Projets novateurs réalisés

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

29 670 projets achevés

2811
AB
4990
C.-B.
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projets par catégorie

Development of a Cost Effective Type-Specific Small Aircraft Simulator

Current flight simulation technology is aimed at the commercial aviation industry, with larger
simulators used to simulate larger aircraft. There are few high fidelity type-specific aircraft
simulators available and these have a much higher cost than most flying clubs can afford.
This project aims to produce a low cost Diamond DA20 aircraft simulator while meeting
Transport Canada requirements for a minimum of Level 5 Flight Training Device certification.
Reducing cost without sacrificing fidelity of the flight simulator comes with many technical
challenges which will require the collaboration of the Carleton University research team, OAS
Flight Centre principals and staff, and the B-Con Engineering team. This project will provide
OAS Flight Centre with a viable option for increasing its simulator capacity and student
throughput. For B-Con, this project will provide a platform to demonstrate its technology for
the projection of images onto non-flat surfaces. This project was preceded by a feasibility
assessment which concluded that such a simulator could be developed

Voir la description complète du projet
Superviseur du corps professoral :

Robert Langlois

Étudiant :

Partenaire :

Ottawa Aviation Services Flight Centre;B-Con Engineering Inc

Discipline :

Engineering

Secteur :

Professional, scientific and technical services

Université :

Carleton University

Programme :

Accelerate

Metal scrap recycling 4.0: towards smart operation and productionof high-quality alloys

Metalliage is a company that recycles scrap ferrotitanium parts. It supplies ferrotitanium to manufacturers all over the world. The plant has five departments through which the products pass successively: the material division, the washing line, the furnace, the crushing, and the bagging. However, the production line is not optimized, there is plenty of handling; and the plans of the factory detailing its organization are outdated. In order to increase its market share, in the era of Industry 4.0, Metalliage aims to identify critical issues in their production process in order to improve them. The objective of this project is to identify these problems and to propose solutions to solve them like automating tasks or rearrange (modify) installations. Methods such as Value Stream Mapping or 5S and Lean Manufacturing approaches will be deployed to meet these objectives.

Voir la description complète du projet
Superviseur du corps professoral :

Lucas Hof;Jean-Pierre Kenne

Étudiant :

Partenaire :

Metalliage Inc

Discipline :

Engineering

Secteur :

Manufacturing and Construction; Sustainability & the Environment; Advanced Manufacturing

Université :

École de technologie supérieure

Programme :

Accelerate

Development and modification of green composites obtained from recycled polyethylene/crumb tire/ inorganic additives for exterior applications

In the current project, the use of recycled polymers such as landfilled tires and polyethylene (PE) plastic is suggested to be reused for the manufacturing of composites for exterior applications such as shingle roofing, cladding and post fencing. Even though, this material has been already used for this application, but the mechanical and thermal properties of the products need to be developed by using inorganic additives. In this project, aluminum trihydrate (ATH) and clay are suggested to improve flame retardancy and mechanical properties of the PE/crumb tire/additive. Therefore, several formulations and compounding methods will be investigated to achieve the target.

Voir la description complète du projet
Superviseur du corps professoral :

Eric David

Étudiant :

Partenaire :

Industrial Solutions

Discipline :

Engineering

Secteur :

Manufacturing

Université :

École de technologie supérieure

Programme :

Accelerate

Mise en place d’un banc d’essai de phytoremédiation sur le site Blasting Cap de la propriété d’Orica Canada Inc

Afin de limiter dans le sol l’accumulation et la mobilité de contaminants pouvant entraîner un risque potentiel pour la santé humaine, animale et végétale, Orica Canada Inc., un fournisseur mondial d’explosifs commerciaux, souhaiterait décontaminer un de leur terrain hébergeant auparavant une usine de production d’explosifs. L’entreprise désire s’engager résolument dans des approches vertes et durables pour résoudre cette problématique environnementale qu’est celle des sols contaminés, et cela en faisant appel à la phytoremédiation, une technologie utilisant les végétaux, et parfois les microorganismes (bactéries et champignons) qui leur sont associés, pour éliminer, contenir ou rendre moins toxiques les contaminants présents dans le sol. Ainsi, par la mise en place de plantation à petite échelle sur une portion du site, le stagiaire Mitacs évaluera les capacités adaptatives et phytoremédiatrices de différentes espèces végétales et étudiera les interactions qu’elles établissent avec les bactéries et champignons du sol.

Voir la description complète du projet
Superviseur du corps professoral :

Michel Labrecque

Étudiant :

Partenaire :

Orica

Discipline :

Life Sciences

Secteur :

Manufacturing

Université :

Université de Montréal

Programme :

Accelerate

Testing, Integration, and Optimal Control Strategy of Residential Hybrid HVAC System

The Canadian federal government committed to encouraging low carbon alternatives and the growth of clean technology that reduces greenhouse gas (GHG) emissions. It is stated that the new target is to reduce GHG emissions by 80% by 2050, relative to 2005 GHG levels. In order to achieve this goal, one of the government’s strategic plan is to promote systems and technologies that minimize natural gas/fossil fuel usage and increase the use of clean electricity. Although various research groups studied the potentials in energy consumption reduction in residential houses, hybrid integrated energy systems are found to be effective in reducing energy consumption and its associated operating cost and GHG emissions. However, their optimal control methodology is still lacking for cost-effective large-scale deployment and adoption of such hybrid residential HVAC systems in the Canadian residential sector. Therefore, this project will examine the benefits of a state-of-the-art cloud-based Smart Dual Fuel Switching System (SDFSS) of two sets of residential hybrid HVAC system of 1) electric air source heat pump (ASHP) and natural gas furnace (NGF) and 2) ASHP, electric water heater tank, and natural gas instantaneous hot water heater, for simultaneous reduction of energy cost and GHG emission.

Voir la description complète du projet
Superviseur du corps professoral :

Alan Fung

Étudiant :

Partenaire :

Cricket Energy

Discipline :

Engineering

Secteur :

Construction and infrastructure

Université :

Toronto Metropolitan University

Programme :

Accelerate

Human Motion Library and Predictive Capabilities for Digital Human Ergonomics Simulation Solution

Within their ergonomics process, automotive manufacturers rely heavily on computer simulation technology, specifically “Jack” (Siemens PLM, Plano, TX). Advancements to “Jack” provide users the ability to create workstations, yet much time is required to produce a single simulation. This 3-year industrial collaboration will reduce the time necessary for their completion and, improve on the accuracy of digital simulations. This will be accomplished by capturing motions of humans performing common manufacturing tasks, identified as difficult to simulate, allowing for the creation of a dynamic-motion-library that will be integrated into “Jack”.

Voir la description complète du projet
Superviseur du corps professoral :

Joel Cort

Étudiant :

Partenaire :

Ford Motor Company

Discipline :

Engineering

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

University of Windsor

Programme :

Accelerate

Applying Machine Learning to Develop Meaningful Rail Condition Indices

Rail transit and freight rail properties apply rail grinding to maintain rail condition and ensure satisfactory performance of rail infrastructure systems. The proposed research investigates and applies a variety of computationally intelligent algorithms to establish useful relationships between rail corrugation, noise generation, and vibration. These relationships will support more timely and effective rail grinding interventions. The algorithms will process real-world rail corrugation, noise, and vibration data collected from three rail transit properties in North America. The long-term research goal is the development of a generic and transferrable rail corrugation index, which will help rail maintenance practitioners determine when rail corrugation is likely to generate unacceptable noise and vibration. Consequently, the research directly supports rail and vehicle asset management programs, helps reduce noise irritation for passengers and citizens in the vicinity of rail transit lines, and improves ride quality.

Voir la description complète du projet
Superviseur du corps professoral :

Ian Jeffrey;Jonathan Regehr

Étudiant :

Partenaire :

Advanced Rail Management

Discipline :

Engineering

Secteur :

Professional, scientific and technical services; Transportation and warehousing

Université :

University of Manitoba

Programme :

Accelerate

Semi supervised object detection

Deep learning technology is a great tool to learn complex patterns and make prediction based on this learning. In order to get the most accurate predictions, one needs to train those neural networks on vast amount of labelled data. Labelling data is a time consuming and costly task. Using semi supervised learning, it should be possible to label a fraction of the dataset and let the neural network learn by itself on the rest of the, unlabelled, data, thus greatly reducing the overhead of using deep learning technology. This project aims at identifying and implementing the best possible semi supervised strategy for object detection.

Voir la description complète du projet
Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

Teledyne DALSA Semiconducteur (Montreal, QC)

Discipline :

Computer science

Secteur :

Manufacturing

Université :

Université de Montréal

Programme :

Accelerate

Computer Vision R&D project – Autonomous Robot Assistant

The project’s main objective is to provide a robot with the capabilities to perform diverse helping tasks in the office, such as fetching objects, greeting colleagues, clients and newcomers. This will be a stimulating project involving tangible real-world application of multiple artificial intelligence approaches to robotics, including and focused on advanced computer vision algorithms, in the objective for the robot to navigate and recognize objects using a built-in camera.
This project will provide the company with a definite increase in its expertise in the commercial application of AI to robotics. Since Menya has already shown its skills in the space industry, this project may help get into new sectors in industry, but also in societal domains such as healthcare and education.

Voir la description complète du projet
Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

Menya Solutions

Discipline :

Computer science

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

Université de Montréal

Programme :

Accelerate

Power network transfer capability (Phase II – data error detection)

Hydro-Québec is a public utility that generates and distributes electricity. Despite selling most of its electricity in Québec, its most lucrative sales are in the neighboring markets. To ensure the best possible quality of service, the transmission system must remain stable, but to maximize profits, the company also wants to increase its transmission capacity to maximize energy exports. The transfer limit is now conservatively estimated based on a certain combination of simulated network configurations. This project aims to more accurately estimate the transfer limits of the electric grid and the uncertainty of these estimated limits. Recent advances in machine learning, especially in deep learning, in conjunction with more traditional algorithms used in computer science, have the potential to improve these estimates and therefore augment exports for Hydro-Québec.

Voir la description complète du projet
Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

Hydro-Quebec (Varennes, QC)

Discipline :

Computer science

Secteur :

Utilities

Université :

Université de Montréal

Programme :

Accelerate

The Development of Anti-gingivitis Probiotics Derived From the Human Oral Microbiome

The human oral cavity contains over 700 different bacterial species. In healthy people, these bacteria are living in harmony and not likely to cause diseases. However, sometimes this bacterial balance is disturbed as the oral pathogenic bacteria start to overgrow causing many oral implications such as halitosis, sore throat, dental caries and gingivitis. A promising solution to tackle this microbial population destabilization is the use of beneficial microbes called probiotics. Previous investigations showed that the oral commensal Streptococcus salivarius is an excellent candidate for the development of new probiotic treatments. This bacterium is human friendly and is one of the first microorganisms to colonise the babies few hours after birth. S. salivarius can produce unique molecules which can be used as molecular missiles to attack pathogenic bacteria and restore the microbial balance to the oral cavity.

Voir la description complète du projet
Superviseur du corps professoral :

Bernhard Ganss;Michael Glogauer

Étudiant :

Partenaire :

Ostia Sciences

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

With the Child in Mind – Brain Development and Best Interests Decisions

Everyday across Canada, judges from both the federal and provincial courts make ‘best interest’ decisions affecting the lives of thousands of children and their families. Infants and children whose cases come before the justice sytem are more likely to have experienced abuse, neglect, exposure to family violence, parenting impaired by addiction or mental illness, and substandard or unstable home environments. Lawyers, judges and child-serving professionals need to be educated in the science of early brain development so that they can maked informed, timely decisions that will have lasting positive effects on the health and development of children. This project aims to collaborate with family court judges and lawyers to identify their information and learning needs in the area of early brain developement. These needs will be adressed by creating and piloting learning resources that will allow them to meaningfully integrate the science of early child development into their daily practice.

Voir la description complète du projet
Superviseur du corps professoral :

Laura Ghali

Étudiant :

Partenaire :

Sinneave Family Foundation

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology

Université :

University of Calgary

Programme :

Accelerate

Précédent
1...1,9331,9341,9351,9361,9371,9381,939...2,473
Suivant

Joignez-vous à un écosystème d’innovation florissant.

Abonnez-vous à notre infolettre dès maintenant!

S’inscrire