Spring 2023: OU Engineering Presents Dissertation Excellence Awards

Ten students from the Gallogly College of Engineering at the University of Oklahoma were selected to receive this semester’s Engineering Dissertation Award, a $5,000 award created to encourage doctoral students to graduate with excellence. The award helps scholars who are near completion of their Ph.D., says Zahed Siddique, the college’s associate dean for research who heads the committee. 

Established in 2018, the Engineering Dissertation Award is made possible by the Thomas Ira Brown, Jr. Endowed Scholarship. Brown (1926-2016) created a new market for electronic control of industrial gas turbines. He earned a bachelor’s degree in electric engineering from OU in 1950.

Spring 2023 recipients, along with their OU advisers, are:

Manjurul Ahsan, School of Industrial and Systems Engineering, recommended by Shivakumar Raman, Ph.D. 

Topic: “Data Balancing Approaches in Quality, Defect, and Pattern Analysis”

Research: The imbalanced ratio of data is one of the most significant challenges in various industrial domains. Consequently, numerous data-balancing approaches have been proposed over the years. However, most of these data-balancing methods come with their own limitations that can potentially impact data-driven decision-making models in critical sectors such as product quality assurance, manufacturing defect identification, and pattern recognition in healthcare diagnostics. This dissertation addresses three research questions related to data-balancing approaches: 1) What are the scopes of data-balancing approaches toward the major and minor samples? 2) What is the effect of traditional Machine Learning (ML) and Synthetic Minority Over-sampling Technique (SMOTE)-based data-balancing on imbalanced data analysis? and 3) How does imbalanced data affect the performance of Deep Learning (DL)-based models?  

Gökhan Ariturk, School of Electrical and Computer Engineering, recommended by Hjalti Sigmarsson, Ph.D.

Topic: "Microwave Filters for Next Generation Radio Frequency Transceivers"

Javad Asadi, School of Aerospace and Mechanical Engineering, recommended by Pejman Kazempoor, Ph.D., and Iman Ghamarian, Ph.D. 

Topic: “Flexible Design and Operation of Carbon Capture Technologies for Optimal Integration with Low-Carbon Power Generation System.”

Research: Renewable energy sources and fossil-fueled power plants integrated with carbon capture technologies are expected to play an important role in sustainable energy supply in the future. The flexible operation of carbon capture systems (CCSs) has received considerable attention due to the increasing penetration of intermittent renewable sources. This research aims to propose a novel flexible design by hybridization of membrane-based and amine-based CO2 capture systems integrated with the solar field as a promising technology for integration into natural gas combined cycle power plants. Accordingly, a comprehensive techno-economic assessment of several system designs as well as the dynamic behavior of the process have been studied to investigate the potential and viability of such systems as flexible CCS technology. Rigorous steady-state and dynamic models of the proposed CCS process have been developed that involve lumped parameter models for the balance of plants and a mechanistic model for analyzing process performance. Rigorous rate-based modeling is applied for the simulation of amine-based CCS that is integrated with renewable energy resources to provide the required thermal energy for the stripper reboiler and reduce the energy penalty of the power plant. This research provides new insights into the flexible design and operation of CCS and the dynamics analysis of the process. The results of this research are fundamental and provide new knowledge for process designers in the field of integration of carbon capture systems with a fossil-fueled power plant.

Elton Lima Correia, School of Chemical, Biological and Materials Engineering, recommended by Dimitrios Papavassiliou, Ph.D., and Sepideh Razavi, Ph.D. 

Topic: “Synergistic effects of particles and surfactants at fluid-fluid interfaces”

Research: My work is dedicated to probing the role different surface-active species (i.e., surfactants and nanoparticles) play in resulting interfacial phenomena such as surface tension/pressure, mechanical properties when subjected to compressional/dilational or shear stresses, and stability of multicomponent fluidic systems. We investigate various features that can be employed in tuning the competitive vs. synergistic behavior of species that make up the system and determine the role non-idealities play in tuning the properties of the populated interface and the stability of the interfacial systems. The knowledge base achieved from this Ph.D. work will equip us with a fundamental understanding of the critical attributes in the design and engineering of solutions to address real-life challenges in a broad range of applications from wastewater remediation to increasing the shelf-life of a product. 

Grant Graves, School of Civil Engineering and Environmental Science, recommended by Thomas Neeson, Ph.D., K.A. Strevett, Ph.D., and Jason Vogel, Ph.D. 

Topic: “Freshwater Stream Monitoring Process Improvements for Fecal Indicator Impairment Designation”

Research: Recreational water quality standards for freshwater streams and rivers are important to understand the potential human health risks associated with primary body contact recreation. Fecal indicator bacteria (FIB), Enterococcus and Escherichia coli, are used to routinely monitor and assess waterbodies for impairment. The 2020 Clean Water Act 303(d) Integrated Report indicates that approximately 7,500 miles of streams and rivers are impaired for both E. coli and Enterococcus in Oklahoma. FIB sources are often difficult to assess as they are from numerous anthropogenic, wildlife and environmental non-point sources and require consistent monitoring and assessment due to potential dynamic spatial and temporal factors within streams. The Oklahoma water quality standards provide threshold criteria and a general sampling frequency for FIB to make an impairment assessment but do not provide a detailed process for how, when, or where water samples should be collected. Therefore, the objectives of this dissertation were to 1) evaluate spatial and temporal factors in Oklahoma streams that may influence FIB, 2) investigate stream sediment as a contributing factor to Enterococcus in streams and rivers, 3) evaluate the fluorogenic substrate enumeration method for applicability to analyze freshwater stream samples for Enterococcus, and 4) explore existing geospatial and water quality data to develop correlation factors and regression equations to improve prediction of FIB for monitoring and assessment. The outcomes of this work indicate that more emphasis should be placed on the evaluation of the sampling process design and methodology for assessing Oklahoma streams for FIB impairment determination.

Marvin Manalastas, School of Electrical and Computer Engineering, recommended by Ali Imran, Ph.D., and Fahd Khan, Ph.D. 

Topic: “Addressing Mobility Challenges in an AI-enabled Emerging Cellular Networks”

Research: The cellular network industry has undergone significant evolution in recent years, but mobility management is increasingly becoming a major challenge due to factors such as densified base station deployment, diverse use cases, and evolved network architecture. If not properly addressed, the potential inefficiencies in mobility management in emerging cellular networks could be a significant issue. However, developing an effective mobility management scheme is challenging. One of the challenges is the lack of suitable tools to investigate mobility due to various reasons, including the complexity, computational efficiency, and realism of these tools. In my dissertation, I propose a tri-pronged approach to address this challenge, including developing a simulator, deploying an experimental testbed, and proposing a machine learning-based handover parameter optimization framework. Additionally, my dissertation addresses the challenge of reducing inter-frequency handover failures through a novel data-driven solution called TORIS and proposes a novel user-specific parameter to optimize handover performance for emerging cellular networks.

Sanjana Mudduluru, School of Computer Science, recommended by Dean Hougen, Ph.D. 

Topics: “Developing and Applying Hybrid Deep Learning Models for Computer-aided Diagnosis of Medical Image Data”

Research: The thesis discusses three methods to address the challenges of applying deep learning models to medical imaging. The first method involves the development of a new joint deep learning model, J-Net, to achieve lesion segmentation and classification simultaneously. The J-Net model outperforms the individual models in accuracy with small datasets. The second method performs automatic image detection using a two-stage deep learning model to produce clean data. The third method involves developing multi-stage deep learning algorithms to generate synthetic medical image data, which can be used to overcome the lack of large, diverse datasets. These methods demonstrate that building enhanced training datasets can play a vital role in improving the performance of deep-learning models in medical imaging applications. 

Mohammad Mukhtaruzzaman, School of Computer Science, recommended by Mohammed Atiquzzaman, Ph.D. 

Topic: “Stable dynamic feedback-based predictive clustering protocol for vehicular ad hoc networks"

Research: Scalability presents a significant challenge in vehicular communication, particularly when there is no hierarchical structure in place to manage the increasing number of vehicles. As the number of vehicles increases, they may encounter the broadcast storm problem, which can cause network congestion and reduce communication efficiency. Clustering can solve these issues, but due to high vehicle mobility, clustering in vehicular ad hoc networks (VANET) suffers from stability issues. Existing clustering algorithms are optimized for either cluster head or member, and for highways or intersections. The lack of intelligent use of mobility parameters like velocity, acceleration, direction, position, distance, degree of vehicles, and movement at intersections, also contributes to cluster stability problems. A dynamic clustering algorithm that efficiently utilizes all mobility parameters can resolve these issues in VANETs. To provide higher stability in VANET clustering, a novel robust and dynamic mobility-based clustering algorithm called junction-based clustering protocol for VANET (JCV) is proposed in this dissertation. Unlike previous studies, JCV takes into account position, distance, movement at the junction, degree of a vehicle, and time spent on the road to select the cluster head. JCV considers transmission range, the moving direction of the vehicle at the next junction, and vehicle density in the creation of a cluster. JCV's performance is compared with two existing VANET clustering protocols in terms of the average cluster head duration, the average cluster member duration, the average number of cluster head changes, and the percentage of vehicles participating in the clustering process, etc. To evaluate the performance of JCV, we developed a new cloud-based VANET simulator (CVANETSIM). The simulation results show that JCV outperforms the existing algorithms and achieves better stability in terms of the average CH duration (4%), the average CM duration (8%), the number of CM (6%), the ratio of CM (22%), the average CH change rate (14%), the number of CH (10%), the number of non-cluster vehicles (7%), and clustering overhead (35%). The dissertation also introduced a stable dynamic feedback-based predictive clustering (SDPC) protocol for VANET, which ensures cluster stability in both highway and intersection scenarios, irrespective of the road topology. SDPC considers vehicle relative velocity, acceleration, position, distance, transmission range, moving direction at the intersection, and vehicle density to create a cluster. 

Vi Nguyen, School of Chemical, Biological and Materials Engineering, recommended by Dimitrios Papavassiliou, Ph.D., and Alberto Striolo, Ph.D. 

Topic: “Transport of nanoparticles in porous media by numerical simulations”

Research: The movement of nanoparticles (NPs) through porous materials has diverse practical uses in environmental and industrial fields, including the spread of pollutants in underground water sources, the distribution of injected chemicals or surfactants in oil reservoirs, the flow of chemicals in packed bed reactors, and the delivery of nutrients through scaffolds in bioreactors. My area of research concerns the distribution of fluid velocity and the hydrodynamic dispersivity of NPs in porous media. Furthermore, the mobility of NPs in porous media is significantly impacted by their tendency to aggregate. To address this, I have developed a model that can simulate the aggregation process and assess the morphology of the resulting aggregates in confined geometries.

Vy Nguyen, School of Chemical, Biological and Materials Engineering, recommended by Bin Wang, Ph.D. 

Topic: “Nature and Catalytic Role of Extra-Framework Aluminum and Partially Coordinated Aluminum in MFI Zeolite”

Research:  Zeolites are extensively employed in the chemical and petroleum industries as an acid catalyst for cracking, isomerization, and alkylation reactions. Recently, they have also gained attention as potential catalysts for biomass conversion. The activity of Brønsted acid sites (BAS) contained within the microporous zeolite channels is known to be affected by the location and local environment that stabilizes reaction intermediates and transition states. Additionally, it has been reported that extra-framework Al (EFAL) and partially coordinated Al (PFAL) species can significantly alter reaction rates by modifying the environment around active sites. However, the mechanism behind these enhancements and the catalytic roles of individual EFAL and PFAL species are yet to be fully understood. In this project, we utilized Density Functional Theory (DFT) to elucidate the locations and local environments of active sites in H-ZSM-5. We also propose the mechanisms behind the synergistic effects generated by the BAS and EFAL/PFAL in proximity. Moreover, the individual catalytic effects of different EFAL and PFAL on alkane cracking reactions were investigated.

Alireza Rangrazjeddi, School of Industrial and Systems Engineering, recommended by Kash Barker, Ph.D. and Andrés González, Ph.D.

Topic: “Game Theoretic Algorithms for Decentralized Decision-Making” 

Research: Traditional viewpoints on the decision-making environment have been centered on a top-down perspective, guided by a hierarchy in which only one entity may establish desirable criteria and pursue them while considering others to be quiet agents in the system, merely obeying instructions and judgments taken. Many research studies and mathematical formulations have been developed with this centralized approach in mind. Although systems in the past were not entirely centralized in architecture, researchers assumed this simplification because systems were less complex and with fewer interdependent relationships. Therefore, considering a centralized mathematical formulation describing such systems' behavior was sufficient. However, today's complex systems, which have a very high degree of interconnectedness, exhibit distinct dynamics as subsystems are increasingly dependent on one another. Additionally, considering the free market environment, which encourages reserving government function to the private sector, the number of decision-makers with distinguished interests and profiteering behavior that can affect the system has increased. Therefore, two types of interdependency can be realized in dealing with today's complex systems: (i) physical interdependency among the systems, and (ii) interdependency among decisions made by the decision-makers in the system. Focusing on these two interdependency types, this research proposes algorithms incorporating mathematical formulations addressing decentralized decision-making environments considering interdependent complex systems. 

Chenguang Xu, School of Computer Science, recommended by Christopher Weaver, Ph.D.

Topic: “Statistical anomaly discovery through visualization”

Research: Developing a deep understanding of data is a crucial part of decision-making processes. It often takes substantial time and effort to develop a solid understanding to make well-informed decisions. Data analysts often perform statistical analyses through visualization to develop such understanding. However, applicable insight can be difficult due to biases and anomalies in data. An often overlooked phenomenon is mixed effects, in which subgroups of data exhibit patterns opposite to the data as a whole. This phenomenon is widespread and often leads inexperienced analysts to draw contradictory conclusions. Discovering such anomalies in data becomes challenging as data continue to grow in volume, dimensionality and cardinality. Sometimes, we are faced with the dilemma of choosing between statistical analysis and visualization for studying such paradoxical anomalies. This research explores various approaches to bridge the gap between statistical analysis and visualization for discovering and examining anomalies in multidimensional data. I start with an automatic anomaly detection method based on the correlation comparison and perform experiments to learn the running time and the complexity of my algorithm. Subsequently, I investigate the design, development and implementation of a series of visualization techniques to keep pace with the upgrade of the analytical procedure. An interactive visual analysis system, Wiggum, is created to reveal various forms of mixed effects. A user study to evaluate Wiggum strengthens understanding of the factors that contribute to comprehending the statistical concepts. Furthermore, a conceptual framework, visualization correspondence, is presented to improve the usability of visualizations. It is practical to build visualizations with highly coherent views by visual correspondence theory. As a result, we present a hybrid tree visualization, PatternTree, which applies the visual correspondence theory. PatternTree supports users to more readily discover statistical anomalies and explore their relationships.

Compiled by Lorene A. Roberson, Gallogly College of Engineering

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