Innovative Projects Realized

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

29670 Completed Projects

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801
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8841
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95
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568
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1088
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Projects by Category

Tool-path generation and optimization for robot machining using Artificial Intelligence

This research aims to generate and optimize optimal tool-path(process path of robot) by learning data on kinematics and dynamics properties required for the machining process using multi-axis robots with Artificial Intelligence(AI). Based on the end-effector of the robot, a new approach is proposed to optimize tool path in drilling or milling processes and to develop AI algorithms in terms of surface roughness, circularity, defects, and time. Recently, the manufacturing robot market has been gradually expanding and research has been actively conducted. Accurate prediction and process optimization are essential for the machining process. However, most industrial robots do not provide information about the robot’s dynamic parameters. For precision of machining requires data on dynamic properties that affect the result. Therefore, researches are being carried out to estimate dynamic properties. But, these methods take a long time and are very complex. Thus, if this research is carried out, it is expected that the outcomes of the development of new analysis methods for robot machining will be expected through the development of AI algorithms.

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Faculty Supervisor:

Jihyun Lee

Student:

Partner:

Ulsan National Institute of Science and Technology (UNIST)

Discipline:

Engineering

Sector:

Artificial Intelligence; Advanced Manufacturing; Other

University:

University of Calgary

Program:

Globalink Research Award

Modeling logistics for supply of bioenergy feedstock

The current version of the Integrated Biomass Supply Analysis (IBSAL) model is deterministic. As such, the uncertainty surrounding the results obtained cannot be studied or quantified. The focus of this research project is to incorporate uncertainty into the model in order to make it more realistic, provide additional insights into its properties and potentially suggest new types of solutions with corresponding margins of error and variability. The probabilistic version of the IBSAL model will allow users to test and explore more realistic sets of input assumptions and allow for the investigation of “what if” type of scenarios. The probabilistic version of the model will provide an opportunity to quantify spatial variability of economic, carbon and fuels costs in response to major driving variables such as crop residue yield and recovery rates.

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Faculty Supervisor:

Shahab Sokhansanj

Student:

Partner:

Nexterra Energy Corp;Agriculture and Agri-Food Canada;BioFuelNet

Discipline:

Engineering

Sector:

Utilities

University:

The University of British Columbia

Program:

Accelerate

Plasmon-enhanced fluorescence and sensing of novel magnesium and gold structures

This project will investigate the light-matter interaction of metallic nanoparticles, known as plasmonic, using experimental and theoretical approaches. The main objective is to provide experimental evidence on the ability of magnesium, a cheap, earth-abundant but yet unexploited for plasmonic applications metal, to enhance the optical response of fluorescent molecules. The effect of the different magnesium nanoparticle shapes and the magnesium oxide or polymer shell thickness on the fluorescent signal enhancement will be investigated using suitable spectroscopy techniques. The novel findings will cement the suitability of magnesium as a plasmonic material and will pave the road for its use in applications, especially for biological or medical sensing. Additionally, the morphology of complex gold nanoparticle assemblies, featuring a central nanoparticle of gold or different metal, will be investigated using computational techniques. Numerical results will provide a theoretical platform to guide the nanoparticle assembly in order to achieve maximum environmental sensitivity towards the design of efficient sensors.

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Faculty Supervisor:

Denis Boudreau;Anna Ritcey

Student:

Partner:

University of Cambridge

Discipline:

Physics

Sector:

Education

University:

Université Laval

Program:

Globalink Research Award

Use of floral phenology to estimate canola seed yield using satellite imagery

Canola is an important oilseed crop grown across Saskatchewan for its high quality oil. Canola has a very distinct reproductive stage due to its yellow color flowers. Therefore canola fields can be very clearly identified when observed through satellites with medium resolution. This leads to numerous potential applications, and one such application is in-season canola yield prediction. There are freely available satellite platforms which has a resolution of about 3-4m and therefore is useful in extracting valuable information such as flowering intensity. These traits can be quantified through remotely sensed images and can be used to develop models to predict yield. Predicting yield during the season helps managers, consultants and farmers to make important decisions regarding their produce.

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Faculty Supervisor:

Steve Shirtliffe

Student:

Partner:

CropPro Consulting

Discipline:

Life Sciences

Sector:

Agriculture

University:

University of Saskatchewan

Program:

Accelerate

S’orienter à l’enseignement supérieur : regard croisé Québec-France

Le système éducatif façonne les parcours scolaires des élèves, notamment à travers les modes d’organisation des écoles et les pratiques institutionnelles. Il participe à la (re)production des inégalités, mais peut aussi être un puissant outil de mobilité sociale ascendante. Si l’orientation est un sujet traité depuis un long moment en France et au Québec, peu de recherches y ont abordé les effets de récents changements structurels (p. ex., réformes, modifications législatives) sur cette question. C’est donc une analyse comparative Québec-France inédite que ce projet propose. Plus précisément, une étude ethnographique d’un lycée public dans la région de la Normandie en France sera réalisée. Ces données seront comparées à celles obtenues lors d’un projet connexe où une étude ethnographique d’une école secondaire publique de Montréal est effectuée. Ce projet vise à identifier dans les contextes français et québécois les pratiques d’orientation qui réduisent les inégalités sociales et scolaires et favorisent l’équité et la justice sociale.

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Faculty Supervisor:

Annie Pilote

Student:

Partner:

Université de Rouen Normandie

Discipline:

Sociology

Sector:

Education

University:

Université Laval

Program:

Globalink Research Award

La conception automatique des architectures de l’apprentissage profond (Deep Learning)

Le projet de recherche proposé vise à fournir un outil de classification automatique qui pourra être utilisé pour plusieurs applications de télédétection comme la cartographie de l’occupation des sols, ainsi qu’un élément d’aide à la décision pour la gestion des catastrophes naturelles et le suivi des cultures.
Dans le contexte de la classification des images de télédétection par réseau de neurones convolutionnel (CNN), la sélection des meilleurs variables (appelées hyperparamètres) qui déterminent la structure du CNN est un défi majeur lors de la construction de l’architecture de ce réseau. Il est donc important de trouver la combinaison optimale des hyperparamètres puisqu’ils contrôlent directement les performances du modèle CNN. Ainsi, nous visons l’utilisation des avantages du concept des algorithmes génétiques afin de trouver l’ensemble optimal des hyperparamètres pour les structures CNN qui ont une application dans les domaines agricoles et environnementaux.

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Faculty Supervisor:

Karem Chokmani

Student:

Partner:

Université de Carthage

Discipline:

Earth science

Sector:

Education

University:

Université du Québec : Institut national de la recherche scientifique

Program:

Globalink Research Award

Inhaled allergen challenge methodology: Assessment of SOLO Vibrating Mesh Nebulizer for Allergen-Induced Late Asthmatic Responses

The allergen inhalation challenge model is an important research technique for asthma drug development, and has been used for many years by the AllerGen CIC consortium of Canadian investigators in their evaluations of new asthma therapies. An alternate nebulizer is needed for performing inhaled allergen challenges, and this device needs to be tested for its ability to induce characteristic allergen-induced late asthmatic airway bronchoconstriction and associated inflammation. Additional novel non-invasive measures of airway inflammation for mechanistic outcomes will permit comprehensive understanding immune pathways affected by investigational medications. This study will compare the development of allergen-induced asthmatic responses (bronchoconstriction and inflammation) generated using a Solo® nebulizer to that of the Wright® nebulizer. This information is critically important because late asthmatic responses are the endpoint of most studies assessing the efficacy of investigational asthma medications, and will improve the efficiency and quality of Canadian asthma research and drug development.

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Faculty Supervisor:

Gail Gauvreau

Student:

Partner:

Allergy, Genes and Environment Network

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

McMaster University

Program:

Accelerate

Histoire de la conversion énergétique des déchets

Le stage à l’UQTR fait partie d’un projet postdoctoral sur l’histoire des techniques de conversion énergétique des déchets de 1970 à 2000. Il dresse une comparaison entre la France, les États-Unis et le Canada, par l’analyse des programmes de recherche et développement (R&D) ainsi que des dynamiques d’acteurs impliqués dans ces trois territoires. L’approche comparative permet d’identifier des tendances internationales et des spécificités territoriales. Le but est d’étudier les transformations sociales, technologiques et économiques des déchets et de l’énergie au croisement de la “crise des déchets” et de la “crise de l’énergie” dans les années 1970, dues à une consommation sans cesse croissante de matières et d’énergie.

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Faculty Supervisor:

Mahdi Khelfaoui

Student:

Partner:

Université de Nantes

Discipline:

Sociology

Sector:

Education

University:

Université du Québec à Trois-Rivières

Program:

Globalink Research Award

Deep learning based fault diagnostics for manufacturing systems considering data imbalance

Due to the recent 4th industrial revolution, manufacturing systems have become intelligent with the fusion of the industrial internet of things (IIoT) and cloud computing. To improve the reliability and availability of intelligent manufacturing systems, data-driven fault diagnostic methods have been paid much attention due to numerous data obtained from the manufacturing systems. Among data-driven approaches, deep learning based fault diagnostic methods have shown outstanding performance. However, the existing deep learning based approaches are limited in the case of an imbalanced dataset. In real-world manufacturing applications, data imbalance is commonly encountered because the manufacturing systems are mostly operated under healthy conditions. To solve this challenge, in this project, we develop a new deep learning model for fault diagnostics of manufacturing systems considering data imbalance problem. The developed model will be further validated using the dataset from the testbed and real-manufacturing system to realize the fault diagnostics for the intelligent manufacturing systems.

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Faculty Supervisor:

Chi-Guhn Lee

Student:

Partner:

Seoul National University

Discipline:

Engineering

Sector:

Education

University:

University of Toronto

Program:

Globalink Research Award

Targeted Incentive Offering for At-Risk Customers in an E-Commerce Setting

In this project, we focus on increasing sales in e-commerce shops by offering purchasing incentives to shoppers who are likely to leave without buying. More specifically, our goal is to predict which shoppers are likely to abandon their shopping cart and what can be done while they’re still on the site to customize their shopping experience and encourage them to buy (e.g. offering a discount). Our approach is based on analyzing the shopping experiences of various customers in many different retail stores to learn a statistical model of the customer shopping cycle. We investigate how machine learning techniques can be used to estimate the likelihood that a customer will abandon a purchase. For a customer who is likely to leave, we try to learn which of the available promotional actions are most probable to encourage the customer to finish the current purchase.

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Faculty Supervisor:

Joerg Sander

Student:

Partner:

Granify Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

Verres d’oxydes lourds et fibres pour l’infrarouge

Ce projet portant sur le développement de fibres optiques moyen-infrarouge pour l’amplification laser s’appuie sur des compositions de verres à base d’oxydes lourds dopées avec des ions luminescents tels que des terre-rares. En effet grâce à l’emploi d’oxydes lourds, l’énergie de phonons du verre est abaissé permettant une exaltation des phénomènes de luminescence rendant intrinsèquement plus efficace l’amplification laser. De plus, avec des températures de transition vitreuse plus élevées, le matériau supporte une plus forte intensité lumineuse d’excitation, améliorant d’autant l’amplification laser.
Ainsi, ce projet a pour ambition de développer ces nouvelles compositions vitreuses et à en obtenir des fibres optiques par étirage d’une préforme de verre réalisée par coulée. L’effort principal du projet sera mis sur l’identification des compositions d’oxydes lourds les plus intéressantes et sur les conditions expérimentales permettant d’obtenir des préformes viables pour un étirement en vue d’obtenir des fibres optiques.

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Faculty Supervisor:

Younès Messaddeq

Student:

Partner:

Université de Bordeaux

Discipline:

Physics

Sector:

Technology; Advanced Manufacturing

University:

Université Laval

Program:

Globalink Research Award

AI-driven evaluation of clinical electroencephalographic (EEG) recordings

The proposed project is part of a large-scale collaboration between SFU’s Behavioral and Cognitive Neuroscience Institute (BCNI) and Fraser Health Authority(FHA) in the domain of AI applied to clinical electroencephalographic (EEG) scans recorded and evaluated in the process of diagnostic workup in FHA’s public hospitals (n>40’000). The key goal of the SFU/FHA collaboration is to automate the process of EEG reporting by building a decision support system. Conventional review of EEG relies on neurologists to visually inspect complex, noisy, high-dimensional digital data. Such an approach is slow, not fully reliable, and suboptimal. There is considerable variability in EEG interpretation, and this variability is affected by specific reader characteristics. Given these challenges, clinical reporting of EEG is highly suitable for machine-driven automation. The focus of the proposed project at a smaller scale is on predicting diagnoses (codes for most responsible diagnosis, secondary diagnosis, etc, according to International Statistical Classification of Diseases ICD-10) from in-patient EEG scans. We aim to address the following question: what current deep learning approaches can be used to reach state-of-the-art performance for EEG classification based on diagnostic codes.

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Faculty Supervisor:

Vasily Vakorin

Student:

Partner:

Ukrainian Catholic University

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Artificial Intelligence; Biotechnology

University:

Simon Fraser University

Program:

Globalink Research Award

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