It is difficult to perform the dynamic analysis on large scale power systems within a desirable time frame. Most utilities therefore resort to reduce the scale of power system by representing the external system using an equivalent network. This project proposal in conjunction with Manitoba HVDC Research Centre aims to develop simulation based methods complemented with modal methods to obtain a dynamic system equivalent for the external power system.
The goal of this research project is to develop novel technics to teach artificial agents how to play complex video games using reinforcement learning and demonstrations. Namely, we wish to propose a novel approach for learning from demonstrations, in which an agent simultaneously learns a behavior and the corresponding reward signal. This training procedure will rely on generative adversarial imitation learning in order to learn from expert demonstrations (in our case from players).
In this research, a new approach to model Frequency Dependant Network Equivalent (FDNE) will be introduced and implemented in PSCAD/EMTDC. FDNEs are used to accelerate and reduce the size of unnecessary part of the network under simulation. The new approach utilizes Brunes network synthesis and Tellegens extension to create a multiport network whose impedance is the same as the given FDNE. Unlike other fitting methods, the proposed method inherently guarantees the passivity of the fitted network, thus no need for further passivity enforcement.
The proposed project represents a critical effort towards developing the enabling communication technology for the future of subsea connectivity where conventional communications technologies such as Wi-Fi and GPS cannot be used. The intern will work to completely overhaul traditional underwater communications methodologies and advance acoustic communications towards the higher reliability and data rates needed for future underwater networked applications and deployments.
Autonomous vehicular applications require the distribution of high volume of information-rich and safety-critical data among heterogeneous players. Autonomous vehicles (AV) communicate with each other and the world around them in high mobility manner under poor connectivity and tough signal propagation. Attacking AVs are applicable business and cyber-attacks can affect the AV industry and cause severe damages to individuals and organizations.
The proposed internships aim at investigating the relevance of deep learning (DL) techniques for target detection in radar data processing. More specifically, we are looking to demonstrate the feasibility of DL techniques to deal with unusual types of data (i.e., radar data) in situations where an well performing processing with classical techniques is a challenge (e.g., detection of objects in noisy scenes from a maritime environment caused by the interference produced by the reflection of the radar waves on the sea).
Modern power systems wherein renewable energy sources are prevalent will exhibit larger frequency deviations than conventional power systems due to the diluted share of conventional generation based upon large electric machines with massive spinning rotors. To combat this, power-electronic converters that are used to interface renewable sources need to provide ancillary, such as frequency support and inertia emulation. This research will investigate this functionality for a class of power-electronic converters, namely modular multilevel converters.
One in every five people will develop liver disease in their lifetime. Few people think about liver disease until the disease has progressed and has permanently damaged the liver. Fatty liver disease falls into this category with obesity as the most common cause. Given more than 50% of Canadians are overweight and 75% of obese individuals are at risk of developing a fatty liver, this is a widespread problem. Canadians with fatty liver and other liver diseases need a tool for detecting and monitoring their liver health. Analogous to blood pressure numbers, we need numbers for liver health.
The interns will develop a quantum communication network built on RBCs optical fiber infrastructure and perform secure commercial transactions using quantum-generated secure keys in the integrated classical communication network. The quantum communication network will be based on the measurement-device-independent quantum key distribution technology, which is developed by Prof. Hoi-Kwong Los and Prof. Li Qians groups at the University of Toronto. It will be an important milestone towards cybersecurity in the financial sector in Canada.
Ericsson is an industry leader in offering telecommunication solutions and products. As an important step on the path towards the automatic and autonomous management of next generation networks, Ericsson is developing technology in machine learning and artificial intelligence that will benefit operators around the world, including in Canada where Ericsson supplies technology to most of the major telecommunication network operators.