Innovative Projects Realized

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

29670 Completed Projects

2811
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4990
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801
MB
663
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825
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8841
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9197
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95
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568
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1088
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Projects by Category

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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Faculty Supervisor:

Mickaël Gardoni

Student:

Partner:

Schneider Electric of Canada (St. Pointe-Claire)

Discipline:

Engineering

Sector:

Manufacturing

University:

École de technologie supérieure

Program:

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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Faculty Supervisor:

Kevin J Johnson

Student:

Partner:

Fondation CHUM

Discipline:

Business

Sector:

Health and Related Sciences & Technology

University:

HEC Montréal

Program:

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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Faculty Supervisor:

Oh-Sung Kwon

Student:

Partner:

Kyungpook National University

Discipline:

Engineering

Sector:

Construction; Environmental Science and Technology; Sustainability & the Environment

University:

University of Toronto

Program:

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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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

AFX Medical Inc.

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Université de Montréal

Program:

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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Faculty Supervisor:

Christian Moreau;Pantcho Stoyanov

Student:

Partner:

Besser Proneq

Discipline:

Engineering

Sector:

Manufacturing

University:

Concordia University

Program:

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.

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Faculty Supervisor:

Liliana Perez

Student:

Partner:

SYSTRA Canada Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

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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Faculty Supervisor:

Stephanie Gora

Student:

Partner:

Ontario Clean Water Agency

Discipline:

Engineering

Sector:

Construction and infrastructure; Utilities

University:

York University

Program:

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.

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Faculty Supervisor:

Manuel Morales;Christian Dorion;Anatoliy Swishchuk;Manuel Morales

Student:

Partner:

Futures First Canada Inc

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

HEC Montréal; Université de Montréal; University of Calgary

Program:

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.

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Faculty Supervisor:

Simon Bonner

Student:

Partner:

University of California, San Diego

Discipline:

Mathematics

Sector:

Education

University:

The University of Western Ontario

Program:

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.

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Faculty Supervisor:

Raman Paranjape

Student:

Partner:

Saskatchewan Telecommunications

Discipline:

Engineering

Sector:

Information and cultural industries

University:

University of Regina

Program:

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.

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Faculty Supervisor:

Keiko Hattori

Student:

Partner:

GoldCorp (Mussel White Mines)

Discipline:

Earth science

Sector:

Mining

University:

University of Ottawa

Program:

Accelerate

Development of bio-physical communication model predicting potential toxicity of polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons (PAHs) are environmental pollutants that occur in various chemical forms across the terrestrial and marine environments and have long been of the subject of intense research worldwide due to their highly toxic properties. PAHs are uptaken by bio-organisms and activated to electrophilic metabolites to exert their toxic effects. Meanwhile, different toxicity of PAHs with similar structure and molecular weight could not been accurately explained by current in silico models. Using PAH target materials, we will develop bio-physical communication model based on two hypotheses: a) biological activity of a toxicant with the receptor ligand in bio-organism is influenced by electron-mediated reaction and b) when different toxicants with the same concentration react with the same receptor, their biological activity is solely dependent to the material’s physico-chemical properties. New toxicity prediction model would help us understand biological response against toxicant’s physico-chemical properties characterized using synchrotron-radiation X-ray spectroscopy. Upon successful development, this model will serve as an absolute toxicity assessment tool for screening chemicals and evaluating the safety of pharmaceutical products rapidly, which will result in significant abatement of traditional animal testing.

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Faculty Supervisor:

Gap Soo Chang

Student:

Partner:

Seoul National University

Discipline:

Physics

Sector:

Education

University:

University of Saskatchewan

Program:

Globalink Research Award

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