Innovative Projects Realized

Comprehensive Study of Tri-generation System based on ClimatwWellTechnology for Canadian Residential Homes

A feasibility study will be conducted for a proposed tri-generation (generation of heat, electricity and cooling)

using a Stirling engine and a ClimateWell heat activated chiller. The Stirling engine will provide simultaneous

heat and electricity from burning natural gas. The heat from the engine will be transferred to either space

heating, domestic hot water heating, or to the heat activated chiller. The chiller will then be able to provide space

cooling. The ClimateWell chiller has been installed in commercial applications (hospitals, commercial building,

etc.) but has not yet been used for residential applications. The proposed study seeks to determine the

engineering and economic feasibility of the tri-generation for residential use. MITACS interns will be installing,

running, and testing a prototype system at the Archetype Sustainable House at the Toronto and Region

Conservation Authority. The data will be analyzed and design improvements/recommendations will be made

Providing Resident Support: Incorporating lived experiences into the development process

Due to the lack of investment from the Federal and Provincial governments for affordable housing and the low number of housing cooperatives formed in the last 30 years the co-op housing model is outdated. In response to a need for affordable housing in Vancouver, BC the Community Land Trust (CLT) has recently opened two newly built housing cooperatives as well as affordable housing option with non-profit partners. This research project aims to identify an engagement framework for the development/redevelopment process of CLT projects. Specifically, CLT wants to incorporate the lived experiences and opinions of residents into their engagement strategy throughout the development/redevelopment process.

Visual Inspection UAV in Harsh Environments and Confined Spaces

Preforming regular visual inspections is essential in up keeping structures. These inspections are used to identify defects at an early stage before they pose a major threat. Unfortunately, these inspections require scaffolding or hiring a boom to access certain areas. Other areas are tight and put the worker at risk. The use of visual inspection drones, specifically tailored for confined space, provide an excellent tool to perform these inspections. Throughout this research project we will be testing and analyzing how these drones perform/withstand different harsh environments. These environments include: extreme temperatures, high moisture, acidic vapour. Initially, we will test the subcomponents individually, then proceeding to testing the drone as a whole. Ultimately, we will provide our partner will a fully functional confined space visual inspection drone able to operate in harsh environments.

Exploring applications of Machine Learning and Quantum Computing in optimization, finance, and the healthcare industry

MITACS interns at 1QBit will aid in the research and development of experimental usage of quantum and classical hardware devices for industry use including, healthcare, finance, advanced materials, and optimization. Interns will have the opportunity to work alongside academics and research teams. MITACS interns will gain the practical experience of applying their knowledge for industry use and working in a business setting with clients. Internships provide a great opportunity for future career of the students at 1QBit. 1QBit will stay in touch with successful interns and offer hiring opportunities to the elite students after successful completion of their postgraduate degrees.

Composing without forgetting

In this project, we propose a continual learning approach to face the problem of catastrophic forgetting in online image classification problems. Concretely, we propose a model that learns how to mask a series of general modules in a deep learning architecture, so that generalization emerges through the composition of those modules. This is of vital importance for Element AI to provide reusable solutions that scale with new data, without the need of learning a new model for every problem and improving the overall performance.

Sustainable Engineering Design Audit

The Sustainable Engineering Design Audit (SEDA) score is to be designed to evaluate environmental impact on the balance sheet alongside the bottom line. The ability to evaluate environmental use alongside financial decisions will increase green practices and pro-environmental behaviour. The score will use appropriate Life Cycle Analysis (LCA) boundaries through a cradle-to-grave analysis and examine the regional impacts (i.e. spatial, temporal, concentration, etc…) for RWH Engineering in Toronto. The SEDA score will incorporate methodology used in Eco-Indicator 99 and the simplicity of the nine Planetary Boundaries for easy conceptual understanding. The context of the LCA and environmental preference is to be weighted within a values based assessment of the employees on what is most important to them; a survey is to be conducted to determine these results. A cost-benefit analysis will be used demonstrating financial benefits of using the score….

Evaluation of Durable Hydrocarbon-Based Proton Exchange Membrane for Fuel Cell Applications

This MITACS project aims to investigate the durability of IONOMR’s PEMIONTM membranes in Proton Exchange Membrane (PEM)-based fuel cells for automotive applications and explore their usage as the PEM in PEM-based fuel cells. Specific test conditions and protocols for use at IONOMR based on industry standards will be developed and the materials will be benchmarked against current state-of the art materials in order to prioritize development efforts and aid in customer adoption efforts. IONOMR will benefit from access to specialized equipment available at UBC in addition to expertise in regards to the testing and optimization of PEM fuel cell solutions provided through the Wilkinson lab.

International Trade Diversification Opportunities for Canada’s Agri-Food Sector: Lessons from New Zealand

The Canadian economy is built on decades-worth of industrial activity and its links to the natural endowment of the country. For an extended period of time, Canadians have looked to our natural resources for wealth creation, value-added processing, and international market opportunities. On the other end, recent studies and experts have demonstrated concern and warnings against the environmental concerns linked to over-harvesting and over-exploiting Canadian natural resources.

As the Canadian economy continues to rely on natural resource extraction and value-added processes, new markets are emerging outside of the United States of America. For Canada, markets like China are becoming more important as demand continues to soar from Chinese middle-class consumers and an appetite for better and more sustainable foods from Canada.

The research internship, which will take place in collaboration with Dalhousie University’s School for Resource and Environmental Studies and Shanghai Jiao Tong University’s School of Environmental Science and Engineering, will look at the current state of international trading from Canada to China for the agri-food sector, in addition to the future outlook of the Canadian single-use plastic packaging dilemma derived from food products.

Reinforcement Learning for Predictive Sports Analytics

Our project develops novel machine learning algorithms for interpreting complex, multi-agent scenarios in sports analytics. The collaboration with our industrial partner SPORTLOGiQ will tackle open problems in deep reinforcement learning to build novel capabilities in sports analytics for ice hockey. Deep reinforcement learning is a breakthrough technology with prominent successes in games such as Go (AlphaGo) and Chess (AlphaZero). We will develop fundamental algorithmic advances and apply them to tasks including:
– player evaluation
– event predictions (match outcomes, next action, expected scores)
– recognizing types of players, teams, play sequences, and tactics
– identifying characteristic strengths and weaknesses of players and teams
Montreal-based SPORTLOGiQ uses advanced computer vision to extract information about events from video of sports matches. Their information is more detailed than that provided by any other company or organization. This project will build significant Canadian capacity in sports analytics, support academic research, advance commercialization in the sports industry, and train highly qualified personnel.

Comparative assessment of Machine Learning methods for fraud detection and improving the interpretability of the best model

Machine learning algorithms are being used in a wide range of applications. It is a branch of computer science where the system can learn from the data and make decisions. Financial fraud is an increasing hazard in the financial industry, and it is important to detect a fraudulent transaction. Machine learning algorithms can be used to decide whether the transaction is fraud or not. After the system makes its prediction, it is important for users to understand the reason behind the prediction in such cases. This research project presents a machine learning classifier for fraud detection that will predict if the transaction is fraudulent or not, and also the interpretation of the predictions made by the model for them to be understood by humans.

A Photovoice Study of Settlement Experiences and Needs of Recent Immigrant Men in Central Alberta

Red Deer College, together with the Red Deer Local Immigration Partnership (RDLIP), received Mitacs Accelerate funding to research the settlement experiences of recent immigrant men to Central Alberta. There have been concerted efforts from the federal, provincial, and municipal governments to attract and retain newcomers in mid-size cities and rural communities in Canada; nevertheless, the overall settlement experiences and needs of newcomers in these communities have not received much empirical investigation; this is especially the case among immigrant men. This research will examine settlement experiences of recent immigrant men in Central Alberta to address the knowledge gap. The research team will share research findings with community members, settlement services provider organizations, and policymakers through Photovoice exhibition, conference presentation, and publication in an academic journal.

Imaging the Shallow Shear Wave Velocity Structure of the Northern Cascadia Subduction Zone via Compliance Signals Recorded by Ocean-Bottom Seismometers

The Cascadia subduction zone, which extends along a significant portion of western North America from northern California to southern British Columbia, has the potential to produce large devastating earthquakes and tsunamis. Therefore, better understanding the complex structure of the Cascadia subduction zone is paramount to accurately assessing the seismic and tsunamigenic hazard it poses to the western hemisphere. However, this region is difficult to study because it lies entirely offshore. Therefore, this study will image the structure of the subduction zone by using data recorded by instruments deployed on the seafloor, specifically, those belonging to the Canadian NEPTUNE (North-East Pacific Time-series Undersea Networked Experiments) array. One outcome of this research is that it will pave the way for further seismological study utilizing NEPTUNE data. For example, a firm understanding of how seismic energy interacts with the structure beneath NEPTUNE stations is critical to further studies using other imaging techniques. Furthermore this work will showcase the utility of the NEPTUNE arrat for subduction zone research.