Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

AI to predict emergency visits

ClosedLoop.ai is an AI-based predictive analytics platform that goes beyond traditional claims-based risk scores to use all patient-related healthcare data to provide both clinicians and care managers with a full breadth of timely, transparent and accurate predictions of health outcomes. ClosedLoop.ai helps value-based providers confidently answer a variety of health-care questions like, which patients are most likely to be readmitted to the hospital? Or which of my patients would most benefit from establishing a relationship with a primary care provider? The objectives are to evaluate the performance of seven prediction models that answers these kinds of questions and to improve one of the seven prediction models by identifying and training a deep learning algorithm.

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Faculty Supervisor:

Yoshua Bengio

Student:

Vincent Morissette-Thomas

Partner:

Logibec

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Power network transfer capability

Hydro-Québec is a public utility that generates and distributes electricity. Despite selling most of its electricity in Québec, its most lucrative sales are in the neighboring markets. To ensure the best possible quality of service, the transmission system must remain stable, but to maximize profits, the company also wants to increase its transmission capacity to maximize energy exports. The transfer limit is now conservatively estimated based on a certain combination of simulated network configurations. This project aims to more accurately estimate the transfer limits of the electric grid and the uncertainty of these estimated limits. Recent advances in machine learning, especially in deep learning, in conjunction with more traditional algorithms used in computer science, have the potential to improve these estimates and therefore augment exports for Hydro-Québec.

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Faculty Supervisor:

Yoshua Bengio

Student:

Louis-François Préville-Ratelle

Partner:

Hydro-Québec

Discipline:

Computer science

Sector:

Energy

University:

Université de Montréal

Program:

Accelerate

Satellite Solar Radiation Nowcasting

The main duty of Hydro-Québec is to repond efficiently to the energy demand of customers, in a safe and secure way while remaining competitive in the markets as well. The main goal of this start-up project is to support Hydro-Québec in developing a future-oriented energy system by proposing innovative technical solutions. Among these solutions, deep learning has been the final choice. Using a deep learning approach, satellite images, weather model outputs and data from solar radiation measurement stations, will be use for the development of a solar radiation nowcasting model. This project will have impact on several business functions related to: PV forecasting, demand forecasting, hydrology, etc.

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Faculty Supervisor:

Yoshua Bengio

Student:

Jimmy Leroux

Partner:

Institut de recherche d’Hydro‐Québec

Discipline:

Computer science

Sector:

Energy

University:

Université de Montréal

Program:

Accelerate

Pathways for Deep Decarbonization in Cities: Mechanisms, tools and governance structures for transformative climate action

As the urgency for action against climate change increases, local governments around the world are committing to reducing greenhouse gas emissions through deep decarbonization targets. Cities are the largest place-based sources of GHG emissions and therefore have great potential to reduce emissions on a global scale. In order to reach meaningful reduction levels, transformative change is not only needed to create deep decarbonization pathways, but also to disrupt the current path dependency on carbon that most cities face today. This qualitative study will examine pathways within climate action plans. It will also identify the actors, governance mechanisms, and tools that cities are using in order to achieve their decarbonization targets by mid-century. Through a partnership with ICLEI Canada, the student intern will gain access to relevant internal research data and resources needed for the study, while the partner organization expects that the academic research will be useful to their existing and ongoing projects. The purpose of this project is to inform the creation and implementation of deep decarbonization plans for cities.

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Faculty Supervisor:

Amelia Clarke

Student:

Samantha Linton

Partner:

ICLEI Local Governments for Sustainability

Discipline:

Environmental sciences

Sector:

Other

University:

Université de Montréal

Program:

Accelerate

Optimization of a calibration procedure for Mecademic’s Meca500 robot arm

Mecademic manufactures the smallest and most precise six-axis robot arm. The repeatability of this robot is better than 0.005 mm, but like any industrial robot, the robot’s accuracy is far worse. The only practical way of improving the robot’s accuracy is to calibrate each individual robot. While various methods for the calibration of six-axis robot arms have already been developed in the past, the proposed research project is different since the robot will be installed directly on the CMM and the full pose (position and orientation) will be measured by touch probing the datum cube attached to the robot’s flange. Moreover, since the applicant will have full access to all physical characteristics of the robot, an attempt will be made to develop a simpler mathematical model, so that the model can be used in real time.

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Faculty Supervisor:

Souheil-Antoine Tahan

Student:

Oleksandr Stepanenko

Partner:

Mecademic

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

École de technologie supérieure

Program:

Accelerate

Autonomous structure detection and inspection using unmanned aerial systems

In this project, a new method is developed to optimize the performance of an Unmanned Aerial Vehicle (UAV) for autonomous detection and on-the-job view-planning of infrastructure elements with the purpose of their accurate three-dimensional (3D) modeling. The existing view-planning approaches in the literature have mostly modeled non-complex or small-scale objects and have rarely been adapted to flying robots. In addition, the target object is often identified by human operators. This research addresses these problems by training a drone to find the desired object of interest in an unknown environment during an inspection task without human interventions. To this end, first, a technique for object detection will be developed to recognize and locate the target object while the drone is exploring the environment. Second, based on the available information about the desired object, the drone will start next-best-view and motion planning to acquire an adequate photogrammetric network of images in order to reconstruct the inspection target in 3D both accurately and completely. This research will have important impacts on the evolution of infrastructure monitoring and assessment approaches using UAV systems.

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Faculty Supervisor:

Mozhdeh Shahbazi

Student:

Parnia Shokri

Partner:

Centre de géomatique du Québec Inc

Discipline:

Engineering - computer / electrical

Sector:

Professional, scientific and technical services

University:

University of Calgary

Program:

Accelerate

Sensitivity Analysis of Gas and Particulate Matter Emissions from Future Power Generation in the Province of Alberta

The Province of Alberta (AB) has decided to phase out coal power generation by 2030 and increase renewable electricity production to 30% of total power generation, also by 2030 with the remaining 70% of the power generation being dependent on natural gas. It has been conjectured that part of generation portfolio could be diversified to include nuclear power generation. The current proposal aims at studying available power generation (seasonally) in Alberta and create a model to predict their gas (CO2, CH4, and NOx: mainly N2O, but also NO and NO2) and PM1 (particulate matter) emissions in time using different generation portfolios. Once this model is verified against gas emission data obtained from the literature, future seasonal emissions will be predicted after varying the generation portfolio to include a certain amount of nuclear power generation (from 0 to 25% of the total output). An uncertainty analysis of the prediction will also be performed.

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Faculty Supervisor:

Edgar Matida

Student:

Nick Ogrodnik

Partner:

Canadian Nuclear Association

Discipline:

Engineering - mechanical

Sector:

Energy

University:

Carleton University

Program:

Accelerate

Pattern Recognition: The Exploration of Machine Learning Algorithms in Archaeological Site Prediction, Fraser River Valley, British Columbia

Golder Associates Ltd., teaming with the Seyem’ Qwantlen Business Group (Kwantlen First Nation), was retained by the Township of Langley to develop a model to predict the location of unrecorded archaeological sites on a 10,000 year-old landscape located in the Fraser River Valley, British Columbia. Conventional predictive modelling techniques are common practice however with the increased availability of more powerful computers and software there is a growing potential for using machine learning algorithms to predict a wider variety of archaeological site types with greater accuracy. For this Pattern Recognition Project (PRP) the intern will complete a machine learning literature review and an examination of the local archaeology to identify potential machine learning methodologies and algorithms to predict site locations as well as the best environmental, physiographic and cultural variables to input into the model. This research will be used to create a report which describes the PRP, its results, and recommendations. The results generated by the PRP will be used to pilot a new approach for archaeological predictive modelling using machine learning algorithms and will ultimately be used to assist the Township in responsibly managing local archaeological sites.

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Faculty Supervisor:

Andrew Martindale

Student:

Raini Johnson

Partner:

Golder Associates

Discipline:

Anthropology

Sector:

Construction and infrastructure

University:

University of British Columbia

Program:

Accelerate

Measurement-based Distribution System Models for Distributed Energy Resources Control

The integration of significant capacities of distributed energy resources (DERs) such as renewable wind and solar generation for a more sustainable energy future creates several challenges to the reliable and efficient operation of power distribution systems. These include: (i) Uncertain and intermittent nature of renewable generation compromises power quality for end-customers.  (ii) Up-to-date distribution system network topologies are not well known and their real-time monitoring is limited. As a result, effective management of DERs is challenging. (iii) Establishing full network observability may be prohibitively costly. (iv) Accurate DER control may require solving complex optimization problems.
To this end, the goal of this research project is to study measurement-based methods to design DER management systems by developing equivalent, reduced-network models of distribution systems from real-time  measurements.

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Faculty Supervisor:

Yu (Christine) Chen

Student:

Severin Nowak

Partner:

Enbala Power Networks Inc

Discipline:

Engineering - computer / electrical

Sector:

Energy

University:

University of British Columbia

Program:

Accelerate

Identifying SMEs’ Barriers to Electronic Payment Adoption

During the past two decades in Canada, use of electronic payment has steadily increased. However, despite the downward trend in the volume, the value of cheques has steadily increased, with the five-year average volume growth increasing by about 2% due in large part to their common presence in the business-to-business space. These trends indicate that even with emerging of EFT payment instruments and online transfer options as substitutes for cheque, there are yet some barriers to electronic payment adoption specially for small and medium businesses. In this research we aim to not identify the possible SMEs’ barriers to electronic payment adoption, prioritize them, and find the instrumental and psychological mechanisms behind them.

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Faculty Supervisor:

Frank Safayeni

Student:

Ahmad Tanehkar

Partner:

Interac Corp

Discipline:

Business

Sector:

Finance, insurance and business

University:

University of Waterloo

Program:

Accelerate

Study Hot Cracking Susceptibility of Critical AA6111 Aluminum Alloys during Direct Chill Casting

AA6111 aluminum alloys possess a combination of excellent strength, good formability and good corrosion resistance that are widely use in the car panel manufacture. Direct chill (DC) casting process is typically employed for producing such alloy ingots. Despite its advantages, AA6111 alloys are considered as “hard-to-cast” alloy among 6xxx alloys because of high susceptibility to hot cracks. The present project will investigate the effect of chemical composition and grain refinement on hot crack susceptibility. Preventing one of the major cast defects, hot cracks, in aluminum ingot production not only increases the productivity of DC casting but also significantly reduce the production cost. This project will provide the best guideline to the industrial partner for improving the industrial production of aluminum alloys. The knowledge gained through this study would be highly beneficial for all aluminum industry in Quebec and Canada.

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Faculty Supervisor:

X-Grant Chen

Student:

Hamid Khalilpoor

Partner:

Rio Tinto Alcan

Discipline:

Engineering - other

Sector:

Manufacturing

University:

Université du Québec à Chicoutimi

Program:

Accelerate

The History of the Quebec Common Gaol

This is a project to update the Literary and Historical Society of Quebec’s permanent exhibit, Doing Time: The Quebec City Common Gaol (1808 – 1867). Since the exhibit was launched in 2011, much research has been done on the history of the gaol.  Doing Time is seen by some 25,000 visitors per year. For some, it is their only exposure to the history of prison life. The exhibit needs to be as accurate as possible. Revision will be done by an intern working under the supervision of Donald Fyson, professor of history at Université Laval and specialist in the gaol’s history.

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Faculty Supervisor:

Donald Fyson

Student:

Malena Johnson

Partner:

Morrin Centre

Discipline:

History

Sector:

Arts, entertainment and recreation

University:

Université Laval

Program:

Accelerate

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