Projets novateurs réalisés

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

30 508 projets complétés

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5105
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825
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681
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860
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97
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586
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Projets par catégorie

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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Superviseur du corps professoral :

Oh-Sung Kwon

Étudiant :

Partenaire :

Kyungpook National University

Discipline :

Engineering

Secteur :

Construction; Environmental Science and Technology; Sustainability & the Environment

Université :

University of Toronto

Programme :

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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Superviseur du corps professoral :

Ioannis Mitliagkas

Étudiant :

Partenaire :

AFX Medical Inc.

Discipline :

Computer science

Secteur :

Information and cultural industries

Université :

Université de Montréal

Programme :

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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Superviseur du corps professoral :

Christian Moreau;Pantcho Stoyanov

Étudiant :

Partenaire :

Besser Proneq

Discipline :

Engineering

Secteur :

Manufacturing

Université :

Concordia University

Programme :

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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Superviseur du corps professoral :

Liliana Perez

Étudiant :

Partenaire :

SYSTRA Canada Inc

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

Université de Montréal

Programme :

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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Superviseur du corps professoral :

Stephanie Gora

Étudiant :

Partenaire :

Ontario Clean Water Agency

Discipline :

Engineering

Secteur :

Construction and infrastructure; Utilities

Université :

York University

Programme :

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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Superviseur du corps professoral :

Manuel Morales;Christian Dorion;Anatoliy Swishchuk;Manuel Morales

Étudiant :

Partenaire :

Futures First Canada Inc

Discipline :

Mathematics

Secteur :

Finance and Insurance

Université :

HEC Montréal; Université de Montréal; University of Calgary

Programme :

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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Superviseur du corps professoral :

Simon Bonner

Étudiant :

Partenaire :

University of California, San Diego

Discipline :

Mathematics

Secteur :

Education

Université :

The University of Western Ontario

Programme :

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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Superviseur du corps professoral :

Raman Paranjape

Étudiant :

Partenaire :

Saskatchewan Telecommunications

Discipline :

Engineering

Secteur :

Information and cultural industries

Université :

University of Regina

Programme :

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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Superviseur du corps professoral :

Keiko Hattori

Étudiant :

Partenaire :

GoldCorp (Mussel White Mines)

Discipline :

Earth science

Secteur :

Mining

Université :

University of Ottawa

Programme :

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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Superviseur du corps professoral :

Gap Soo Chang

Étudiant :

Partenaire :

Seoul National University

Discipline :

Physics

Secteur :

Education

Université :

University of Saskatchewan

Programme :

Globalink Research Award

Information-based Public Transport Control Strategy Study Under Pandemic Situation

Health and safety are key concerns during the current public health crisis to individuals who rely on public transit. To lessen the risk of those individuals, the research aims to develop an information-based control strategy using real-time travel information as a meaningful non-pharmaceutical intervention strategy in the public transportation sector. To achieve this goal, the proposed research has four distinct but related parts that contribute to a safer and resilient public transportation system. First, this research will measure the risk level of infection. Second, the research will explore how information and travel recommendations change the traveler’s choice. Third, we will develop a realistic design of a real-time framework to control the target level of density by providing real-time travel information. Finally, we will take an integrated, user-centered approach based on advanced gamification design techniques to increase transit user’s compliance rate with the travel recommendation. The proposed models can assist in developing the effectiveness of continuing various policies to minimize the spreading of COVID-19.

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Superviseur du corps professoral :

Yong Hoon Kim

Étudiant :

Partenaire :

University of Seoul

Discipline :

Engineering

Secteur :

Transportation (excluding aerospace); Public Service, Policy, and Governance

Université :

University of Windsor

Programme :

Globalink Research Award

Integrated Circuit Architectures for Photonic Computing

Photonic integrated circuits allow the computations required for artificial neural networks to be performed using light. By doing so, they obviate many of the challenges associated with electronic computers, paving the way to a new class of information processing hardware. Such hardware could help address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. However, interfacing electronics are still required, at a minimum to control the photonic devices. This project researchers the microelectronic integrated circuits required to interface to photonic neural networks for computing. In collaboration with industry partners, the objective is to co-design the combined electronic/photonic neural network.

Voir la description complète du projet
Superviseur du corps professoral :

Tony Chan Carusone

Étudiant :

Partenaire :

Huawei Technologies Canada Co Ltd (Markham, ON)

Discipline :

Engineering

Secteur :

Information and cultural industries; Professional, scientific and technical services

Université :

University of Toronto

Programme :

Accelerate

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