Innovative Projects Realized

Fuel cell diagnostics through integrating model-based technique with electrochemical impedance spectroscopy (EIS)

The wider adoption of fuel cells has been hindered by durability and reliability issues. This research alliance (consisting of Ballard and SFU) has made significant recent advances on developing novel diagnostics techniques to detect the inception of hydrogen leak faults in fuel cells. We aim to continue to build on these advances through this grant.
This research alliance has been working towards truly online diagnostics techniques for detection life limiting faults in fuel cells. Previous successful projects had focused on diagnosing leak faults. This proposal will focus on monitoring the hydration of (level of water within) the membrane used in the fuel cell as loss of hydration is a major degradation mechanism. To this end, this project will develop an enhanced model for fuel cell hydration and simulate different fault levels to generated a fault profile detector.

Squeezing water from rocks: Moon ore and mineral process prototype for water production

For human space exploration to be successful, technologies to produce breathable air, drinking water, and fuel are needed to sustain life. Particularly on the Moon, there are a variety of minerals and ore in different regions on the Moon’s surface that can be extracted and chemically processed to produce air, water, and fuel. The objective of this project is to test and scale up a novel process, invented by CSMC, for producing water and oxygen from minerals that are prepared to simulate the composition of the Moon’s soil. Once this is accomplished in a scalable way, it will be possible for humankind to establish a Moon colony for resource extraction and further space exploration. The benefit to the partner will be to have a completed prototype that is designed to maximize the water produced from the Moon’s soil.

Autonomous Cable Seal Application and Removal

The two major objectives of the umbrella grant are to 1) empower the partner organization to improve their digital capacity and gain insight into their data to support business growth and innovation. 2) Provide students with work-integrated learning experiences that expose the students to advanced data analytical techniques, data mining and machine learning. DICE focuses on a companies Data, Networking and Analysis (DNA) which is often unique to each company and involves solutions that are as unique as the DNA in humans.

This project is projected to be a part of a larger project 3 years in length and will require interns to grow their knowledge in multiple different areas of technology including Machine Learning (ML), IoT and modern software development frameworks.

Rayhawk is providing autonomous railcar loading technologies for granular loading facilities. This project entails enhancing Rayhawk’s existing vision system to work efficiently and without issues as Rayhawk scales it’s customer base and railcar support. Rayhawk has built a machine learning pipeline that has currently satisfied our first production unit within an inside facility focusing on one particular railcar, but as we scale we need to support multiple railcars as well as support them in all weather and all condition with a railcar either stationary or in continuous motion. Data research and analysis is needed to identify steps required to provide a scalable vision system across all of these variables.

This project will focus on integrating LiDAR and other sensors such as cameras and infrared sensors in conjunction with a robotic assembly that will be used to interact with railcars during the loading processes.

Graph-Driven Strategic Intelligence: Innovations in Forecasting, Delisting, and Marketing Optimization – Part 1

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

Federation of heterogeneous data sources for the linked data back-end in the Gold Fish mobile application

The overall goal is to create the backend of a mobile personal organizer that suggests professional events (conferences, colloquia, workshops, exhibitions) and contacts to establish while attending events, to members. Currently, the target audience are professionals in the biomedical domain. Given the need to feed data and meta-data from heterogeneous sources into the application, the industrial partner chose to implement the backend using semantic data management technologies. The main challenge at this stage is therefore to create the database (triplestore) that federates the chosen sources (social network profiles, event descriptions, domain terminologies, standards, etc.). Tools designed and co-designed by members of the academic team will be used and improved during this stage, in particular, methods for matching schemas and ontologies (domain models) originating in independent sources as well as for recognizing the alternative representations of the same entity (e.g. event) in independently created datasets. Thus, the academic team brings its joint expertise in the semantic technologies and its tools to the project that thoroughly benefits the industrial partner.

AI-Driven AR Avatars for Situated Learning

In this project, we explore using augmented reality avatars for supporting learning in low-traffic makerspaces. While makerspaces primarily provide community members with access to tools, they also serve as places to learn where community members build expertise with the equipment and fabrication processes. This learning process can either be guided formally using the makerspace as a working classroom or informally by learning through watching and socializing with peers. These learning opportunities are present in high-traffic makerspaces or at high-use times; however many makerspaces do not have consistently high traffic, or relevant expertise may be unavailable at appropriate times. Artificial intelligence offers opportunities to provide individualized learning support, however, lacks the physicality for learning spaces like makerspaces. Thus, we want to investigate ways that AI-driven AR avatars can support learning experiences in low-traffic makerspaces when human instructors or peer learning is unavailable. Our work can help democratize access to learning experiences in physical spaces such as makerspaces by increasing access to learning resources. This democratization can help to enable lifelong learning in key areas such as trades or fabrication technologies and help to create a flexible workforce that is resilient to a changing future.

Vanadium-Induced Protein Aggregation

Human health is impacted every day by metal contact, ranging from direct contact with metal surfaces to exposure to corrosion by-products, nanoparticles, or ions. The interaction of human proteins with these materials remains poorly understood, but metal-protein contact has been implicated as the cause of many adverse physiological reactions, such as allergies, contact dermatitis, and cancer. It has been suggested that metal exposure can result in structural changes and the aggregation of proteins, which in turn can influence corrosion reactions in protein-rich environments. Protein aggregation is harmful because it is toxic to cells, and thought to be the cause of many neurodegenerative diseases. Vanadium is an alloying element used in the most common titanium biomaterial, and exposure to this metal is suggested to be especially harmful. In this project I will travel to Colorado State University to work in Dr. Debbie Crans’ research group where they study vanadium chemistry and toxicity, and I will investigate the interactions between vanadium from corroding biomedical implants and human proteins. This will provide a new system for the Crans group to study, and will benefit my home group, Dr. Yolanda Hedberg’s group at Western University, where we study biomedical implant corrosion.

The Impacts of Driver Monitoring Systems on Mitigating Drowsiness Due to Conditional Automation

Driving automation is becoming increasingly available with the advancement in sensors and computational power. The next generation, i.e., conditional automation, allows drivers to engage in other activities like sleeping. If the system cannot operate in certain conditions due to limitations, the driver is required to takeover vehicle control. However, sleepy drivers might not be fit for taking over. Driver monitoring systems (DMS) can use driver physiological and behavioural data (e.g., heart rate, eye-tracking), vehicle kinematics (e.g., lane position), subjective measures, or their combination to detect unsafe driver states. DMS can be used to inform the vehicle and initiate interventions (e.g., warning systems, adaptive interfaces) to alert the driver. There is a vital need to understand how conditional automation can lead to drowsiness and whether and how the vehicle can intervene using a DMS to prepare the driver for a takeover request (TOR).

The objective of this study is to understand how drowsy drivers interact with a TOR in conditionally automated vehicles. The second goal of the study is to evaluate DMS-based drowsiness interventions to prepare the drowsy driver for an upcoming TOR. For this purpose, a driving simulator study will be conducted at the Driver-Vehicle Interaction Lab, Ulm University.

Understanding Drone Pilot Needs to Develop a VR Training System

Using drones to inspect power lines, which can involve travelling in isolated areas, makes this process faster and more secure. Yet, training drone pilots for this task is time-consuming and expensive. In this project, we propose to partner with Connect Atlantic Utility Services Corporation to develop a Virtual Reality training system for drone pilots. This system will utilize the industry partner’s training scenarios and geographical data to create a realistic experience. Moreover, we will design a system that captures the drone pilots’ training needs by talking with current drone pilots. In return, Connect will acquire a custom-made VR training system for drone pilots that will help enhance training efficacy, reduce operational costs, and expedite the deployment of skilled drone pilots for powerline inspections.

Comparative Analysis of the Electrified Haber Bosch and the Electrochemical Ammonia Synthesis Approaches for Green Ammonia Production

Currently, green ammonia is produced using a process that combines green hydrogen and nitrogen. There’s a newer method called Direct Ammonia Synthesis (DAS) that can be better because it can produce green ammonia directly, without going through the extra steps of producing green hydrogen and then combining it with nitrogen. This could save a lot of money and energy. We’re studying both methods and will conduct a comparative analysis. The partner company, FuelPositive, has a system using the current method, and we want to compare it with the new DAS approach. We’ll look at how much ammonia each method makes using the same amount of power, water, and nitrogen. Additionally, we’ll use a neural network model to compare the DAS method with FuelPositive’s approach.

Seasonal Domestic Harvest Labour Access in British Columbia

British Columbia’s agriculture sector faces substantial labour issues including difficulties in recruitment, retention, and workforce engagement of domestic seasonal workers, which significantly impacts its productivity and sustainability. This project addresses the critical need for a comprehensive understanding of the domestic seasonal labour market: who the workers are, what attract them, how we can increase their participation in the short term and long term. The study will focus on collect in-depth interview data from growers and domestic workers in apple, cherry and grape industries in the Okanagan region. The results can provide recommendations to improve the sector’s competitiveness, increase appeal and strategies for recruitment, retention and relationship management of domestic labour.

Non-wood fibrillated cellulose composites

The aim of the proposed research is to develop renewable textiles from agricultural residues. The project will use an alternative process, proposed by Earth Protex, for converting primarily wheat straw into a wood pulp like material while minimizing the losses during the process. The pulp will undergo further chemical treatment under mild conditions before being sheared into smaller fibrils, referred to as fibrillated cellulose. The fibrillated cellulose will then be reassembled into filaments which can be spun into yarns to produce renewable and sustainable textile alternatives to cotton and polyester, which both come with significant environmental challenges. Wheat straw is an ample residue in the Canadian Prairies and the development and eventual commercialization of the proposed technologies can bring economical, social and environmental benefits to local communities, as well as environmental benefits across Canada and the globe by contributing to a transition towards sustainable textiles and clothing.