Projets novateurs réalisés

Modèle optimisé de planification de la stratégie opérationnelle pour une communauté intelligente à émissions nettes zéro

Due to some complex technical problems, both power plant’s generators and local grid transmission lines do not have the potential to generate and transmit enough electricity to cover and meet all load demand during peak hours. Based on this technical difficulty, there is no solution for customers to use costly energy during peak hours and accordingly pay more money for their consumed electricity(Refer to electrical pricing scheme).In this case, in order to remedy above mentioned problem, smart solar communities as grid-friendly consumers, would be established with the purpose of supporting the local grids. This feature would be more important when local grids are in peak period hours. To this end, solar communities should shift their internal electric loads from peak load period to off-peak hours. Regardless of shifting the loads, they should either decrease overall amount of energy consumption or used dynamic load shedding/shaving methods. In this case, for managing load demand during peak hours, a community-scale optimal strategy planning model is necessary. Base on community-scale dynamic complexity and uncertainty, this model should be constructed based on inexact optimization techniques such as Interval Parameter linear Programming, (Mixed) Integer Linear Programming, Fuzzy Linear Programming or integration of above methods.

How Wildlife Use Existing Crossing Structures along Roads in the Laurentians: Light at the End of the Tunnel?

Roads and traffic subdivide wildlife habitats and increase the mortality of wild animals that try to cross due to collisions with vehicles. Wildlife crossing structures are costly, but an alternative may exist in structures that are already present under roads such as water culverts. It is unknown how often wildlife use such structures in the Laurentians. This project will install cameras on many of these structures and track-boxes near them to identify the species that use the structures and quantify their frequency of use. Statistical analysis of the data will help Éco-corridors laurentiens evaluate what future measures should be put in place to further reduce the negative impacts of roads on wildlife. This is very important in the Laurentians, as the road and highway being evaluated pose a threat to wildlife frequenting National Parks in the region.

Design of Tunable Surface Coatings for Magnetic Nanoparticles for the Adsorption and Removal of Organic Contaminants

This project entails the design, synthesis, and characterization of porous surface coatings made from metal–organic frameworks for magnetic nanoparticles. The combination of these two materials will allow for the removal of organic contaminants that currently interfere with the correct functioning of DNA and RNA extraction test kits (e.g., COVID-19 test kits). As developers and producers of such kits, the partner organization (Galenvs Sciences Inc) will benefit from this novel joint research approach by improving the quality and function of these kits. In addition, Galenvs will enhance the size of their materials research portfolio for incorporation into future products.

Efficient, Edge-based Video Streaming for Video Action Recognition

Streaming video applications in the home (video doorbells, security cameras, babycams, robot vacuums etc.) have started to become commonplace. Automatically detecting events of interest in such video streams can help to reduce manual effort from users in sifting through false alarms, enhance user safety, comfort and satisfaction, as well as enable new applications in areas such as safety and wellness. This project focuses on systematically investigating the performance of action recognition models under resource constraints found in practical deployment scenarios such as variable and metered network bandwidth, limited compute, etc. as well as develop technologies to overcome these challenges.

Équité algorithmique; impacts et changements de mesures

Présentement, les modèles de tarification en assurance auto calculent les primes chargées aux assurés en tentant de prédire leur risque d’assurance grâce à plusieurs critères (âge, sexe, état-civil, expérience de conduite, caractéristiques du véhicule, etc.). Le problème auquel ce projet s’adresse est celui du point de vue éthique d’une telle approche. En effet, l’usage de certaines variables peut être vu comme discriminatoire envers certains groupes de la société. Un des exemples courants est que les femmes présentent généralement un risque d’assurance moins élevé et paient ainsi des primes d’assurance plus basses. Cette pratique est généralement acceptée socialement et permise par la législation canadienne, mais il pourrait y avoir d’autres situations similaires concernant d’autres variables sensibles. Le but général du projet sera de déceler ces situations, de déterminer comment les améliorer si nécessaire et d’évaluer l’impact potentiel d’antisélection que les modifications auront au global sur les portefeuilles d’assurances concernés.

Ceci est avantageux pour l’organisme partenaire dans la mesure où il souhaite s’assurer que ses pratiques de tarification soient alignés aux valeurs de l’entreprise.

Predicting Equipment Failure in Canadian Mines

In order to reach the production targets, mining operations typically need to operate without a stop. Equipment maintenance planning plays an important role in ensuring the required number of mining haul trucks are available at any given time. If not planned correctly, corrective maintenance would be required, which could slow down or halt the production as well as potentially costing higher in maintenance. In this project, reliability analysis and predictive maintenance for turbocharger and transmission failures will be proposed using physics-based, data-based, machine learning based and hybrid approaches, using the data collected by the sensors on mining haul trucks. The factors that increase the fuel consumption will also be studied. Finally, anomaly detection model will be developed to detect sudden sensor reading changes and failures for the cases to alert the driver or an operator.

Variations du microbiome des baies et des feuilles de vignes soumises à différents changements de température au champ

Au Canada, les études explorant la relation entre la vigne et son microbiome sont encore peu nombreuses, surtout
sur les cépages hybrides cultivés dans l’Est du Canada. Ces cépages, issus de croisements entre l’espèce Vitis vinifera et des espèces Vitis indigènes à l’Amérique du Nord (V. labrusca, V. riparia), ont une biochimie particulière, qui leur permet une forte résistance au froid et une maturité hâtive, mais qui affecte aussi la biochimie des baies.1,15
Les modèles de changements climatiques suggèrent que l’Est du Canada connaîtra des augmentations significatives des températures dans un avenir proche.16,17 Ces augmentations auront un impact global sur l’agriculture canadienne, notamment dans le secteur de la viticulture. Si l’on peut s’attendre à un développement plus hâtif et/ou plus abouti des baies, la température affectera également la microflore des vignobles, avec un impact certain sur l’incidence des maladies, mais aussi sur la qualité des vins produits. Dans le cadre de ce projet, nous caractériserons le microbiome fongique et bactérien du cépage hybride l’Acadie blanc cultivé dans l’Est du Canada puis nous évaluerons sa résilience face à différents traitements de température mimant les changements climatiques.

Portfolio Optimizer

Portfolio managers evaluate stocks to find those with the best expected returns, but they need risk measures to complete their decision-making process.
The project has the objective to give adequate risk metrics to portfolio managers for a given portfolio and a list of potential candidates.

Historical price values are useful to determine risk, but they could be better optimized to reflect the uncertainty given that historical values may not reflect future values.

The model which uses reinforcement learning should give portfolio managers better insights on their risk when they add a new stock to their portfolio.

The goal of the partner organization is to improve its knowledge on the matter as well as offering free access to the research made by the intern.

Expanding the analytical capacity of Nanopore sequencing for infectious disease surveillance and diagnostics in a One Health framework

The alarming increase in the scale at which infectious diseases spread and devastate global socioeconomics necessitates timely characterization of the disease-causing agent to devise effective intervention strategies to stop the spread of disease at the source of introduction. It is known that domestic and wild animals play significant roles in the transmission of pathogens to humans via direct contact or the ingestion of contaminated foods. Hence, the proposed project will partner with an animal health diagnostic lab, Prairie Diagnostic Services Inc. (PDS), to develop laboratory and computational solutions to expedite decision making in clinical and surveillance settings. The tools and protocols will replace laborious conventional workflows with long turnaround times and streamline the process from pathogen sequencing data generation to result interpretation. The generated genetic information of the pathogens from the project will subsequently be used to identify genetic changes associated with increased livestock mortality rates and disease prevalence. The collective work will provide PDS with the analytical infrastructure to offer accredited rapid sequencing services for Canadian researchers and agriculture whom will benefit from the assurance of food safety and livestock productivity.

Building relevant datasets and performance indicators to drive Canadian nature-based solutions (NbS) to achieve 30×30 and climate targets while promoting synergy between Indigenous and non-Indigenous communities

If Canada is to achieve its 30×30 targets (protecting 30% of land and water by 2030), a combination of emissions
reductions and innovative carbon sequestration solutions are needed. Further, these solutions must achieve multiple
benefits for health and community resilience given that adverse health and climate change impacts are predicted to
disproportionately impact vulnerable communities. Nature-based solutions (NbS) [Cohen-Shacham et al., 2016; are
defined as interventions to protect, manage, and restore landscapes that store carbon and ultimately benefit human
well-being. While NbS can contribute to multiple benefits (e.g., climate resilience, conservation of cultural resources,
carbon mitigation, water security, and biodiversity preservation), pitfalls do exist. It has been questioned whether NbS
is equally accessible and beneficial for all (Kaufmann, et al., 2021) as some interventions (e.g. protected areas, forest
plantations) have the potential to negatively impact Indigenous Peoples and Local Communities (IPLC) through
displacement, livelihood restrictions, and cultural impacts (Townsend, Moola & Craig, 2020). The federal government
is increasingly exploring the concept of NbS but there is no current policy governing how NbS should be developed
in Canada (or in any country to our knowledge). This is needed.

Communication-Efficient Federated Learning Systems

In distributed computing settings, data privacy is of paramount importance (e.g., mobile or medical devices). Federated Learning (FL) empowered the state-of-the-art deep neural network model to provide AI solutions to clients while keeping the client data private in distributed computing settings. Communication between different devices is one of the main bottlenecks of FL model because of the heterogeneity of computational resources and network conditions of different client devices. Considering the diversity and prevalence of AMD’s hardware ecosystem, we would like to evaluate and explore the generalizability and impact of various optimization methods (e.g., compression and quantization techniques). These optimization methods will focus on levigating communication bottlenecks in FL systems through improved computational intensity and memory usage. This study will provide AMD with an FL optimization method that AMD can apply to client FL systems solutions with AMD hardware.

4D Scanning and Automated Reconstruction of Digital Doubles for Visual Effects

Modern visual effect films rely on photorealistic, believable, synthesized humans and characters. Often these characters are used as stand-ins for the hero character in a film and are referred to as digital doubles. In the past, digital doubles have been limited in use due to cost or complexity. As the demand for the use of digital doubles in more aspects of modern filmmaking rises, we seek solutions to accelerate and automate the creation of digital doubles. This project will explore approaches to capture sufficient high-quality details of an actor’s performance, in neutral or synthesized lighting conditions, to generate a synthetic human.