hold a great promise to improve the efficiency and productivity of numerous sectors in Canada and around the world.

Flow Optimization for Wormhole Regions of Post-CHOPS Reservoirs

This project aims to provide Canadian petroleum companies a comprehensive big-data-analytics tool that concludes the essential controlling parameters which enable successful experimental and numerical studies on CO2-based solvent injection processes in post-CHOPS reservoirs. The proposed database includes relevant experimental research work that expand through multiple experimentation scales, as well as relevant numerical research work that cover from pore network simulation, Darcy-scale reservoir simulation, CFD simulation etc.

Evaluation of CO2 Sequestration Opportunities in Lloydminster Post-chops Heavy Oil Reservoirs and Underlying Aquifers

Carbon capture and storage is a feasible, reliable and economic approach to reduce CO2 emission. Llydiminster area on the boarder of the provinces of Saskatchewan and Alberta is an area with significant heavy oil production and a large amount of CO2 resources from thermal operated heavy oil production facilities and oil upgraders. Deploying CCU technology in this area can significant reduce the CO2 emission in the oil industry in this area. This study focus on feasibility of carbon geo-sequestration in those heavy oil reservoirs and underlying aquifers in this area.

Reinforcement Learning for anomaly detection in real-time camera feed

How to automatically monitor wide critical open areas is a challenge to be addressed. In this project we are looking for using CNN+LSTM technique for identifying anomalies and by using a deep reinforcement learning approach, classify them into one or more groups such as health, crime, accidents etc. This project aims to alleviate this problem by using deep learning reinforcement algorithms to emergency conditions in a video feed. In this way, the intern should work on this real-time data to, at first, finding anomalies from the live video, then, categorize them into relevant classes.