Transformers are one of the key elements in electric power systems whose health ensures reliable, stable electric power for the economy and society. Partial discharge (PD) is known to be a symptom of defects in electrical insulation systems, such as those employed in transformers. Detection and localization of PD in transformers are critical to develop a mitigation strategy. The main objective of the proposed research is to develop an artificial intelligence model that is able to predict the location of PD in transformers.

Power systems are subjected to randomly occurring normal and abnormal events that can trigger oscillatory response creating unsafe sustained fluctuations in power, voltages, currents and the frequency. Increasing integration of power electronics interfaced renewable generation contribute to new types of oscillations due to control interactions, sometimes supper imposed on classical electromechanical oscillations. Such oscillations can damage equipment and ultimately results in blackouts due to cascaded tripping if not controlled using appropriate remedial actions.

Timely diagnosis of dementia is a public health challenge within Canada. It is hoped that machine-learning empowered cognitive examinations might improve Canada’s response to the dementia population. This study proposed the validation of an automated cognitive examination, the Autonomous Cognitive Examination, which uses machine learning to evaluate users to evaluate previously difficult to assess inputs such as complex images or speech.

Our main aim is to design and fabricate lightweight mechanical structures to show high energy absorption capacity with as low as initial reaction forces, electricity generation via embedding smart materials (piezo patches), and isolate external dynamic excitations. The designed mechanical structures could be applied in the automobile and motorcycle industries. In a car crash, the designed mechanical structures could absorb a significant amount of kinetic energy with low initial reaction forces simultaneously, that could provide safety for the passengers.

Translation of scientific bench-top discoveries into a commercial enterprise can be one of the hardest transitions for academic groups. Additionally, there are very few therapeutic options for neurodegenerative diseases, and a great need exists for the development of new drug candidates to treat diseases, such as amyotrophic lateral sclerosis. Our proposal seeks to continue with the development of new neurodegenerative therapeutics, while simultaneously analyzing and developing commercialization strategies for the translation of drug discoveries into start-up enterprises.

Manufacture companies use foam materials made of polystyrene to protect the goods during storage and shipping. However, polystyrene is plastic-based and made using crude oil, which is terrible for the environment. Used polystyrene foams are challenging to eliminate and occupy space in landfills for a very long period.

Pharmaceutical contamination of water systems poses direct threats to human health and the environment. Unfortunately, wastewater treatment plants are not always capable of sufficiently removing pharmaceutical contamination, particularly where drugs are disposed of in high concentrations – such as pharmaceutical manufacturing facilities. When some residual drugs are released into the environment they can persist and disrupt important functions in humans and wildlife.

Augmented Reality (AR) provides real-time interactive instructions to improve efficiency and accuracy of product maintenance processes. Although different methods have been proposed for the AR-based product maintenance, there is a lack of methods of automatic product disassembly and assembly planning under the AR environment. This project develops an AR-based tool for product maintenance instructions. The maintenance targets are modeled, tracked and simulated in an AR user interface developed to facilitate the guide and visualization of maintenance operations.

To maintain grain quality, grain drying is essential to turn the newly harvested grain from a high moisture content, high temperature and easily spoiled state to a safe condition for transportation or storage. Convection grain dryers are the most widely-used grain dryers around the world, however, the design of dryers nowadays is still mainly based on experience. This study presented a numerical model to simulate the drying processes in a mixed-flow grain dryer based on coupled DEM-CFD method.

Products have to meet high customer satisfaction standards, low production costs in order to compete in the global market. Comparing with conventional production techniques, the additive manufacturing (AM) technology can construct complicated forms with more efficiency. In order to produce high quality 3D printed parts, the appropriate selection of printing parameters is crucial. Advanced parameter settings can improve the printing quality and reduce the printing defect.