Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

31 915 projets complétés

3006
AB
5256
C.-B.
878
MB
703
NL
913
SK
9488
ON
9950
QC
98
PE
629
NB
1215
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Projets par catégorie

Participatory assessment of Aklak (grizzly bear) abundance and distribution in the Kivalliq Region, Nunavut

The objective of this project is to estimate grizzly bear abundance and distribution in the Kivalliq region of Nunavut by combining Inuit traditional knowledge about grizzly bears with genetic data already collected by the Government of Nunavut. Working with the communities of Arviat and Baker Lake, we will use both pre-existing interview recordings and new interview data collected by trained local interviewers so that no researcher from down south needs to visit Nunavut during the pandemic.

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Superviseur du corps professoral :

Douglas Clark

Étudiant :

Partenaire :

Churchill Northern Studies Centre

Discipline :

Life Sciences

Secteur :

Education; Professional, scientific and technical services

Université :

University of Saskatchewan

Programme :

Accelerate

Innovation des services

Il s’agit d’identifier les besoins potentiels de services à proposer afin d’accroître son marché et son positionnenemt. Elle permettra à l’entreprise de décider d’engager ou pas le développement d’un pan de ses activités au Canada, à court terme. Cette étude est aussi une opportunité d’utilisation des outils de gestion de connaissance afin de mieux s’imprégner de l’évolution des demandes du marché, et de proposer des approches pour y faire face.

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Superviseur du corps professoral :

Mickaël Gardoni

Étudiant :

Partenaire :

Schneider Electric of Canada (St. Pointe-Claire)

Discipline :

Engineering

Secteur :

Manufacturing

Université :

École de technologie supérieure

Programme :

Accelerate

Diagnostic de capacités organisationnelles d’apprentissage en contexte de crise

En situation de crise ou haute turbulence, les organisations doivent pouvoir réagir rapidement aux bouleversements provenant de l’environnement interne et externe ayant un impact sur les pratiques et routines habituelles, tout en maintenant leurs activités quotidiennes. Afin de répondre à des situations critiques inédites, l’organisation de ces réponses doit pouvoir se faire selon des cycles d’apprentissage accélérés au sein des équipes : absorption de connaissances externes (inhabituelles), partage de ces connaissances au sein des équipes, capacité d’expérimenter de nouvelles pratiques, pollinisation de nouvelles connaissances et transfert d’apprentissages entre équipes. Or, cette vitesse d’apprentissage dépend de plusieurs facteurs à la fois associés aux capacités d’apprentissage en temps normal dépendamment entre autres de la culture organisationnelle, à la fois de l’interaction des capacités existantes avec la situation critique inédite qui est productrice de stress sans précédent.
Au moyen d’une méthodologie quantitative basée sur l’analyse de réponses à des questionnaires, notre recherche s’intéresse donc à identifier plus précisément les facteurs ayant une influence sur les capacités d’apprentissage et d’adaptation au sein des équipes en contexte de crise.

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Superviseur du corps professoral :

Kevin J Johnson

Étudiant :

Partenaire :

Fondation CHUM

Discipline :

Business

Secteur :

Health and Related Sciences & Technology

Université :

HEC Montréal

Programme :

Accelerate

Application of a DNN model for seismic performance prediction of structures retrofitted with steel dampers

The intensity and frequency of earthquakes in Korea have increased in the past few years. Thus, the need for seismic retrofit of many middle- or low-rise buildings has increased which were designed without seismic design provisions. Nonlinear time history analyses need to be performed for accurate seismic performance evaluation and for appropriate retrofit of structures. The NLTH analyses, however, requires significant computational time and modelling efforts. It is possible to predict the nonlinear response of structures subjected to an earthquake by constructing a database of the seismic response of structures equipped with various dampers. The database can be used to train a deep learning algorithm. In this study, a deep learning algorithm that can predict the seismic response of a retrofitted structure will be proposed based on the results of a large number of numerical analyses of structures equipped with steel dampers.

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Superviseur du corps professoral :

Oh-Sung Kwon

Étudiant :

Partenaire :

Kyungpook National University

Discipline :

Engineering

Secteur :

Construction; Environmental Science and Technology; Sustainability & the Environment

Université :

University of Toronto

Programme :

Globalink Research Award

3D Brain lesion detection from MRI images

This project aims to detect 3D brain lesions automatically using machine learning and MRI images. Given an MRI image of a brain, the project will automatically detect any size lesion. The project will establish a baseline, as well as improvement over the baseline for which performance metrics will be validated. The partner organization benefits from this by advancing their own research to better help partnered radio-oncologists in their brain lesion segmentation tasks. It is a time consuming and precise task which could be supported using state-of-the-art deep learning methods.

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Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

AFX Medical Inc.

Discipline :

Computer science

Secteur :

Information and cultural industries

Université :

Université de Montréal

Programme :

Accelerate

Development of a thermal spray wear-resistant coating for abrasive concrete applications

Mechanical wear and chemical corrosion are the two main dominant factors that limit the service life of industrial machinery. Wear and tear of tools and equipment are significantly intensifying due to the increasing demand for superior efficiency, productivity, and throughput of industrial apparatuses. Since the surface of the materials is more exposed to abrasive wear and erosion, surface protection is considered an effective and economic approach to improve the service life of machinery components. Currently, thermally sprayed WC-based cermet coatings are the most widely used wear-resistant cermet materials to protect various metallic components. In this research, the deposition of potential wear-resistant thermal sprayed coatings (WC-17 %wt. Co and WC-10% wt. Ni) on steel molds is investigated using microstructural studies as well as microhardness and wear tests. This study aims to develop a wear resistance coating which is also tough and ductile enough to increase the service life and reduce the disposal of molds, used in the manufacturing of concrete products. The partner organization, Besser Proneq, will benefit from the research by acquiring the knowledge to produce molds and wear liners with improved service life and durability.

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Superviseur du corps professoral :

Christian Moreau;Pantcho Stoyanov

Étudiant :

Partenaire :

Besser Proneq

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Concordia University

Programme :

Accelerate

Development of an online tool for crowd movement simulation research

Crowds are everywhere and studying them is important for urban planning, transportation, evacuation, and safety at large public events. Simulation modelling lets us study crowd behaviour by simulating large numbers of individual pedestrians and seeing how they behave as a crowd in different, sometimes dangerous scenarios. Simulation allows us to study situations that would be impossible with live experiments. There are already many models covering some or all parts of pedestrian movement and crowd behaviour, but there are only a few open-source platforms using them for crowd simulation modelling. Most of those that exist require good knowledge of programming to use, and none are web-based. This project will produce an accessible, online, and open-source crowd simulation platform that can benefit researchers and professionals alike. The project will include the calibration and validation of this model, ensuring a certain quality of simulation, and will also involve a case study using the platform to simulate emergency evacuation inside a public transport station.

Voir la description complète du projet
Superviseur du corps professoral :

Liliana Perez

Étudiant :

Partenaire :

SYSTRA Canada Inc

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Accelerate

Achieving Circular Wastewater Management with Machine Learning

Effective wastewater treatment is essential to the health of the environment and municipal wastewater treatment plants in Canada are required to achieve specific effluent water quality goals to minimize the impact of human generated wastewater on the surrounding environment. Most wastewater treatment plants include a combination of physical, chemical, and biological unit processes and therefore have several energy inputs to drive mixing, maintain ideal temperatures, and move water from one unit process to the next. Methane and other gases (biogas) and biosolids are generated during wastewater treatment. Both of these can be captured and repurposed for use within and outside of the wastewater treatment plant and can in some cases even be converted to revenue streams. Thus, biogas and biosolids are considered recoverable resources rather than waste products. Circular wastewater management (CWM) is an emerging approach that aims to optimize wastewater treatment, energy usage, and resource recovery. To achieve CWM, the operators of wastewater treatment plants must have a thorough understanding and reliable control of the different elements of the system. This is usually achieved using a combination of operator expertise, online sensors, and offline water quality measurements coupled with data collection, storage, and analysis software. TOBECONT’

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Superviseur du corps professoral :

Stephanie Gora

Étudiant :

Partenaire :

Ontario Clean Water Agency

Discipline :

Engineering

Secteur :

Construction and infrastructure; Utilities

Université :

York University

Programme :

Accelerate

Futures First Algorithmic Derivatives Trading

This internship research project is aimed at using state-of-the-art machine learning and AI techniques to create profitable trading strategies in the derivatives markets. This research is key to the business operations of the partner organization and gives interns invaluable experience and insights to the business.

Voir la description complète du projet
Superviseur du corps professoral :

Manuel Morales;Christian Dorion;Anatoliy Swishchuk;Manuel Morales

Étudiant :

Partenaire :

Futures First Canada Inc

Discipline :

Mathematics

Secteur :

Finance and Insurance

Université :

HEC Montréal; Université de Montréal; University of Calgary

Programme :

Accelerate

CONVERGENT CROSS MAPPING FOR DEMAND FORECASTING

The availability of inexspensive electricity in a constant and reliable fashion is critical to economic development and efficient resource consumption. To this end, accurate short term load forecasting (STLF) on an electrical grid enable the minimization of dispatch and running costs on the scale of seconds to a week. Models and approaches employed in STLF include multi-linear regression, Box-Jenkins Analysis, fuzzy systems, non-linear state space reconstruction (SSR), and various hybrid models. The domain of this project lies in pure and potentially hybrid non-linear state space methods where SSR have already been explored (5,7). Unexplored in this domain is a method called convergent cross mapping (CCM). The thrust of CCM is a potentially novel approach to making inferences regarding the causal drivers of a time series variable. The primary goal of this project is: use CCM to identify predictors of power grid demand and determine whether or not such predictors improve upon current demand forecast methods.

Voir la description complète du projet
Superviseur du corps professoral :

Simon Bonner

Étudiant :

Partenaire :

University of California, San Diego

Discipline :

Mathematics

Secteur :

Education

Université :

The University of Western Ontario

Programme :

Globalink Research Award

Modelling a 5G mmWave Cell Site to evaluate RF exposure

This project will produce a useful simulation of a 5G mmWave Cell site. This simulation will allow the Industrial Partner to evaluate and optimize the operation of a typical 5G Cell site. The simulation will model a 5G phased array antenna connected to a Base Station which will be able to create focused beam radiation patterns that will track simulated users moving through the cell site. The effectiveness of user tracking with beams can thus be studied. The simulation will also allow an evaluation of the RF exposure of the users within the cell site. It is critically important to the Canadian Economy that 5G Communication Systems are quickly adopted because 5G will be a key enabler of the new connected economy.

Voir la description complète du projet
Superviseur du corps professoral :

Raman Paranjape

Étudiant :

Partenaire :

Saskatchewan Telecommunications

Discipline :

Engineering

Secteur :

Information and cultural industries

Université :

University of Regina

Programme :

Accelerate

Occurrence of the Timiskaming-type sedimentary rocks in the North Caribou Greenstone belt

Jason Duff, who is completing MSc program at the University of Ottawa, recently found evidence for sedimentary rocks in the North Caribou greenstone belt that are 2680 million years old, younger than other rocks in the belt. The rocks elsewhere in the belt contain 3.0 billion year sedimentary and volcanic rocks including the host rocks for the Musselwhite mine. Similar young sedimentary rocks, called Timiskaming-type rocks, are known to be spatially associated with large gold deposits elsewhere in the Archean Superior Province (which forms a large part of “Canadian Shield”), including the Porcupine, Kirkland Lake, Larder Lake, Malartic, Pickle Lake, and Hemlo gold camps. The proposed study will determine the distribution of this young sedimentary rock unit in the belt and evaluate the source of the sedimentary rocks; outcrops in the area will be examined, and rock types identified and their ages determined by isotopic methods.

Voir la description complète du projet
Superviseur du corps professoral :

Keiko Hattori

Étudiant :

Partenaire :

GoldCorp (Mussel White Mines)

Discipline :

Earth science

Secteur :

Mining

Université :

University of Ottawa

Programme :

Accelerate

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Mitacs — Retour sur innovation

Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.