Engineering Commons^™

Surface Pressure Measurements In Translating Tornado-Like Vortices, Aya Kassab, Chowdhury Jubaye, Arash Ashraf, Horia Hangan

Civil and Environmental Engineering Publications

High spatial and temporal surface pressure measurements were carried out in the state-of-the-art tornado simulator, the Wind Engineering, Energy and Environment (WindEEE) Dome, to explore the characteristics of stationary and translating tornado-like vortices (TLV) for a wide range of swirl ratios (𝑆=0.21 to 1.03). The translational speed of the TLV and the surface roughness were varied to examine their effects on tornado ground pressures, wandering, and vortex structure. It was found that wandering is more pronounced at low swirl ratios and has a substantial effect on the peak pressure magnitude for stationary TLV (error percentage ≤ 35%). A new method …

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 …

Investigation Of Human Adipose-Derived Stem-Cell Behavior Using A Cell-Instructive Polydopamine-Coated Gelatin-Alginate Hydrogel., Settimio Pacelli, Aparna R Chakravarti, Saman Modaresi, Siddharth Subham, Kyley Burkey, Cecilia Kurlbaum, Madeline Fang, Christopher A Neal, Adam J Mellott, Aishik Chakraborty, Arghya Paul

Chemical and Biochemical Engineering Publications

Hydrogels can be fabricated and designed to exert direct control over stem cells' adhesion and differentiation. In this study, we have investigated the use of polydopamine (pDA)-treatment as a binding platform for bioactive compounds to create a versatile gelatin-alginate (Gel-Alg) hydrogel for tissue engineering applications. Precisely, pDA was used to modify the surface properties of the hydrogel and better control the adhesion and osteogenic differentiation of human adipose-derived stem cells (hASCs). pDA enabled the adsorption of different types of bioactive molecules, including a model osteoinductive drug (dexamethasone) as well as a model pro-angiogenic peptide (QK). The pDA treatment efficiently retained …

Reliability Models For Smartphone Applications, Sonia Meskini, Ali Bou Nassif, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

Smartphones have become the most used electronic devices. They carry out most of the functionalities of desktops, offering various useful applications that suit the user’s needs. Therefore, instead of the operator, the user has been the main controller of the device and its applications, therefore its reliability has become an emergent requirement. As a first step, based on collected smartphone applications failure data, we investigated and evaluated the efficacy of Software Reliability Growth Models (SRGMs) when applied to these smartphone data in order to check whether they achieve the same accuracy as in the desktop/laptop area. None of the selected …

Experimental Heat Transfer Investigations Of A Double Pipe U-Tube Heat Exchanger Equipped With Twisted Tape And Cut Twisted Tape Internals, Raj Kumar Nayak Maloth, Glen Cletus Dsouza, Swarna Mayee Patra

Mechanical and Materials Engineering Publications

For several decades, the use of heat exchangers for both heating and cooling applications has been established in industries ranging from process to space heating. Out of the various types of heat exchangers, U-tube heat exchangers are preferred owing to their abilities to handle larger flowrates and their simplicity in construction. U-tube exchangers are often equipped with innards of various forms which facilitate higher heat transfer rates and thermal efficiencies. Although higher heat transfer rates have been established with the addition of internals, there is a lack of coherence on the underlying complex physical phenomena such as heat transfer boundary …

Effect Of Non-Uniform Temperature Exposure On The Out-Of-Plane Bending Performance Of Ordinary Laminated Glass Panels, Maged A. Youssef, Ajitanshu Vedrtnam, Chiara Bedon, Shashikant Chaturvedia

Civil and Environmental Engineering Publications

Among the open design issues for structural laminated glass (LG) elements, their mechanical performance during fire exposure stands as a safety concern. This issue is the focus of this paper. An experimental investigation is conducted to evaluate the effect of non-uniform thermal gradients on the out-of-plane bending capacity of standard LG specimens. The pre-fracture, cracking, and post-fracture performances of LG panels were examined using a three-point bending setup. The bending tests were carried out on un-heated LG samples and heated samples, which were either hot or cooled down. The reliability of available analytical and numerical methods to predict the out-of-plane …

Precision Grasp Using An Arm-Hand System As A Hybrid Parallel-Serial System: A Novel Inverse Kinematics Solution, Shuwei Qiu, Shuwei Qiu Ph.D., P.Eng.

Electrical and Computer Engineering Publications

In this letter, we present a novel inverse kinematics (IK) solution for a robotic arm-hand system to achieve precision grasp. This problem is kinematically over-constrained and to address the issue and to solve the problem, we propose a new approach with three key insights. First, we propose a human-inspired thumb-first strategy and consider one finger of the robotic hand as the “thumb” to narrow down the search space and increase the success rate of our algorithm. Second, we formulate the arm-thumb serial chain as a closed chain such that the entire arm-hand system is controlled as a hybrid parallel-serial system. …

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 …

Exploiting The Role Of Nanoparticles For Use In Hydrogel-Based Bioprinting Applications: Concept, Design, And Recent Advances, Aishik Chakraborty, Avinava Roy, Shruthi Polla Ravi, Arghya Paul

Chemical and Biochemical Engineering Publications

Three-dimensional (3D) bioprinting is an emerging tissue engineering approach that aims to develop cell or biomolecule-laden, complex polymeric scaffolds with high precision, using hydrogel-based “bioinks”. Hydrogels are water-swollen, highly crosslinked polymer networks that are soft, quasi-solid, and can support and protect biological materials. However, traditional hydrogels have weak mechanical properties and cannot retain complex structures. They must be reinforced with physical and chemical manipulations to produce a mechanically resilient bioink. Over the past few years, we have witnessed an increased use of nanoparticles and biological moiety-functionalized nanoparticles to fabricate new bioinks. Nanoparticles of varied size, shape, and surface chemistries can …

Ductility And Overstrength Of Shape-Memory-Alloy Reinforced-Concrete Shear Walls, Emad A. Abraik, Maged A. Youssef

Civil and Environmental Engineering Publications

The unique properties of superelastic shape-memory-alloy (SMA) bars have motivated researchers to investigate their use as reinforcing bars for concrete elements. They were found to decrease seismic residual deformations, while increasing seismic inelastic deformations. This characteristic deformation behaviour requires an assessment of the seismic design parameters of SMA reinforced concrete walls. This paper addresses this requirement by evaluating their ductility and overstrength factors. A total of 972 walls were analyzed under a quasi-static lateral load. Suitable values for the overstrength and ductility factors were estimated for two proposed locations of SMA bars. FEMA P695 was then utilized to evaluate the …

Rapid Microscopic Fractional Anisotropy Imaging Via An Optimized Linear Regression Formulation., N J J Arezza, D H Y Tse, C A Baron

Medical Biophysics Publications

Water diffusion anisotropy in the human brain is affected by disease, trauma, and development. Microscopic fractional anisotropy (μFA) is a diffusion MRI (dMRI) metric that can quantify water diffusion anisotropy independent of neuron fiber orientation dispersion. However, there are several different techniques to estimate μFA and few have demonstrated full brain imaging capabilities within clinically viable scan times and resolutions. Here, we present an optimized spherical tensor encoding (STE) technique to acquire μFA directly from the 2nd order cumulant expansion of the powder averaged dMRI signal obtained from direct linear regression (i.e. diffusion kurtosis) which requires fewer powder-averaged signals than …

Dynamic Planning Networks, Norman Tasfi, Miriam A M Capretz

Electrical and Computer Engineering Publications

We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, that combines model-based and model-free aspects for online planning. Our architecture learns to dynamically construct plans using a learned state-transition model by selecting and traversing between simulated states and actions to maximize information before acting. DPN learns to efficiently form plans by expanding a single action conditional state transition at a time instead of exhaustively evaluating each action, reducing the number of state-transitions used during planning. We observe emergent planning patterns in our agent, including classical search methods such as breadth-first and depth-first search. DPN shows improved …

Simplified Structural Analysis Of Framed Ordinary Non-Tempered Glass Panels During Fire Exposure, Amer Sabsabi, Maged A. Youssef, Salah El-Din F. El-Fitiany, Ajitanshu Vedrtnam

Civil and Environmental Engineering Publications

Ordinary non-tempered glass is one of the most widely used materials in the construction industry. Knowing its fire resistance is essential to ensure the safety of emergency personnel as its failure increases the oxygen supply and causes a rapid spread of the fire (flashover phenomenon). Existing approaches for evaluating the structural fire safety of glass façades require expensive experimental tests and/or extensive knowledge of Finite Element modeling. This paper provides a simplified, rational, and reliable approach to assess the structural capacity of ordinary glass panels during fire exposure. A simplified method is developed to predict the temperature difference between …

Arm-Hand Systems As Hybrid Parallel-Serial Systems: A Novel Inverse Kinematics Solution, Shuwei Qiu, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

No abstract provided.

Harnessing The Physicochemical Properties Of Dna As A Multifunctional Biomaterial For Biomedical And Other Applications, Aishik Chakraborty, Shruthi Polla Ravi, Yasmeen Shamiya, Caroline Cui, Arghya Paul

Chemical and Biochemical Engineering Publications

The biological purpose of DNA is to store, replicate, and convey genetic information in cells. Progress in molecular genetics have led to its widespread applications in gene editing, gene therapy, and forensic science. However, in addition to its role as a genetic material, DNA has also emerged as a nongenetic, generic material for diverse biomedical applications. DNA is essentially a natural biopolymer that can be precisely programed by simple chemical modifications to construct materials with desired mechanical, biological, and structural properties. This review critically deciphers the chemical tools and strategies that are currently being employed to harness the nongenetic functions …

Seismic Performance Of Rc Beam-Column Edge Joints Reinforced With Austenite Stainless Steel, C Xu, Moncef L. Nehdi, Maged A. Youssef, L. V. Zhang

Civil and Environmental Engineering Publications

Using stainless steel (SS) reinforcement can mitigate colossal corrosion damage inflicted to reinforced concrete (RC) structures worldwide. However, there is still dearth of studies on the seismic behavior of SS-RC structures. Hence, quasi-static tests were carried out in this study to explore the seismic performance of three RC frame edge joint specimens reinforced with SS having strength grade of 500 and one control RC specimen made with grade 400 normal steel. RC edge frame joints reinforced with ordinary steel and SS exhibited similar bending-shear failure patterns at the beam root. The load bearing capacity of the SS-RC edge fame joint …

Northern Tornadoes Project. Annual Report 2020, Northern Tornadoes Project

Project Reports

NORTHERN TORNADOES PROJECT:

IMPACT AT A GLANCE

Entered into working partnerships with University of Manitoba, York University and The Weather Network

Acquired cutting-edge drone technology, allowing us to obtain high-quality, highly accurate damage survey data and images

Obtained an advanced drone licence, allowing us to fly drones longer distances without keeping the drone in sight

Conducted 409 NTP investigations, 292 Planet satellite surveys, 31 ground surveys, 24 drone surveys and 4 aircraft surveys

Verified the occurrence of 77 tornadoes across Canada in 2020. NTP investigations increased the verified tornado count by 166%

Created a more useful, user-friendly Dashboard and Open …

The Effects Of Customer Segmentation, Borrowers' Behaviours And Analytical Methods On The Performance Of Credit Scoring Models In The Agribusiness Sector, Daniela Lazo, Raffaella Calabrese, Cristian Bravo Roman

Statistical and Actuarial Sciences Publications

The main aim of this study is to analyse the joint effects of customer segmentation, borrowers' characteristics and modelling techniques on the classification accuracy of a scoring model for agribusinesses. To this end, we used data provided by a Chilean company on 161,163 loans from January 2007 to December 2013. We considered random forest, neural network and logistic regression models as analytical methods. Regarding the borrowers' profiles, we examined the effects of socio-demographic, repayment-behaviour, agribusiness-specific and credit-related variables. We also segmented the customers as individuals, SMEs and large holdings. As the segments show different risk behaviours, we obtained a better …

Transfer Learning By Similarity Centred Architecture Evolution For Multiple Residential Load Forecasting, Santiago Gomez-Rosero, Miriam A M Capretz, Syed Mir

Electrical and Computer Engineering Publications

The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could lead to a better understanding of low voltage consumption behaviour. Nevertheless, users with similar characteristics can present diversity in consumption patterns. Consequently, transfer learning methods have become a useful tool to tackle differences among residential time series. This paper proposes a method combining evolutionary algorithms for neural architecture search with transfer learning to perform short term load …

‘Digits’ App - Smartphone Augmented Reality For Hand Telerehabilitation, Hongdao Dong, Edward Ho, Herbert Shin, Tania Banerjee, Geoffrey Masschelein, Jacob Davidson, Sandrine De Ribaupierre, Roy Eagleson, Caitlin Symonette

Electrical and Computer Engineering Publications

Hand telerehabilitation currently has limitations for accurate and remote assessment of range of motion (ROM) in small finger joints. ‘DIGITS’ application utilises the front smartphone camera to measure finger ROM in a reliable and rapid assessment protocol. Our initial beta-phase testing examined the consistency of our software measurements to in-person goniometry. 6 to 9 degrees of difference existed between the smartphone application recorded data versus the in-person measurements. This range is within acceptable 7 to 9 degree tolerance for interrater goniometry measurements. The effect of environmental factors such as hand distance, lightings and hand orientation was evaluated. The intraclass correlation …

Characterization & Calibration Of Foresight Ice, Hareem Nisar, Terry M Peters, Elvis C.S. Chen

Robarts Imaging Publications

No abstract provided.

Real-Time Voluntary Motion Prediction And Parkinson's Tremor Reduction Using Deep Neural Networks, Anas Ibrahim, Yue Zhou, Mary E. Jenkins, Ana Luisa Trejos, Michael D. Naish

Mechanical and Materials Engineering Publications

Wearable tremor suppression devices (WTSD) have been considered as a viable solution to manage parkinsonian tremor. WTSDs showed their ability to improve the quality of life of individuals suffering from parkinsonian tremor, by helping them to perform activities of daily living (ADL). Since parkinsonian tremor has been shown to be nonstationary, nonlinear, and stochastic in nature, the performance of the tremor models used by WTSDs is affected by their inability to adapt to the nonlinear behaviour of tremor. Another drawback that the models have is their limitation to estimate or predict one step ahead, which introduces delay when used in …

Bridge Damage Identification Using Deep Learning-Based Convolutional Neural Networks (Cnns), Sandeep Sony

Civil and Environmental Engineering Publications

In this paper, a novel method is proposed based on a windowed-one-dimensional convolutional neural network for multiclass damage detection using acceleration responses. The data is pre-processed and augmented by extracting samples of windows of the original acceleration time series. 1D CNN is developed to classify the signals in multiple classes. The damage is detected if the predicted classification is one of the indicated damage levels. The damage is quantified using the predicted class probabilities. Various signals from the accelerometers are provided as input to the 1D CNN model, and the resulting class probabilities are used to identify the location of …

A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz

Electrical and Computer Engineering Publications

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent …

Micro-Ct Of The Human Ossicular Chain: Statistical Shape Modeling And Implications For Otologic Surgery, Western University, London Health Sciences Centre

Electrical and Computer Engineering Publications

The ossicular chain is a middle ear structure consisting of the small incus, malleus and stapes bones, which transmit tympanic membrane vibrations caused by sound to the inner ear. Despite being shown to be highly variable in shape, there are very few morphological studies of the ossicles. The objective of this study was to use a large sample of cadaveric ossicles to create a set of three-dimensional models and study their statistical variance. Thirty-three cadaveric temporal bone samples were scanned using micro-computed tomography (μCT) and segmented. Statistical shape models (SSMs) were then made for each ossicle to demonstrate the divergence …

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 …

Pwd-3dnet: A Deep Learning-Based Fully-Automated Segmentation Of Multiple Structures On Temporal Bone Ct Scans, Soodeh Nikan, Kylen Ann Van Osch, Mandolin Li Bartling, Allen Gregory Daniel, Seyed Alireza Rohani, Ben Connors, Sumit Kishore Agrawal, Hanif M. Ladak

Electrical and Computer Engineering Publications

The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among critical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based pipeline …