Engineering Commons^™

The Role Of Transient Vibration Of The Skull On Concussion, Rodrigo Dalvit Carvalho Da Silva

Electronic Thesis and Dissertation Repository

Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to …

Physical Investigation Of Downburst Winds And Applicability To Full Scale Events, Federico Canepa

Electronic Thesis and Dissertation Repository

Thunderstorm winds, i.e. downbursts, are cold descending currents originating from cumulonimbus clouds which, upon the impingement on the ground, spread radially with high intensities. The downdraft phase of the storm and the subsequent radial outflow that is formed can cause major issues for aviation and immense damages to ground-mounted structures. Thunderstorm winds present characteristics completely different from the stationary Gaussian synoptic winds, which largely affect the mid-latitude areas of the globe in the form of extra-tropical cyclones. Downbursts are very localized winds in both space and time. It follows that their statistical investigation, by means of classical full scale anemometric …

Lunar Regolith Simulant Behaviours Affected By Shock Metamorphism And Mineralogy, Xiao Chen Zhang

Electronic Thesis and Dissertation Repository

There are still many gaps in improving the fidelity of lunar regolith simulants to simulate more properties. This study compares some fundamental physical and mineralogical properties of three types of lunar highland regolith simulants: LHS-1, a commercial product with high mineralogical fidelity; UWO-1G, an original simulant that is the main component of LHS-1; and UWO-1S, another original product that is attempted to produce shocked grains in lunar simulants from pulverizing and mixing impact rocks sourced from the Mistastin Crater.

Preliminary results indicated that even though all simulants are composed of mostly plagioclase minerals and have similar particle size distribution patterns, …

Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this paper, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …

Non-Circular Hydraulic Jumps Due To Inclined Jets, Ahmed Mohamed Abdelaziz

Electronic Thesis and Dissertation Repository

When a laminar inclined circular jet impinges on a horizontal surface, it forms a non-circular hydraulic jump governed by a non-axisymmetric flow. In this thesis, we use the boundary-layer and thin-film approaches in the three dimensions to theoretically analyse such flow and the hydraulic jumps produced in such cases. We particularly explore the interplay among inertia, gravity, and the effective inclination angle on the non-axisymmetric flow.

The boundary-layer height is found to show an azimuthal dependence at strong gravity level only; however, the thin film thickness as well as the hydraulic jump profile showed a strong non-axisymmetric behaviour at all …

Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms’ limitations plays a vital role in selecting the …

Ciculant Matrix And Fft, Thomas S. Devries

Undergraduate Student Research Internships Conference

The goal was to produce all the eigen values for a BOHEMIAN matrices using coefficient set {0, 1, -1, i, -i} of a size 15 vector. There are 5^15 eigen values so it was attempted to be done in parrallel for parts of the algorithm that permitted.

Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez

Electronic Thesis and Dissertation Repository

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …

Development Of Advanced Solid-State Electrolytes And Interfaces For High-Performance Sulfide-Based All-Solid-State Lithium Batteries, Feipeng Zhao

Electronic Thesis and Dissertation Repository

All-solid-state lithium batteries (ASSLBs) have become increasingly attractive due to the demand of high-energy-density and high-safety lithium-ion batteries for electric vehicles (EVs). As the core component of ASSLBs, solid-state electrolytes (SSEs) are regarded as essential to determine the electrochemical performance of ASSLBs. The inorganic SSEs is one of the most important categories in all developed SSEs, representing the advance of superionic lithium conductors as well as the cornerstone to construct flexible polymer/inorganic composite SSEs. The sulfide-based inorganic SSE is one of the most promising SSEs that is receiving a lot of attentions, because only sulfide SSEs can show ultrahigh ionic …

Non-Linear Effects Of Solution Parameters And Gamma Radiation On Nickel Oxidation Dynamics, Razieh Karimihaghighi

Electronic Thesis and Dissertation Repository

Nickel is the main component in nickel-based superalloys which are known for their superior corrosion resistance and mechanical properties. These alloys are used in nuclear power plants, mainly for thin-walled heat exchanger tubing where these materials are exposed to a continuous flux of ionizing radiation. The long-term corrosion behaviour of these alloys at high temperatures and in the presence of gamma-radiation is not well understood. Moreover, the mechanism by which nickel improves the corrosion resistance of these alloys is also not known. Therefore, a mechanistic understanding of the nickel oxidation process is required to develop a predictive corrosion model for …

Deep Learning For High-Impedance Fault Detection: Convolutional Autoencoders, Khushwant Rai, Firouz Badrkhani Ajaei, Farnam Hojatpanah, Katarina Grolinger

Electrical and Computer Engineering Publications

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD …

Longitudinal Partitioning Waveform Relaxation Methods For The Analysis Of Transmission Line Circuits, Tarik Menkad

Electronic Thesis and Dissertation Repository

Three research projects are presented in this manuscript. Projects one and two describe two waveform relaxation algorithms (WR) with longitudinal partitioning for the time-domain analysis of transmission line circuits. Project three presents theoretical results about the convergence of WR for chains of general circuits.

The first WR algorithm uses a assignment-partition procedure that relies on inserting external series combinations of positive and negative resistances into the circuit to control the speed of convergence of the algorithm. The convergence of the subsequent WR method is examined, and fast convergence is cast as a generic optimization problem in the frequency-domain. An automatic …

Analytical And Computational Modelling Of The Ranque-Hilsch Vortex Tube, Nolan J. Dyck

Electronic Thesis and Dissertation Repository

The Ranque-Hilsch vortex tube (RHVT) is a simple mechanical device with no moving parts capable of separating a supply of compressed fluid into hot and cold streams through a process called temperature separation. The overall aim is to develop models which can be used to assess the temperature separation mechanisms in the RHVT, leading to a better overall understanding of the underlying physics. The introductory chapter contains a thermodynamic analysis and introduction to the flow physics, alongside three miniature literature reviews and critiques identifying research gaps.

The body of the thesis contains three articles. The first article studies the flow …

Cfd Simulations Of Bubble Column Equipped With Bundles Of Concentric Tubes, Glen C. Dsouza

Electronic Thesis and Dissertation Repository

Bubble column reactors are multiphase contactors that have found several industrial applications owing to various attractive features including excellent thermal management, low maintenance cost due to simple construction and absence of moving parts. In order to attain desired performance for a given application, these reactors are usually equipped with internals such as vertical tube bundles to facilitate heat transfer. The column hydrodynamics and turbulence parameters are altered when the column is occluded with internals which adds to the complexity of the problem. The use of Computational Fluid Dynamics (CFD) tools for the study of multiphase flows has gained a lot …

Simulation Approaches To X-Ray C-Arm-Based Interventions, Daniel R. Allen

Electronic Thesis and Dissertation Repository

Mobile C-Arm systems have enabled interventional spine procedures, such as facet joint injections, to be performed minimally-invasively under X-ray or fluoroscopy guidance. The downside to these procedures is the radiation exposure the patient and medical staff are subject to, which can vary greatly depending on the procedure as well as the skill and experience of the team. Standard training methods for these procedures involve the use of a physical C-Arm with real X-rays training on either cadavers or via an apprenticeship-based program. Many guidance systems have been proposed in the literature which aim to reduce the amount of radiation exposure …

Electronic And Local Structures Of Pt-Based Bimetallic Alloy And Core-Shell Systems, Jiatang Chen

Electronic Thesis and Dissertation Repository

This thesis investigates the electronic structure of Pt for catalysis applications. The importance of the Pt 5d band is discussed in terms of the bonding capability of Pt. The oxygen reduction reaction in proton exchange membrane fuel cells is chosen as the catalytic reaction model to illustrate the effect of Pt 5d states on Pt-O interaction. Pt-based bimetallic systems are introduced as a solution for the high price and limited resources of Pt. Despite lower usage of Pt, the tuning capability to optimize the Pt 5d band in bimetallic catalysts is supposed to provide superior catalytic activity. Advanced synchrotron X-ray …

Nonlinear Dynamics Of Carbon Steel Corrosion Under Gamma Radiation, Youn Gyeong Shin

Electronic Thesis and Dissertation Repository

Corrosion of materials is still an unresolved problem affecting a multitude of industries. One of the grand challenges facing the corrosion community is the development of high-fidelity models for corrosion in actual service environments. The difficulties arise since corrosion involves transfer of metal atoms between the solid and solution phases thus making the system non-adiabatic. Interfacial transfer of atoms increases the chance of establishing systemic feedback between chemical reactions and transport processes, which results in chemical oscillation and periodic patterns on the corroding surfaces. These oscillating behavior in electrochemical measurements and pattern formation on corroding surfaces have been reported in …

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Water Supply Capacity Development In The Context Of Global Change, Patrick Breach

Electronic Thesis and Dissertation Repository

The ANEMI model is an integrated assessment model of global change that emphasizes the role of water resources. The model is based on the principles of system dynamics simulation in order to analyze changes in the Earth system using feedback processes. Securing water resources for the future is a key issue of global change, and ties into global systems of population growth, climate change carbon cycle, hydrologic cycle, economy, energy production, land use and pollution generation.

This thesis focusses on the development of global water supplies necessary to keep pace with a growing population and global economy using an integrated …

Quantification Of Septic System Contribution To Nutrient Loads In Surface Waters, Archana Tamang

Electronic Thesis and Dissertation Repository

Freshwater systems worldwide are threatened by excessive nutrient (nitrogen and phosphorus) loading. This study evaluated the contribution of septic systems to stream nutrient loads in nine subwatersheds. Stream sampling was conducted during low and high discharge conditions, with samples analyzed for total phosphorus (TP), soluble reactive phosphorus (SRP), nitrate (NO₃-N), and acesulfame (ACE; wastewater tracer). Higher septic effluent reached the subwatershed outlets during high discharge conditions. Subwatersheds with newer households had a lower percentage of septic effluent reaching the stream compared with subwatersheds with older households. Seasonal and event-based ACE concentration-discharge relationships revealed that the hydrological pathways delivering …

Northern Tornadoes Project 2018/19 Report, Northern Tornadoes Project

Project Reports

Three years ago, we set our sights on finding at least a few undocumented tornado tracks in the remote forests of northern Ontario.

We have covered greater distances and nurtured bigger ambitions since then.

From northern Ontario to all of Canada, from aircraft surveys to drones and satellites, from on-the- ground damage investigations to artificial intelligence analyses, the Northern Tornadoes Project is one of the most comprehensive tornado research projects in the country. It aims to better detect tornado occurrences throughout Canada, improve communication of tornado science and risk, and mitigate against harm to people and property. NTP also seeks …

A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki

Electronic Thesis and Dissertation Repository

Through social media platforms, massive amounts of data are being produced. Twitter, as one such platform, enables users to post “tweets” on an unprecedented scale. Once analyzed by machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. This thesis describes a visual analytics system (i.e., a tool that combines data visualization, human-data interaction, and …

Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma

Electrical and Computer Engineering Publications

No abstract provided.

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …

High-Pressure Studies On The Transition From Red Phosphorus To Black Phosphorus, Heng Xiang

Electronic Thesis and Dissertation Repository

Black phosphorus (BP) is a promising material in many research fields. However, the transition process from amorphous red phosphorus (ARP) is elusive and hence hinders large scale synthesis and applications. This work describes the application of the high-pressure method to study the transition process from ARP to BP.

In this thesis, the following three objectives were achieved: (1) to understand the mechanism of the transition, (2) to facilitate the synthesis of BP by taking the advantage of less pure ARP, (3) to propose new methods of synthesizing BP-based materials, such as the moderately oxidized BP and the black phosphorus/ amorphous …

Numerical And Semi-Analytical Estimation Of Convective Heat Transfer Coefficient For Buildings In An Urban-Like Setting, Anwar Demsis Awol

Electronic Thesis and Dissertation Repository

Urban building arrangements such as packing density, orientation and size are known to influence the microclimate surrounding each building. Studies on the impact of urban microclimatic changes on convective heat transfer coefficient (CHTC) from a stock of buildings, however, have been rare in surveyed literature. The present study focuses on numerical and analytical investigation of CHTC from building-like models with homogeneous set of equal and unequal planar and frontal densities. Consequently, the study discusses the CHTC response in relation to broader changes in the urban surface form. Part of the process involves the development of a simplified one-dimensional semi-analytical CHTC …

Characterization And Computational Modelling For The Garnet Oxide Solid State Electrolyte Ta-Llzo, Colin A. Versnick

Electronic Thesis and Dissertation Repository

The all-solid-state-battery (ASSB) serves as a promising candidate for next generation lithium ion batteries for significant improvements in battery safety, capacity, and longevity. Of the material candidates researched to replace the conventionally used liquid electrolyte, the garnet oxide Ta-LLZO (Li_6.4La₃Zr_1.4Ta_0.6O₁₂) has received much attention thanks to its high chemical and electrochemical stability, and ionic conductivity which rivals that of liquid electrolytes. While much investigation has taken place regarding the electrochemical performance of Ta-LLZO, much less is known about the micromechanics, including microstructural characterization, stress and strain development, and material failure …

High Strain Dynamic Test On Helical Piles: Analytical And Numerical Investigations, Mohammed Fahad Alwalan

Electronic Thesis and Dissertation Repository

Helical piles are currently considered a preferred foundation option in a wide range of engineering projects to provide high compressive and uplift resistance to static and dynamic loads. In view of the large capacity of large diameter helical piles, there is a need to determine their capacity using accurate and economically feasible testing techniques. The capacity of piles is usually determined by conducting a Static Load Test (SLT). However, the SLT can be costly and time consuming, especially for large capacity piles. The High Strain Dynamic Load Test (HSDT) evaluates the pile capacity using dynamic measurements generated through subjecting the …