Self-Adaptive Pattern Recognition with Deep Neural Networks

The purpose of this project is to investigate self-adaptive forecasting and anomaly prediction algorithms based on deep neural networks (DNNs). DNNs present a compelling technology due to their wide-spread availability through open-source projects (e.g. TensorFlow, MXNet). However, usability of DNNs in scenarios outside of image, speech or text pattern recognition is mostly unproven. This project aims to reduce the knowledge gap that exists in the usage of DNNs in the context of pattern recognition with DNNs in network management and network equipment manufacturing.

Measurement-based Distribution System Models for Distributed Energy Resources Control

The integration of significant capacities of distributed energy resources (DERs) such as renewable wind and solar generation for a more sustainable energy future creates several challenges to the reliable and efficient operation of power distribution systems. These include: (i) Uncertain and intermittent nature of renewable generation compromises power quality for end-customers.  (ii) Up-to-date distribution system network topologies are not well known and their real-time monitoring is limited. As a result, effective management of DERs is challenging.

Autonomous structure detection and inspection using unmanned aerial systems

In this project, a new method is developed to optimize the performance of an Unmanned Aerial Vehicle (UAV) for autonomous detection and on-the-job view-planning of infrastructure elements with the purpose of their accurate three-dimensional (3D) modeling. The existing view-planning approaches in the literature have mostly modeled non-complex or small-scale objects and have rarely been adapted to flying robots. In addition, the target object is often identified by human operators.

Requirements for emulating inertia with voltage-source converters

The research in this proposal examines the growing concern of frequency swings in modern power systems. With the increasing penetration of generation from renewable resources, the share of conventional modes of generation will be diluted and as a result the system’s natural ability to maintain its frequency is diminished. Advanced converter systems may be able to help; however, their ability to do so is limited by several factors such as converter topology and device ratings, among other things.

Passivity Guaranteed Frequency Selective Time Domain Simulation of Very Large Linear Electric Power Systems

In this research, a new approach to efficiently simulate large RLC represent power systems will be introduced and implemented in RTDS. The new approach utilize principle component analysis to search the subspace of state space vectors corresponding to a customer designed frequency band excitations and using projection method to form the reduced order system. Unlike other frequency dependent network equivalent methods, the proposed method reserves all internal information of original system and also inherently guarantees the passivity of the equivalent network.

Quantum Resources Required for GKP Qubits

Qubits are fundamental units for quantum computation. Photonics is a promising physical medium to realize large-scale quantum computation. One proposal to realize photonic qubits was proposed by Gottesman, Kitaev and Preskill (GKP). Here, the logical qubit is encoded into states of a bosonic mode or a quantum harmonic oscillator. It is expected that such a procedure will lead to a better quality and number of qubits.

AI-powered operating system for buildings: new performance metrics

In the real estate sector, a large volume of data is produced by businesses, commercial users and building visitors in a great variety of forms. For instance, three extensive sources of data come from unstructured text (e.g. documents, contracts), numerical data containing resources consumption and sensor/image-type data describing user behavior. A challenging problem for the sector is how to process the generated data into a useful asset that can provide insights to help business decisions, optimize user navigation and automate building-related processes.

Pure-sine GaN-based motor inverter

This project applies wide-bandgap (WBG) transistors to voltage level multiplier module (VLMM) topology in motor inverter applications. It is expected that this approach can yield the benefits of WBG motor inverters (high motor efficiency, fast control response, lower motor torque ripple, close to ideal sinusoidal motor current waveform, smaller filter size, lower cost filter, etc.) while leveraging the benefits of VLMM (lower component cost, high frequency switching only at low voltage, filter-less output signal) to yield a commercially viable highly-efficient pure-sine motor inverter.

3d density estimation using normalizing flows and its application to 3d reconstruction in cryo-EM

Generative models enable the researchers to address multiple problems spanning from noise removal to generating novel samples with properties of the domain. Generative models are commonly studied for images and in this project the idea will be expanded to 3D structures or volumes. Single-particle cryo-electron microscopy (cryo-EM) is a technique to estimate accurate 3D structures of biological molecules which is used by practitioners in fields like precision medicine. This allows them to design drugs that could cure patients with rare diseases and avoid side effects.

Developing Smart Price Forecasting Service for Supply Chain Procurement of Agri-fresh Produce Using Machine Learning

Loblaw Companies Limited (LCL) supplies all fresh produce (FP) to South Western Ontario stores from Waterloo Distribution Center (DC). DC decides prices and quantities to meet FP demand. Timed fair priced orders minimize waste, bring prosperity to growers, consumers and FP trades. Factors affecting prices are highly uncertain due to environmental and socio-economic effects such as income, labor, trade, globalization and climate change which makes price prediction challenging.