Projets novateurs réalisés

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

31 620 projets complétés

2978
AB
5221
C.-B.
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projets par catégorie

Effects of Partial Shading on Bifacial Photovoltaic Modules

This research project will analyze the effects of partial shading, which photovoltaic modules are subject to. The objective of this research is to elucidate the mechanisms of degradation of partial shading. Investigations in photovoltaic systems will involve the collection and analysis of production data. Measurements of the individual I-V curve of the modules of a tracker will be performed. Through these results it will be possible to identify if any module will present degradation and eventually correlate them with its location. In conclusion, it is hoped that this research project will enable the exchange of knowledge and experiences between research groups and, ultimately, contribute to research and innovation in the photovoltaic sector.

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Superviseur du corps professoral :

Maxime Darnon

Étudiant :

Partenaire :

Universidade Tecnológica Federal do Paraná

Discipline :

Engineering

Secteur :

Education

Université :

Université de Sherbrooke

Programme :

Globalink Research Award

AR Marine Visualization System

A sailor needs to obtain real-time information about the status of the sailboat and also derive an accurate approximation of the behaviour of the surrounding environment, in order to safely and successfully sail a boat. We also aim to investigate how providing vital knowledge using an immersive approach will affect the cognitive load on the sailor, thus potentially enhancing efficiency and safety at sea.

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Superviseur du corps professoral :

Bruce Kapron

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Computer science

Secteur :

Automotive; Technology; Water

Université :

University of Victoria

Programme :

Accelerate

Data Markets for Blockchain-based Federated Learning in Health Care

Artificial intelligence (AI) is a promising technology for patient-centric health care, for example, to diagnose diseases, or to recommend therapies. However, deploying AI in health care is challenging. The creation of AI systems typically requires large amounts of data, but health data is kept private due to high privacy requirements. Furthermore, AI in health care should not only benefit majorities in society but also benefit underrepresented groups. Toward this end, this research project aims to combine federated learning with blockchain to develop responsible AI for health care. Federated learning enables the creation of AI systems without disclosing private data. A blockchain infrastructure enables a transparent and fair information system. Furthermore, a blockchain enables a data marketplace through a token economy, where individuals are financially incentivized for sharing unique data for the creation of AI systems. The goal of the project is to demonstrate a practical implementation for responsible AI in health care, and its benefits. Ultimately, it can transform the Canadian health care system.

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Superviseur du corps professoral :

Ivan Beschastnikh

Étudiant :

Partenaire :

Karlsruher Institut für Technologie

Discipline :

Computer science

Secteur :

Education

Université :

The University of British Columbia

Programme :

Globalink Research Award

Machine Learning for Turbulence Modelling

Machine learning has revolutionized a variety of fields in the past decade, due to increasing availability of data and processing power. Engineering simulations of most industrially relevant fluid flows (e.g aircraft design and turbomachinery) require modelling of turbulent fluctuations in the flow. For the turbulence modelling community, which has seen widespread stagnation, machine learning offers a clear path to improve model accuracy, estimate uncertainty, and develop new data driven models. While the potential for machine learning in the field of turbulence modelling is clear, the number of thorough investigations is limited. This project involves a detailed investigation into improvements and testing of a neural network architecture for turbulence modelling, and implementation of this method in a practical engineering simulation setting. The project represents a critical leap forward in revolutionizing the field of turbulence modelling augmentation by machine learning.

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Superviseur du corps professoral :

Fue-Sang Lien

Étudiant :

Partenaire :

University of Manchester

Discipline :

Engineering

Secteur :

Education

Université :

University of Waterloo

Programme :

Globalink Research Award

Automatic Species Identification in Underwater Environments

Knowledge of the geographic distribution and identification of species is essential for the conservation of biodiversity. With advances in technology and greater accessibility of equipment capable of recording underwater, it was possible to obtain data efficiently. However, it leads to an immense volume of information collected, which requires exhaustive manual processing that requires label, time and money. That is why the creation of tools capable of assisting in the monitoring of these species is so important. In this project, we propose to fill a gap in the literature on the development of species monitoring systems in underwater environments. The proposed approach aims not only to identify the existing species in the training set, but also to be able to identify new species that can be registered in that environment. We intend to explore several methodologies using clustering, dissimilarity and convolutional neural networks. We will also present a new set of public data from high resolution underwater videos that will be available to the scientific community.

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Superviseur du corps professoral :

Alessandro Lameiras Koerich

Étudiant :

Partenaire :

Federal University of Parana

Discipline :

Computer science

Secteur :

Artificial Intelligence; Ocean Tech; Life Sciences (not health)

Université :

École de technologie supérieure

Programme :

Globalink Research Award

Enhancing climate change resilience in viticulture using information in long-term records

Winegrape phenology (the timing of seasonal events – leafout, flowering, harvest) has traditionally been an important tool for winegrowers to plan vineyard management. With warming, however, winegrape phenology has advanced significantly, impacting the type and quality of wine different vineyards can produce. How large the impact of climate change has been, and will be, depends on several factors including which winegrape varieties (or cultivars, such as Pinot noir or Syrah) a vineyard has planted. Shifts in phenology could change the suitability of varieties currently planted, but we have data on only a few widely grown varieties. We propose to use long-term records from the Unité Expérimentale du Domaine de Vassal, a French research vineyard, to examine how climate change affects winegrape phenology of roughly 200 varieties. This unique diversity of varieties will allow us to include typically understudied varieties in our research and help develop tools and information for winegrowers to adapt their vineyards to climate change.

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Superviseur du corps professoral :

Elizabeth Wolkovich

Étudiant :

Partenaire :

INRAE Occitanie-Montpellier Research Centre

Discipline :

Life Sciences

Secteur :

Agriculture and Food; Sustainability & the Environment; Life Sciences (not health)

Université :

The University of British Columbia

Programme :

Globalink Research Award

Potential of pyrolysis oil from biomass as a source for phenolic resins

Phenolic resin, also known as phenolic formaldehyde resin (PF), is a synthetic resin produced from the polymerization of phenol (an aromatic alcohol derived from benzene) and formaldehyde (a reactive gas derived from methane). Resins have applications from use as laminates to in construction materials such as wood and composite materials. The use of petroleum-based compounds as sources of phenols is limited by the climate change and other environmental impacts associated with petroleum derived products. As such there are increased efforts to use renewable resources for the production of useful polymers or composites. As the only renewable source of fixed carbon biomass is the primary candidate for the production of composites. Converting solid biomass (saw dust, corn stover, wheat straw etc.) to a liquid makes extraction of phenols less intensive from a process perspective. The overall objective of this work is to identify and quantify (where feasible) key compounds in py-oil (derived from wheat) and lignin (extracted using an organosolv process), that could serve as a feedstock for phenolic resins to replace petroleum sources.

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Superviseur du corps professoral :

Kelly Hawboldt

Étudiant :

Partenaire :

Karlsruher Institut für Technologie

Discipline :

Engineering

Secteur :

Education

Université :

Memorial University of Newfoundland

Programme :

Globalink Research Award

Utilization of the exhaust gas of the smelting process for cooling and heating applications

Aluminum smelting is a highly energy consuming industrial process. The process generates a large amount of the heat that leaves through the exhaust gas. The exhaust gas must be scrubbed of its contaminants before release to the atmosphere at the gas treatment unit exit. The scrubbing process is more effective if this gas is cooled before entering the gas treatment unit. The main objective of this project is to find a technical and economical method to cool the smelting process exhaust gas upstream of the gas treatment unit. An ejector heat driven system is proposed for cooling the exhaust gas by heat recovery, and for improving the overall plant efficiency.

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Superviseur du corps professoral :

Mikhail Sorin

Étudiant :

Partenaire :

Rio Tinto Alcan (Jonquière, QC)

Discipline :

Engineering

Secteur :

Manufacturing; Mining; Professional, scientific and technical services

Université :

Université de Sherbrooke

Programme :

Accelerate

Determining Properties of Agricultural Straw forEquipment Design and as Feedstock for theBiofuel Industry

Information on the physical and mechanical properties of the wheat stem could be greatly helpful in
effective harvesting of the crop biomass as well as feedstock preparation (post-harvest processing)
for the biofuel industry. Appropriate design of the harvesting systems or post-harvest processing
equipment which is energy efficient depends on the access to physical properties and strength as well
as lignocelluosic material characteristics. In the proposed project, experiments will be conducted
based upon independent variables such as crop genotype, moisture content, type of cutting knife, and
loading rate and the bending and shearing properties and other mechanical and physical properties of
straw will be measured. Finally, dependent variables will be analyzed using regression modeling to
find out and express the importance of abovementioned factors on energy requirement of the
equipment.

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Superviseur du corps professoral :

Lope Tabil

Étudiant :

Partenaire :

CNH Industrial;University of Saskatchewan

Discipline :

Engineering

Secteur :

Other

Université :

University of Saskatchewan

Programme :

Accelerate

Machine-Learning for design and discovery of next generation CO2 electrocatalysts

Mitigation of CO2 emissions in conjunction with the implementation of renewable energy generation and storage are widely recognized among the most pressing technological challenges of the twenty-first century that aim to address runaway climate scenarios. The UBC team in collaboration with its industry partner (AGORA Energy Inc) has introduced the concept of CO2-to-energy via its unparalleled and proprietary CO2 Redox Flow Battery (CRB) technology. There is an ongoing collaboration among UBC and IEK-13 (Julich) to accelerate the commercialization and large-scale deployment of the CRB. The visiting student will participate to ongoing data-driven tasks related to materials discovery for new electrocatalysts based on Metal Organic Framework (MOF) where Artificial Intelligence (AI) models based on data analytics and machine learning are utilized. The collected data and ML-based modelings will be complemented by high-throughput electrochemical characterization methods for rapid screening of advanced catalysts, enhancing system design and optimizing operating conditions at UBC and AGORA.

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Superviseur du corps professoral :

Elod Lajos Gyenge

Étudiant :

Partenaire :

Forschungszentrum Jülich

Discipline :

Engineering

Secteur :

Green/Alternative Energy; Artificial Intelligence; Information and Communications Technology

Université :

The University of British Columbia

Programme :

Globalink Research Award

High-Precision Imitation Learning for Real-Time Robotic Control

In recent years, an increase in industrial robots in manufacturing has emerged. However, there are still possible safety issues and difficulty in specifying tasks for the robots to perform. The objective of this research project is to make a path planning system that uses demonstrations of how to perform a task to learn how to perform the task using techniques from the field of machine learning. These demonstrations will also show the robot how to move in the workspace safely and without entering collision with items in its surroundings. This system aims to be integrated into Mecademic’s Meca500, which will make the robot more user-friendly, safer and more accessible to people unfamiliar with industrial robotics.

Voir la description complète du projet
Superviseur du corps professoral :

Hsiu-Chin Lin

Étudiant :

Partenaire :

Mecademic

Discipline :

Engineering

Secteur :

Manufacturing

Université :

McGill University

Programme :

Accelerate

Identification of Polymorphic Malware using Fractal Complexity Analysis

In the proposed collaborative research, the focus will be on the application of fractal complexity analysis in anomaly detection. Many features are hidden deep within time series information such as network traffic, and complexity analysis will facilitate the extraction of such features. Complexity analysis takes advantage of the self-similar structure which is found widely in nature and which has been shown to be evident in network traffic. These features will be used for the presence of Malware which has compromised a host system. The features will also be used to detect incoming denial of service attacks or other resource crippling behavior which would indicate intrusion or the attempted disruption of normal operations. This research will improve the ongoing research aims of the intern by including complexity analysis as a tool towards the detection of obfuscated forms of Malware. This work will also produce a deliverable in the form of a manuscript which will summarize the work carried out and the core findings.

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Superviseur du corps professoral :

Ken Ferens

Étudiant :

Partenaire :

National Institute of Informatics

Discipline :

Engineering

Secteur :

Education

Université :

University of Manitoba

Programme :

Globalink Research Award

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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.