Master Theses & Projects (FEAS)


Recent Submissions

Now showing 1 - 20 of 471
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    Modeling and analysis of Regional Haul Steer (RHS) truck tire model
    (2024-04-01) Khosravi, Mehran; El-Gindy, Moustafa; El-Sayegh, Zeinab
    In this thesis, the RHS truck tire size 315/80R22.5 was developed using Finite Element Analysis and several material properties. The tire model was then validated using static and dynamic testing, against physical measurements provided by the manufacturer. A simulation model of flooded and snow terrain was then developed using the Smoothed-Particle Hydrodynamics technique and hydrodynamic elastic-plastic material model. The tire-terrain interaction characteristics were then evaluated over flooded and snow surfaces. The interaction characteristics included the rolling resistance, cornering force, self-aligning moment, and overturning moment. The analysis was performed at different operating conditions including terrain depth, longitudinal speed, and vertical loads. In general, the results from both surfaces exhibited similar trends, even though the values were not the same. Future work involves the utilization of genetic algorithms to generate semi-empirical relationships, as well as the implementation of temperature and wear models for the RHS tire.
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    Autonomous UAV-UGV robot collaboration for exploration and mapping of unknown environments
    (2024-04-01) Khabbaz, Noor; Nokleby, Scott
    This thesis addresses the limitations of existing approaches to autonomous exploration and mapping of unknown environments that use multiple Unmanned Ground Vehicles (UGVs). An Unmanned Aerial Vehicle (UAV) is introduced into the multirobot system to overcome the challenges of relative localization and obstacle detection. A novel method is proposed for autonomously determining the UGVs’ starting poses using ArUco markers visible to the UAV, resulting in the initialization of a global merged map. A second method is developed to overcome UGV obstacle detection limitations. UAV depth camera data is processed to detect and incorporate previously unseen obstacles into the UGVs’ navigation schemes, enabling avoidance. Experimental validation demonstrates the effectiveness of both methods in enhancing system autonomy. The integration of a UAV into multi-robot systems presents a promising solution to address UGVs’ localization challenges and limited field of view to improve their functionality in hazardous environments.
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    Analysis of microgrid with renewable generation and energy storage system
    (2024-03-01) Jadeja, Prachal; Sood, Vijay
    The widespread integration of converter-fed renewable energy sources (RESs) and supported by energy storage systems (ESSs), along with their associated challenges, is causing a pressing need to reconsider the operation of distribution networks. Microgrids (MGs) appear as a practical solution to accommodate these RESs and ESSs. This thesis conducts an in-depth analysis of the design and modeling of a generic MG that integrates RESs and ESSs, incorporating a fuzzy logic controller (FLC)-based energy management system (EMS). The MG operation is evaluated in both grid-connected and islanded modes. A frequency analysis reveals that the point of common coupling (PCC) frequency is controlled within the operational range of ± 0.3 Hz, according to IEEE Standard I547. This control is achieved using a combination of battery and supercapacitor ESS in both operational modes. A comparative evaluation of Proportional Integrator (PI) v/s Proportional Resonant (PR) controllers for current controllers for the MG to reduce the amount of total harmonic distortion (THD) at PCC is conducted. Dynamic testing of MG is also conducted under various loading conditions, encompassing small-, medium-, and large-signal changes. The results prove that the PCC voltages are effectively kept within the typical range of ± 0.05 pu through the FLC-based EMS.
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    Comprehensive integration of safety, optimization, and regulation in ¹⁷⁷Lu-based theranostic radiopharmaceuticals: from production to dose assessment
    (2024-03-01) Haghi, Pardis; Waller, Edward
    In recent decades, medical radioisotopes have transformed cancer diagnosis and treatment. The advent of theranostic radionuclides, enabling both diagnostic and targeted radiotherapy with a single radionuclide, merges two nuclear medicine fields, enhancing personalized targeted radiotherapy through internal dosimetry. Among these agents, the short-lived β-emitter ¹⁷⁷Lu is a promising theranostic agent for various oncological diseases. This research covers advanced production techniques, radiation safety, transportation, Canadian regulations, and internal dose assessment in radiopharmaceutical clinical applications. The study reviews regulations for obtaining a license from the Canadian Nuclear Safety Commission for a radiopharmaceutical therapy facility. It also examines internal dose assessment in nuclear medicine, explaining each step and providing an introduction to common software codes, along with a comparison of commercial software packages. Two case studies compare software codes and internal dose assessment results, emphasizing the ongoing challenge of determining accurate organ-absorbed doses in the RPT process. The ultimate goal is to comprehensively integrate key aspects of ¹⁷⁷Lu targeted therapy into a unified overview, bridging a crucial gap in understanding.
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    A Graph Neural Network for pairwise surrogate modeling in population-based algorithms with tournament selection
    (2024-04-01) Gharavian, Vida; Makrehchi, Masoud; Rahnamayan, Shahryar
    Optimization problems widely arise in various science and engineering fields. Optimisation involves evaluating a candidate solution, which can be computationally intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison. We demonstrated a Graph Neural Network (GNN) to be trained on number of pairs, then utilized to compare a pair of candidate solutions. To examine the efficacy of our model, we utilized the surrogate model on CEC2017 benchmarks in different dimensions. Moreover, the result of surrogate-assisted and none-assisted form of two well-known optimization algorithms were compared. Results show that the proposed method can significantly reduce the computing cost. In the presence of higher dimensions, our model is more effective than most surrogate models for comparison-based optimizers.
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    Requirements engineering-driven collaborative software maintenance framework for embedded systems using continual learning
    (2024-04-01) Fariha, Asma; Alwidian, Sanaa; Azim, Akramul
    Embedded software post-deployment evolutions pose significant threats to the safety and reliability of embedded software if it is not adapted to software maintenance through requirements engineering. To solve this problem, we propose a collaborative framework that enables efficient requirements elicitation and continuously integrates it into maintenance. We designed a requirements forum to enhance elicitation through centralized stakeholder collaboration. This study investigated fault and failure detection in the maintenance phase with continual learning as a mechanism of incremental inclusion. The novel CNNBiLSTM deep-learning model on a public drone dataset outperformed state-of-the-art models, achieving a 100% true positive rate in three scenarios. On the other hand, we experienced a 14% increase in the recall metric for the replay-based method combined with pre-training compared to pre-training when fault detection requirements were integrated incrementally. Our findings support the idea that embedded software safety and security can be greatly enhanced through this collaborative framework.
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    A novel approach for route generation and real-time scheduling for public services
    (2024-04-01) Baghyari, Farhad; Seo, Jaho
    Snowplowing and sweeping are essential services to municipalities, which affect travel safety, environment protection, and health to residents. To provide acceptable quality services, route optimization is one of the key strategies that allow for enhancing efficiency, saving costs, and balancing workloads among operational teams. In order to address this issue and reflect on recent research trends in routing problems that require variable conditions and real-time events, this study proposes two heuristic methods: Smart Selective Navigator and a two-stage algorithm for real-time scheduling and route generation. Through two major case studies — winter operations in the City of Oshawa and autonomous street sweeping in Uchi Park —the proposed methods demonstrate superior performance in generating optimal routes that satisfy complex constraints such as turn restrictions and supply limits and handle real-time events like vehicle breakdowns.
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    Identification of Java lock contention anti-patterns based on run-time performance data using machine learning algorithms
    (2024-02-01) Ahmed, Aritra; Liscano, Ramiro; Azim, Akramul
    Locks are critical in concurrent Java applications, ensuring synchronized access to shared resources. Mismanagement of locks and threads can result in contention, affecting performance and scalability. Eight Java lock contention anti-patterns have been identified, representing various scenarios of lock contention within intrinsic locks. Traditional methods for identifying these faults rely on legacy tools and experience. This study evaluates machine learning algorithms’ effectiveness in detecting lock contention anti-patterns. When the algorithms were trained and tested with a 70-30 split we obtained an accuracy above 90%. To validate the findings we used the Dacapo benchmark as the testing set and found XGBoost performing well among the rest with an overall accuracy of 87%. Logistic Regression, Random Forest and Support Vector Machine were the best performing models in terms of precision and recall values. We also validated our recommendations provided by comparing three important performance metrics between the original and refactored version of the anti-patterns.
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    Development and validation of scaled electric combat vehicle virtual model
    (2023-11-01) Vaz, Glenn Xavier; El-Sayegh, Zeinab
    This research focuses on an 8x8 scaled electric combat vehicle (SECV) and aims to create a virtual model made of the same vehicle on a vehicle dynamics simulation software using parameters from the actual vehicle. In the proposed vehicle, each wheel is independently driven and steered. MATLAB and Simulink software were used to design and implement the electric powertrain while TruckSim Modelling and Simulation software was used to simulate the on-road conditions tests. The simulation data was then compared with the experimental data obtained from the physical test scenarios.
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    Filtering antennas based on rectangular dielectric resonators
    (2023-12-01) Su, Xiangqi; Wang, Ying
    Filters and antennas are essential components in communication systems. Being able to simultaneously realize filter and radiation functions, filtering antennas have attracted much attention due to their compactness and low loss. This thesis focuses on different methods of realizing compact filtering antennas using rectangular dielectric resonator antennas (DRAs). First, four designs of DRA to increase bandwidth have been proposed. Low dielectric constant posts are inserted in DRA in different orientations so that the effective dielectric constant can be readily lowered and adjusted. An integrated design of a waveguide dielectric resonator filter and a DRA with an air post shows significant bandwidth improvement. Next, conducting posts are inserted in the DRA to realize radiation cancelation. Radiation nulls, or filtering responses, have been achieved for both linear polarization and dual polarization. All designs have been simulated and optimized using a full wave electromagnetic (EM) simulator, and results are compared and discussed.
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    A universal centralized protection scheme for feeders of AC microgrids using IEC-61869-9 digital instrument transformers
    (2023-10-01) Sharma, Jigyesh; Sidhu, Tarlochan Singh
    This thesis underscores the escalating importance of microgrids powered by renewable energy sources and inverters in modern power systems. Despite offering significant environmental and economic advantages, their decentralized and dynamic nature poses unique protection challenges. Traditional protection methods struggle to adapt to the diverse conditions within microgrids. Past protection techniques relying on current or voltage detection have limitations affecting system reliability and security. To address this, the thesis proposes a pioneering protection approach based on ’discrepant impedance.’ This concept calculates impedance disparities using both feeder ends’ positive sequence voltage and current measurements. This value approximates zero during normal operation but deviates during a fault, enabling effective fault detection. The proposed protection philosophy was rigorously tested through simulations, real-time experiments using RTDS®, and validation on the IEEE-9 bus system with inverter-based resources, a benchmark system by the National Renewable Energy Laboratory National Renewable Energy Laboratory (NREL). Comparative assessments with traditional methods underscore the effectiveness and adaptability of the discrepant impedance-based protection scheme. The thesis concludes by discussing practical implementation options, showcasing the approach’s versatility across varied microgrid configurations and control strategies, and ultimately demonstrating the feasibility and effectiveness of discrepant impedance-based feeder protection for modern microgrid systems.
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    Electroforming of personalized miniature metal parts using additively manufactured molds
    (2023-12-01) Hamed, Hazem Fawzi; Abou-Ziki, Jana
    In response to evolving manufacturing trends favoring personalized, small-batch production, this thesis centers on the development of additively manufactured molds to facilitate the electroforming of personalized metal parts. The methodology encompasses standardized mold design, experimental procedures for mold development and electroforming, and a simulation model for visualizing and predicting the deposition process. The study provides critical design considerations and guidelines for electroforming within additively manufactured molds, successfully demonstrating the production of composite metal components in 2.5D and 3D configurations. Emphasizing cost efficiency and improved part quality, especially for limited-thickness metal components, the developed technique presents advantages over available metal additive manufacturing processes. Electroforming emerges as a versatile and robust metal additive manufacturing technique, expanding its application beyond traditional limitations of thin-walled hollow structures, 2D components and applications at the nanoscale.
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    Investigation of signal shape effects on the gas film in spark-assisted chemical engraving
    (2023-12-01) Eldiasty, Marwan S; Abou-Ziki, Jana
    Spark-Assisted Chemical Engraving (SACE) is a promising method for machining glass micro-parts and devices. However, intricate control requirements linked to the gas film surrounding the tool present a significant challenge in SACE. While several studies have explored the influence of SACE parameters on the gas film, there exists a literature gap regarding the impact of voltage signal shapes on this film. The thesis fills this void by investigating diverse voltage signal shapes designed to enhance the gas film stability. A robust methodology was established linking gas film properties to investigate the effects of signal shapes on the gas film. The research applied these findings to machining applications, establishing correlations between signal shapes and machining outcomes. Key contributions include a refined methodology for gas film evaluation, advancements in understanding signal shapes’ impact on the process, identification of optimal parameters, and potential improvements in machining through a custom signal shape design.
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    Improving binary optimization algorithms using genuine uniform initialization
    (2023-12-01) Ebrahimi, Sevda; Rahnamayan, Shahryar; Makrehchi, Masoud
    Population-based metaheuristic algorithms play a crucial role in solving complex optimization problems. The effectiveness of these algorithms is significantly influenced by the initial population of candidate solutions. This thesis investigates the critical aspect of initialization in population-based metaheuristic algorithms. This research studies Uniform Covering (UC) binary initialization method as the substitute for the Bit-string Uniform (BU) binary population initialization method for population initialization step in binary optimization algorithms. BU is the most commonly used random binary population initialization method in the literature, however, this research uncovers the adverse impact of employing this approach on binary optimization algorithms. Study in this thesis reveals that UC method is capable of providing gene-wise uniformity and chromosome-wise uniformity simultaneously, however BU method is not capable of providing chromosome-wise uniformity in the population. Monte-Carlo simulation and mathematical proofs are provided to demonstrate the limitations of the BU initialization in providing the diversity and uniformity in population initialization, meanwhile the effectiveness of the UC method is revealed as the alternative method, aiming to enhance algorithm convergence, robustness, and solution quality. In order to illustrate the effect of the BU and UC initialization on binary optimization algorithms, several experiments are conducted on single-objective and multi-objective combinatorial optimization problems including feature selection and knapsack problems using GA and NSGA-II algorithms representative of the binary optimization problems and binary optimization algorithms respectively. The experiments outcome confirm that BU initialization drastically degrade the performance of the algorithms and UC initialization is the proper way for the random binary population initialization.
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    The design and development of a Spark Assisted Chemical Engraving system with force feedback control
    (2023-12-01) Bassyouni, Zahraa Hassan; Abou Ziki, Jana
    Spark Assisted Chemical Engraving is a hybrid micromachining method capable of machining micro-holes and micro-channels on non-conductive substrates. This thesis centers around the design of a mechatronics system for precision manufacturing using SACE technology. The setup consists of a machining head and a processing cell. The electronics of the system are implemented on printed circuit boards and embodied in a well-ventilated box that connects the different components of the system. A current probe adapter that enables the reading of the current signal is designed. The system is modeled and controlled, and a force sensor that can detect machining forces is developed. A force-feedback drilling technique is implemented, where the machining continues with minimal contact forces (less than 200 mN). A preliminary study on characterizing the surface quality of machined holes was conducted, and a model that can characterize the surface texture of machined holes is developed.
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    Poisson Distribution based algorithm to reduce grid harmonics caused by EV chargers
    (2023-12-01) Sapkota, Yadunath; Sood, Vijay
    The global market of electric vehicles (EVs) is growing rapidly because of several factors such as growing concerns for pollution, governmental incentives/subsidies and regulations and consumers’ increasing interest in quiet, pollution-free vehicles. Many countries are determined to sell only EVs from 2035 onwards. With the increase of EVs, fast charging stations need to be installed at many facilities such as universities, shopping malls, hospitals, parking facilities etc. When many EVs start plugging into these facilities to charge, the grid power quality is expected to deteriorate, so it is important that the grid power quality be maintained in the most economical way possible. In this thesis, a new statistics-based algorithm is proposed to reduce the grid harmonics caused by EVs which are essentially non-linear loads. The proposed algorithm helps reduce grid harmonics by phase-shifting the carrier waves of each charging bay within a charging station. When the carrier waves are phase-shifted, the switching transients are distributed over time and some of the generated harmonics will cancel each other. This technique does not require any centralized communication or information exchange between the charging bays to coordinate the carrier waves. As an example, the algorithm is implemented in twenty (20) bays within a charging station by utilizing the Mean and Variance values of the Cumulative Poisson Distribution. This methodology is implemented using MATLAB Simulink platform and the performance is validated. The techniques have demonstrated a 30% improvement in the power quality of the grid voltage.
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    Deep learning models for defect and anomaly detection on industrial surfaces
    (2023-12-01) Saberironaghi, Alireza; Ren, Jing
    Automated quality control is essential across various industries to reduce manual inspection and improve operational efficiency. While there are advances in computer vision and machine learning for defect detection, challenges persist, such as defect variability and the computational burden. This thesis presents specialized deep learning architectures addressing defect classification, segmentation, and detection in textiles, civil engineering, and manufacturing. For textiles, a novel system merges capsule networks with convolutional neural networks and a spatial attention module, achieving a 99.42% accuracy on the TILDA dataset. In civil engineering, the DepthCrackNet model, optimized for pavement crack detection, attains mIoU scores of 77.0% and 83.9% on the Crack500 and DeepCrack datasets. In manufacturing, the E-UNet3+ model for steel defect detection showcases a mIoU score of 86.19% on the SD-saliency-900 dataset. The research's core contribution lies in pioneering deep learning architectures that precisely detect defects across sectors.
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    Enhancing meta-heuristic algorithms using center-based sampling at population level
    (2023-11-01) Khosrowshahli, Rasa; Makrehchi, Masoud; Rahnamayan, Shahryar
    In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of center-based sampling can be utilized at either the operation and/or population levels. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at the operation level requires customizing the strategy for a specific algorithm which degrades the scheme’s generalization. This study proposes a center-based sampling at the population level, which is operation-independent and correspondingly can be embedded in any population-based optimization algorithm. In classic mutation and crossover operators, the number of parents involved is a few, causing ineffective exploration; however, the current proposed center-based sampling uses a multi-parent approach, which results in multiple center-based solutions. In this thesis, two proposed schemes, namely, 1) Clustering center-based sampling and 2) Average ranking center-based sampling, are applied to enhance population-based single- and multi-objective optimization algorithms, respectively, in order to enhance their exploration and exploitation capabilities. The conducted comprehensive center-based experiments are a novel strategy to enhance population based mechanistic algorithms. In order to assess the performance of proposed schemes, the proposed strategy is applied to single- and multi-objective optimization problems and experimented with CEC-2017 benchmark functions. The experimental outcomes confirm that the proposed clustering center-based and ranking center-based samplings have a crucial positive impact on convergence rate of various families of optimization algorithms.
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    Development of an automated industrial painting system with optimized quality and energy consumption
    (2023-10-01) Idrees, Muhammad; Gaber, Hossam
    Paint application is vital for product durability and aesthetics, whether done manually or by precise robotic systems. Manual work is error-prone and risky, while robots offer accuracy. However, programming robot trajectories for diverse products is challenging. Therefore, developing an autonomous system capable of generating automated paint trajectories is desirable. While adequate work has been done to optimize paint trajectories for coating thickness on complex free-form surfaces, the investigation of robot energy consumption and process time in the context of painting is left unattended. Thus, this study focuses on formulation of a hybrid optimization scheme to generate time and energy efficient paint trajectories while ensuring optimal coating deposition on a surface. Moreover, considerable effort is put into the development of hardware and software for the integrated robotic system. Results for the trajectory optimization of a car door, hood, and bumper reveal efficient paint trajectories can be obtained using the proposed optimization scheme.
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    Fault detection design for three-phase voltage source inverter in power train applications
    (2023-12-01) Gong, Lingli; Youssef, Mohamed
    This thesis is dedicated to an extensive exploration of fault detection methods tailored specifically for three-phase inverters, critical components within the propulsion systems of Electric Vehicles (EVs) and drive systems in general. A notable focal point of this study involves the application of advanced signal processing techniques to adeptly identify and diagnose potential faults. Through the signal processing mixed clustering technique, we are able to use the proposed algorithm to compare the reference gate-driving signal with the actual output voltage of the voltage source inverter (VSI) to detect the occurrences of faults. Furthermore, to facilitate this investigative journey, intricate simulation models are thoughtfully crafted utilizing the PSIM software platform. These models not only serve as practical testbeds for the proposed fault detection methods but also enable a thorough analysis and assessment of their efficacy. Some preliminary experimental results are also included to provide proof of principle.