Innovative Projects Realized

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

31620 Completed Projects

2978
AB
5221
BC
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projects by Category

À la croisée de la recherche et de l’intervention : une synthèse des connaissances théoriques, empiriques et pratiques sur les options d’apprentissage soutenant la capacité à travailler ensemble à l’ère de l’industrie 4.0

L’avènement de la quatrième révolution industrielle incite les organisations à revoir leurs pratiques afin d’outiller leurs membres quant à leur façon de travailler ensemble (McKinsey Global Institute, 2018). Cette nouvelle réalité fait ressortir le besoin de disposer de moyens de développement adaptés à la complexité et à la diversité des contextes des travailleurs. Face au volume de connaissances éparses, disparates, voire contradictoires, les décideurs organisationnels se heurtent à un véritable casse-tête quant au choix des options d’apprentissage à mettre en oeuvre (Cumberland, Herd, Alagaraja, & Kerrick, 2016). Devant cet enjeu organisationnel, la présente étude vise à dresser un portrait des connaissances portant sur les options d’apprentissage susceptibles de soutenir le développement de la capacité à collaborer avec autrui. Pour ce faire, le projet s’appuie sur une méthode systématique de recherche de données documentaires qui place la consultation des utilisateurs potentiels des connaissances au coeur de la démarche scientifique.

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Faculty Supervisor:

Marie Malo;François Courcy;Philippe Longpré;Julie Lévesque-Côté;Olivier Laverdière

Student:

Partner:

Domtar (Windsor, QC)

Discipline:

Sociology

Sector:

Agriculture; Manufacturing

University:

Université de Sherbrooke

Program:

Accelerate

Developing a facility-level energy load shape simulation method for utility planning

Utility planners require an understanding of how and when energy is used among their customers. Hourly “load shapes”, that represent facility electricity of natural gas use for each hour of the year, often disaggregated into individual “end-uses” such as space heating, lighting or water heating are the most detailed information of this type typically available to planners.
These load shapes allow planners to make bottom-up estimates of aggregate loads over their electricity or gas distribution system over various geographies, allowing them to ensure transmission/distribution infrastructure and generation/commodity supply will meet demand, integrate intermittent renewable resources, and estimate the effects of efficiency, demand-response and distributed generation measures.
This research will build on previous work by the partner to estimate annual hourly energy use by facility type and energy end-use in utility service territories. The methodology is expected to be based on energy load research for similar facilities (i.e. office buildings), equipment (i.e. chillers), or actions (i.e. application of energy conservation or demand response measures), and adjusted based on climate data, known annual energy use at the system level, and qualitative/quantitative survey data.

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Faculty Supervisor:

Burak Gunay;Liam O'Brien

Student:

Partner:

Posterity Group Consulting

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Carleton University

Program:

Accelerate

Helping Servus Members Reach Financial Goals via Transfer Learning

In this self-contained project we will investigate how machine learning can be applied to help provide personalized financial advice. Machine learning is a term that designates types of artificial intelligence that rely on learning behaviors from data or experience. Specifically, the goal of this work is to apply machine learning to Servus Credit Union’s Noble Purpose “Shaping Member Financial Fitness” to provide personalized recommendations to individual members who have set specific financial goals.

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Faculty Supervisor:

Matthew Guzdial

Student:

Partner:

Servus Credit Union Ltd

Discipline:

Computer science

Sector:

Finance and Insurance

University:

University of Alberta

Program:

Accelerate

An exploratory study of Asian International Students‘ experiences in Ontario Schools

A rapid increase in the number of Asian international students attending K-12 schools has led to the investigation of the complex, multi-layered aspects of the life experience and needs of Asian international students in Ontario schools. Working closely with View-Wide International Education Group and using multiple case studies, this study will explore the factor of facilitating their transition and articulate the nature and challenges of Asian international students’ experiences in adjusting to new school settings in Ontario. It will also investigate their perception of the preparedness for studying in Canada and ensure their needs of academic and social success in educational endeavour. The study is important in that it can provide teachers, counselors, school administrators, early study abroad agencies, and after-school institutes with concrete data based upon the experiences of Asian international students

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Faculty Supervisor:

Douglas McDougall

Student:

Partner:

View-Wide International Education Group

Discipline:

Sociology

Sector:

Education

University:

University of Toronto

Program:

Accelerate

Development of a Framework for Risk Assessment for Horizontal Directional Drilling Applications

Horizontal directional drilling (HDD) is a steerable trenchless construction method adapted in the
1970’s from the horizontal oil well drilling technology. HDD has become a very successful worldwide
technology for trenchless utility and pipeline installations becoming an annual multi-billion dollar
industry across five continents. The HDD process involves considerable risks based on many hazards
and uncertainties including risk resulting from unknown ground conditions, construction risks, and
environmental concerns. In response to the critical demand from industry to address these HDD
issues, the University of Alberta and The Crossing Company (TCC) established a collaborative
research partnership. The proposed research will focus on developing a risk assessment framework
addressing the geotechnical and construction risks involved with HDD to develop risk mitigation
strategy guidelines and predictive model development to enable trenchless technology stakeholders to
appropriately and successfully plan and construct HDD installations.

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Faculty Supervisor:

Alireza Bayat

Student:

Partner:

The Crossing Company Ltd

Discipline:

Engineering

Sector:

Transportation and warehousing

University:

University of Alberta

Program:

Accelerate

Détection proactive/préventive d’anomalies dans les machines à commande numérique (CNC)

Au cours des dernières années, CAM Solutions a remarqué que plusieurs de ses clients. des usines munies de machines à commande numérique (CNC), éprouvent systématiquement de la difficulté à les faire fonctionner à pleine capacité et selon un horaire établi. En effet, l’apparition constante de pannes et de problèmes imprévus nuit à la performance de ces machines et entraîne des réparations coûteuses avec un coût d’opportunité élevé : ces machines-outils peuvent coûter plus d’un million de dollars, les matières premières et la main-d’oeuvre entraînant représentent un coût horaire variable important. Les temps d’arrêt sont donc perturbateurs et surtout coûteux.
Pour remédier à ce problème, le projet supervisé suivant a été mis en place. Il aura comme objectif trouver et modéliser les causes principales des pannes / anomalies affectant les machines industrielles CNC. Ceci permettra ultimement de développer une solution complète permettant de mitiger proactivement leur occurrence. Pour arriver à modéliser adéquatement ce phénomène, des méthodes d’apprentissage chronologiques, supervisées et/ou non supervisées, seront utilisées sur les données disponibles (liées aux différentes machines, pièces et tâches) dans le but d’effectuer de l’inférence et de la prévision.

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Faculty Supervisor:

Aurélie Labbe

Student:

Partner:

CAM Solutions

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

HEC Montréal

Program:

Accelerate

Intelligent Orchards: Redefining the Production and Management of Tree Fruits

One of the main challenges in tree fruit orchards is to accurately predict apple yield and identify the health of individual trees (e.g., healthy foliage, fruit development, detecting and identifying diseased trees). Manual performance of these tasks is labour intensive and costly. Therefore, automated processes provide novel solutions with enhanced accuracy, efficiency, and productivity. The proposed solution is to develop an automated system that utilizes machine vision and artificial intelligence strategies to accurately count flower blooms and apples, detect and identify diseased fruit and trees, and improve yield estimates for producers. Automating this process will reduce labour costs and improve apple yields for producers. The system will provide truly meaningful and easy-to-digest information to the farmer about the orchard, including the predicted productivity, suggested trimming, and possible growing issues (such as unhealthy trees or areas that require more attention).

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Faculty Supervisor:

John Cline;Mohammad Biglarbegian

Student:

Partner:

George Weston;Dr Robot Inc

Discipline:

Engineering

Sector:

Manufacturing; Retail trade

University:

University of Guelph

Program:

Accelerate

Visual-haptic Representation for Zero-shot Learning

Humans recognise objects in the world leveraging multi-modal sensory inputs beyond visual aspects (images and videos). Touch based information (Haptics) possesses rich information about structure, shape and other objetness properties. In this work, we will study and learn cross-modal representations between vision and touch. To connect vision and touch, we plan to introduce a zero shot classification task of recognising unseen object categories from shapenet dataset using haptics signals. We will train our model to encode the haptics information to a view agnostic embedding space that captures the geometrical aspects of the object. To support our claims, we will use shapenet dataset, a repository consisting of CAD models of various categories of objects that can be rendered from different views and the Johns Hopkins Modular Prosthetic Limb for haptics data. Our hypothesis is that our learnt representation can help transfer representations across modalities, for zero shot classification and object retrieval.

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Faculty Supervisor:

Aaron Courville

Student:

Partner:

ServiceNow Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Quantifying the oxidization of polysaccharides and optimizing dextran-bovine serum albumin glycation conditions for development of a new pneumococcal vaccine

Pneumonia remains the single leading cause of childhood death under age 5 worldwide. The price per dose of current vaccines is high and supply is limited due to a complex manufacturing process and low yield, significantly reducing its distribution in developing nations. A newly patented vacuo dry-glycation process promises much higher efficacy than the conjugation chemistry used currently, paving the way towards a much lower dosage cost. and its vaccine is a kind of polysaccharide-protein conjugate system. However, the process conditions required for activation of the polysaccharide by vacuo dry-glycation have not been optimized, which is linking with the properties such as molecular weight and oxidation ratio of activated polysaccharides and coupling ratio of conjugate products. This research addresses these deficiencies, enabling PnuVax Inc. to further the development of a more affordable vaccine that can be used in Canada and around the world to reduce childhood death due to pneumonia.

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Faculty Supervisor:

Robin Hutchinson

Student:

Partner:

PnuVax Inc (Kingston, ON)

Discipline:

Engineering

Sector:

Manufacturing

University:

Queen's University

Program:

Accelerate

Deep Unsupervised Anomaly Detection in Options Markets

In the last few years, a high increase in the interest of traders and investors towards financial instruments directly lead to an important augmentation of the information received daily by exchanges. Exchanges regulators, who constantly monitor markets to unveil potential infractions, traditionally perform their investigation manually and the notable growth in market activity represents an important risk of fraudulent events going unnoticed. In response to that new reality, exchanges around the globe are establishing automated surveillance systems that track markets activity. In this project, we set to design a new artificial intelligence algorithm that will detect anything in the Montreal exchange’s market that seems abnormal or fraudulent, so that analysts can focus on these alerts. Such a system could potentially detect fraudulent cases that are currently going unnoticed, while drastically reducing human costs and validation time.

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Faculty Supervisor:

Manuel Morales;Gilles Caporossi;Thomas Hurtut

Student:

Partner:

Bourse de Montréal

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

HEC Montréal; Polytechnique Montréal; Université de Montréal

Program:

Accelerate

AI for delivering product recommendation in retail consumer categories

E-commerce has evolved rapidly in recent decades resulted from globalization and international trade. The demand of online shopping is increasing every day, which has opened business opportunities to attract more costumers locally and globally. However, achieving satisfactory user experience in online shopping remains challenging compared to in-person walk-in shopping. Currently, customers have to input static text and images, or use webcam. Engaging interaction and automatic products recommendation is missing. In this project, we use eyeglasses as a use case to demonstrate how an AI-driven recommendation system can be designed and implemented for retail consumer categories. We will integrate image processing, computer vision and machine learning techniques to address the current issues of poor product recommendations. We will also create a basic deployment interface, enabling the trained model to integrate easily into a production environment.

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Faculty Supervisor:

Irene Cheng

Student:

Partner:

Eyevious Style

Discipline:

Computer science

Sector:

Professional, scientific and technical services; Retail trade

University:

University of Alberta

Program:

Accelerate

Using AI to generate mining algorithms

Hard-rock mining of uranium in Canada’s north is challenging and often difficult. Operating risk exposures are heightened when mining in high-grade uranium ore bodies that are exposed to possible flooding from water above the mine. To succeed in this environment Cameco has successfully mechanized their operations and relies on Jet Boring technology. This proposed project is planned to advance the visibility and automation systems for Jet Boring by: 1) creating large volumes of data created by ongoing measurement of the process; 2) collecting, analyzing, and synthesizing that data in order to form conclusions; and 3) confirming the effectiveness of using those conclusions to inform, control and direct operational decisions and automation of the mechanized Jet Boring systems. At this early stage, proof of concept level work will identify further opportunities that can point to or suggest a roadmap for further automation and optimization possibilities within mining.

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Faculty Supervisor:

Donna Beneteau;Terry Peckham;Douglas Milne;Cyril Coupal

Student:

Partner:

International Minerals Innovation Institute;Cameco Corporation (Saskatoon, SK)

Discipline:

Computer science

Sector:

Mining; Professional, scientific and technical services

University:

Saskatchewan Polytechnic; University of Saskatchewan

Program:

Accelerate

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Mitacs — Return on Innovation

Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.