Engineering Commons^™

Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill

Theses and Dissertations

This dissertation investigates the construction, optimization, and application of quaternion neural networks (QNNs) to Department of Defense (DoD) related problem sets. QNNs are a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. This work provides a critical evaluation of the myriad different quaternion backpropagation derivations that exist in the literature, testing the performance of each on a range of regression problem sets. The optimization dynamics of QNNs are explored, presenting visualizations of QNN loss surfaces and a novel method for assessing the “smoothness” of these loss surfaces. Finally, this dissertation presents …

Evaluating The Chief Of Staff Of The Air Force 2016 Initiative To Revitalize The Squadron: A Thematic Content Analysis Of Appreciative Inquiry Mechanisms, John M. Huntz

Theses and Dissertations

A thorough thematic analysis and literature review were undertaken to understand better the integration of AI mechanisms within the Revitalize the Squadron initiative. To facilitate the initiative's implementation, the aim is to provide commanders with practical instances, dimensions, findings, and results. Throughout the coding process, instances of AI’s mechanisms were discovered in the literature. The link between PE and HQR boosted the overall vitality within the squadron, where vitality was determined to be the goal. AI, as a whole, was not found in the literature, but the analysis determined that the Revitalize the Squadron initiative was “Appreciative” in nature.

Improving Deep Reinforcement Learning Methodology For Autonomous Defense And Escort Of Military High-Value Assets, Joseph Liles Iv

Theses and Dissertations

This dissertation explores the application of machine learning to the control of autonomous unmanned combat aerial vehicles (AUCAVs). In particular, this research applies deep reinforcement learning methodologies to a defensive air combat scenario wherein a fleet of AUCAVs protects a military high-value asset (HVA). A collection of air battle management scenarios along with an original simulation environment and a set of designed computational experiments support the approximation of high-quality decision policies by employing Markov decision processes, approximate dynamic programming algorithms, and deep neural networks for value function approximation.

Test Problem Generation And Metaheuristic Selection For The Multidemand Multidimensional Knapsack Problem, Matthew E. Scherer

Theses and Dissertations

This work focuses on instance generation methods for the multi-demand multidimensional knapsack problem (MDMKP). Specifically, instance space analysis (ISA) is used to characterize the landscape of existing instances and validate the novelty of new instances generated with a novel problem generation method, the primal problem instance generator (PPIG). The instance generator is capable of producing feasible, diverse, and challenging instances by directly controlling the problem features. PPIG contributes to the previous collections of instances and is validated through instance space analysis. The research presents an in-depth empirical evaluation of existing solution procedures for the MDMKP. The portfolio of metaheuristics examined …

Network Vulnerability Identification For The Material Routing Problem, Carson G. Long

Theses and Dissertations

This dissertation considers the importance of identifying spatiotemporal vulnerabilities in ground distribution networks and uses operations research methods to formulate models that allow military logistic planners to implement prevention and mitigation measures regarding the routing of personnel, equipment, and supplies in contested Areas of Responsibility (AOR). For optimization models relating to identifying spatiotemporal network vulnerabilities in distribution networks, this work leverages game theory, mixed-integer programming, multi-objective optimization, and metaheuristics to inform mitigation measures for shipment routing. This research has three related components: the first component develops a multi-objective mathematical program to identify spatiotemporal vulnerabilities via myopic heuristic identification, in combination …

An Analytical Framework For Photonic Storage And Retrieval: Implications For Quantum Optical Networks, Billie V. Deluca

Theses and Dissertations

Quantum information and communication is the next revolution in information technology. However, communication through quantum networks will require the ability to transmit, store, and interact with photonic quantum bits (qubits). The focus of this dissertation is a theoretical description of a promising technology for storage of photonic states employing electromagnetically induced transparency (EIT). A general semiclassical model for EIT photonic storage will be developed, a key capability required for understanding the effect of storage that is independent of the photonic state being stored. This dissertation demonstrates the capability of this model to predict light pulses retrieved from EIT storage as …

Non-Planar 3d Printing Of Thermosetting Prepolymers, Junaid Abid Ali

Master's Theses

Additive manufacturing or three-dimensional (3D) printing is an advanced process of manufacturing that challenges traditional manufacturing methodologies. However, the current 3D printing process does exhibit specific limitations. It generates printing paths that are in a horizontal plane, proving inadequate for printing curved or non-flat structures. This study established a printing procedure capable of simultaneously executing printing in X, Y, and Z directions. The limitations posed by conventional 3D printing lead to the poor surface quality of curved printed objects. In contrast, non-planar 3D printing provides smoother surfaces. This work developed a program capable of producing non-planar print paths for the …

Challenges And Solutions For Autonomous Ground Robot Scene Understanding And Navigation In Unstructured Outdoor Environments: A Review, Liyana Wijayathunga, Alexander Rassau, Douglas Chai

Research outputs 2022 to 2026

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a …

Low-Frequency Laboratory Measurements Of The Elastic Properties Of Solids Using A Distributed Acoustic Sensing System, Vassily Mikhaltsevitch, Maxim Lebedev, Roman Pevzner, Alexey Yurikov, Konstantin Tertyshnikov

Research outputs 2022 to 2026

In recent decades, low-frequency (LF) experiments based on the forced-oscillation (FO) method have become common practice in many rock physics laboratories for measuring the elastic and anelastic properties of rocks. However, the use of the electronic displacement sensors in traditional acquisition systems of FO devices such as conventional capacitive transducers or strain gauges seriously limits both the efficiency and productivity of LF measurements, and, due to the limited contact area of the displacement sensors with a sample under test, increases the requirements for sample homogeneity. In this paper, we present the first results obtained in the development of a new …

Modeling The Moisture Content And Drying Rate Of Zucchini (Cucurbita Pepo L.) In A Solar Hybrid Dryer Using Ann And Anfis Methods, Halil Nusret Bulus, Aytac Moralar, Soner Celen

The Philippine Agricultural Scientist

Estimating product drying kinetics is critical to obtain the best drying process without compromising product quality and necessitates the development of numerical drying models. This research aims to compare the prediction models developed using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS), two popular machine learning approaches in the recent years. Zucchini slices were chosen as samples and dried in a solar-assisted microwave belt dryer at 0.245 m/min belt speed and microwave powers of 0.7, 1, and 1.4 kW. On the data set obtained by computing the moisture content and drying rate values, prediction models were developed …

Open Source Framework For A Broadly Expandable And Reconfigurable Data Acquisition And Automation Device (Bread), Shane Oberloier, Nicholas G. Whisman, Finn Hafting, Joshua M. Pearce

Michigan Tech Publications

Though open source data acquisition (DAQ) systems have been published, closed source proprietary systems are the standard despite often being prohibitively expensive. High costs, however, limit access to high-quality DAQ in low-resource settings. In many cases the functions executed by the closed source and proprietary DAQ cards could be carried out by an open source alternative; however, as desired function count increases, the simplicity of integrating the designs decreases substantially. Although the global library of open source electronic designs is expanding rapidly, and there is clear evidence they can reduce costs for scientists one device at a time, they are …

Antibiotic Resistance In Urban Stormwater: A Review Of The Dissemination Of Resistance Elements, Their Impact, And Management Opportunities, Kassidy N. O'Malley, Walter M. Mcdonald, Patrick J. Mcnamara

Civil and Environmental Engineering Faculty Research and Publications

The public health crisis of antibiotic resistance is a growing threat across the world that is only expected to intensify in the coming years. The cycling of antibiotic resistance genes (ARGs) in the environment via urban stormwater runoff is one means by which humans are exposed to resistant bacteria as traditional gray stormwater infrastructure facilitates the transport of resistance elements into water bodies utilized by the public. In this review, existing research on the occurrence of ARGs in urban stormwater runoff is critically reviewed with the goal of determining the role of stormwater in the dissemination and development of antibiotic …

Impact Of Corrosion Inhibitors On Antibiotic Resistance, Metal Resistance, And Microbial Communities In Drinking Water, Lee K. Kimbell, Emily Lou Lamartina, Stan Kohls, Yin Wang, Ryan J. Newton, Patrick J. Mcnamara

Civil and Environmental Engineering Faculty Research and Publications

Corrosion inhibitors, including zinc orthophosphate, sodium orthophosphate, and sodium silicate, are commonly used to prevent the corrosion of drinking water infrastructure. Metals such as zinc are known stressors for antibiotic resistance selection, and phosphates can increase microbial growth in drinking water distribution systems (DWDS). Yet, the influence of corrosion inhibitor type on antimicrobial resistance in DWDS is unknown. Here, we show that sodium silicates can decrease antibiotic resistant bacteria (ARB) and antibiotic-resistance genes (ARGs), while zinc orthophosphate increases ARB and ARGs in source water microbial communities. Based on controlled bench-scale studies, zinc orthophosphate addition significantly increased the abundance of ARB …

Uncertainties In Retrieval Of Remote Sensing Reflectance From Ocean Color Satellite Observations, Eder I. Herrera Estrella

Dissertations, Theses, and Capstone Projects

Ocean Color radiometry uses remote sensing to interpret ocean dynamics by retrieving remote sensing reflectance (������) from satellite imagery at different scales and over different time periods. ������ spectrum characterizes the ocean color that we observe, and from which we can discern concentrations of chlorophyll, organic and inorganic particles, and carbon fluxes in the ocean and atmosphere. ������ is derived from the total radiance at the top of the atmosphere (TOA). However, it only represents up to ten percent of the total signal. Hence, the retrieval of ������ from the total radiance at TOA involves the application of atmospheric correction …

Multifunctional Organic Monolayer-Based Coatings For Implantable Biosensors And Bioelectronic Devices: Review And Perspectives, Taral Patel, Jie Huang, Katarzyna Krukiewicz

Electrical and Computer Engineering Faculty Research & Creative Works

The Emerging Field of Biosensors and Bioelectronics Seeks to Exploit Biology in Combination with the Recent Advances in Engineering. Even Though Biomedical Implants Can Significantly Improve Quality of Life, Problems Following the Process of Implantation Are Still an Issue. in This Review, We Point Out Organic Monolayer-Based Coatings as a Potential Solution to the Major Limitations of Implantable Biomaterials, Including Limited Biocompatibility, the Risk of Biofouling, Bacterial Colonization, and Stability under in Vivo Conditions. at First, Selected Current and Perspective Biomaterials Are Discussed, with the Focus on their Use as Implantable Biosensors and Biomedical Electrodes. Surface Modification Strategies for Implantable …

Absorbance Measurements From Normal-Hearing Ears In The National Health And Nutrition Examination Survey, 2015–2016 And 2017–2020, Jiayi Sun, Nicholas J. Horton, Susan E. Voss

Engineering: Faculty Publications

Objective: To summarize absorbance and impedance angles from normal-hearing ears within the 2015–2016 and 2017–2020 US National Health and Nutrition Examination Surveys (NHANES).

Design: Two publicly available NHANES datasets were analyzed. Ears meeting criteria for normal hearing and valid absorbance and impedance angle measurements were identified. Measurements were summarized via descriptive statistics within categories of age cohort, race/ethnicity cohort, sex (male, female), and ear (left, right).

Results: A total of 7029 ears from 4150 subjects, ages 6 to 80 years, met inclusion criteria. Differences between subgroups within all categories (age, race/ethnicity, sex, and ear) were fractions of the sample SDs. …

Syne1 Mutation Is Associated With Increased Tumor Mutation Burden And Immune Cell Infiltration In Ovarian Cancer, Laura M. Harbin, Nan Lin, Frederick R. Ueland, Jill M. Kolesar

Markey Cancer Center Faculty Publications

SYNE1, a nuclear envelope protein critical for cellular structure and signaling, is downregulated in numerous malignancies. SYNE1 alterations are found in 10% of gynecologic malignancies and 5% of epithelial ovarian cancers. Previous studies demonstrated an association between SYNE1 mutation, increased tumor mutation burden (TMB), and immunotherapy response. This study evaluates the SYNE1 mutation frequency, association with TMB, and downstream effects of SYNE1 mutation in ovarian cancer. Genetic information, including whole-exome sequencing, RNA analysis, and somatic tumor testing, was obtained for consenting ovarian cancer patients at an academic medical center. Mutation frequencies were compared between the institutional cohort and The Cancer …

Investigation Of Deformation Behavior Of Additively Manufactured Aisi 316l Stainless Steel With In Situ Micro-Compression Testing, Fei Teng, Ching-Heng Shiau, Cheng Sun, Robert C. O'Brien, Michael D. Mcmurtrey

Materials Science and Engineering Faculty Publications and Presentations

Additive manufacturing techniques are being used more and more to perform the precise fabrication of engineering components with complex geometries. The heterogeneity of additively manufactured microstructures deteriorates the mechanical integrity of products. In this paper, we printed AISI 316L stainless steel using the additive manufacturing technique of laser metal deposition. Both single-phase and dual-phase substructures were formed in the grain interiors. Electron backscatter diffraction and energy-dispersive X-ray spectroscopy indicate that Si, Mo, S, Cr were enriched, while Fe was depleted along the substructure boundaries. In situ micro-compression testing was performed at room temperature along the [001] orientation. The dual-phase substructures …

Intrinsic And Atomic Layer Etching Enhanced Area-Selective Atomic Layer Deposition Of Molybdenum Disulfide Thin Films, Jake Soares, Wesley Jen, John D. Hues, Drew Lysne, Jesse Wensel, Steven M. Hues, Elton Graugnard

Materials Science and Engineering Faculty Publications and Presentations

For continual scaling in microelectronics, new processes for precise high volume fabrication are required. Area-selective atomic layer deposition (ASALD) can provide an avenue for self-aligned material patterning and offers an approach to correct edge placement errors commonly found in top-down patterning processes. Two-dimensional transition metal dichalcogenides also offer great potential in scaled microelectronic devices due to their high mobilities and few-atom thickness. In this work, we report ASALD of MoS₂ thin films by deposition with MoF₆ and H₂S precursor reactants. The inherent selectivity of the MoS₂ atomic layer deposition (ALD) process is demonstrated by growth …

Constrained Multiagent Reinforcement Learning For Large Agent Population, Jiajing Ling, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar

Research Collection School Of Computing and Information Systems

Learning control policies for a large number of agents in a decentralized setting is challenging due to partial observability, uncertainty in the environment, and scalability challenges. While several scalable multiagent RL (MARL) methods have been proposed, relatively few approaches exist for large scale constrained MARL settings. To address this, we first formulate the constrained MARL problem in a collective multiagent setting where interactions among agents are governed by the aggregate count and types of agents, and do not depend on agents’ specific identities. Second, we show that standard Lagrangian relaxation methods, which are popular for single agent RL, do not …

Grasp Solution Approach For The E-Waste Collection Problem, Aldy Gunawan, Dang Viet Anh Nguyen, Pham Kien Minh Nguyen, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …

When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem In Peer-To-Peer Logistics Platforms, Zhiqin Zhang, Waldy Joe, Yuyang Er, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Peer-to-Peer (P2P) logistics platforms, unlike traditional last-mile logistics providers, do not have dedicated delivery resources (both vehicles and drivers). Thus, the efficiency of such operating model lies in the successful matching of demand and supply, i.e., how to match the delivery tasks with suitable drivers that will result in successful assignment and completion of the tasks. We consider a Same-Day Delivery Problem (SDDP) involving a P2P logistics platform where new orders arrive dynamically and the platform operator needs to generate a list of recommended orders to the crowdsourced drivers. We formulate this problem as a Dynamic Order Recommendations Problem (DORP). …

Understanding Multi-Homing And Switching By Platform Drivers, Xiaotong Guo, Andreas Haupt, Hai Wang, Rida Qadri, Jinhua Zhao

Research Collection School Of Computing and Information Systems

Freelance drivers in the shared mobility market frequently switch or work for multiple platforms, affecting driver labor supply. Due to the importance of driver labor supply for the shared mobility market, understanding drivers’ switching and multi-homing behavior is vital to managing service quality on – and effective regulation of – mobility platforms. However, a lack of individual-level data on driver behavior has thus far impeded a deeper understanding. This paper taxonomizes and estimates perceived switching and multi-homing frictions on mobility platforms. Based on a structural model of driver labor supply, we estimate switching and multi-homing costs in a platform duopoly …

Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

System Strength Shortfall Challenges For Renewable Energy-Based Power Systems: A Review, Md O. Qays, Iftekhar Ahmad, Daryoush Habibi, Asma Aziz, Thair Mahmoud

Research outputs 2022 to 2026

Renewable energy sources such as wind farms and solar power plants are replacing conventional coal-based synchronous generators (SGs) to achieve net-zero carbon emissions worldwide. SGs play an important role in enhancing system strength in a power system to make it more stable during voltage/frequency disruptions. However, traditional coal-fired SGs are being decommissioned in many parts of the world, owing to stringent environmental regulations and low levelized cost of energy of renewables. Consequently, maintaining system strength in a renewable energy-dominated power system has become a major challenge, and without adequate mitigation techniques, low system strength can potentially cause widespread power outages. …

Enhancing The Co2 Trapping Capacity Of Saudi Arabian Basalt Via Nanofluid Treatment: Implications For Co2 Geo-Storage, Muhammad Ali, Nurudeen Yekeen, Mirhasan Hosseini, Ghazanfer R. Abbasi, Amer Alanazi, Alireza Keshavarz, Thomas Finkbeiner, Hussein Hoteit

Research outputs 2022 to 2026

Mineralization reactions in basaltic formations have gained recent interest as an effective method for CO2 geo-storage in order to mitigate anthropogenic greenhouse gas emissions. The CO2/rock interactions, including interfacial tension and wettability, are crucial factors in determining the CO2 trapping capacity and the feasibility of CO2 geological storage in these formations. The Red Sea geological coast in Saudi Arabia has many basaltic formations, and their wetting characteristics are rarely reported in the literature. Moreover, organic acid contamination is inherent in geo-storage formations and significantly impacts their CO2 geo-storage capacities. Hence, to reverse the organic effect, the influence of various SiO2 …

Harnessing The Power Of Computational Fluid Dynamics For Flow Coefficient And Rain Resistance Improvement Of Type 1 Natural Ventilators, Amin Roostaee, Mehdi Khiadani, Hussein A. Mohammed, Abdellah Shafieian

Research outputs 2022 to 2026

Type 1 Natural Ventilators act like a shield protecting the building from rain intrusion while allowing air to flow from outside. Despite their wide applications, they are far from the ideal design, mainly due to a lack of data on their air discharge coefficient and rain defense effectiveness under different climatic conditions. According to ventilation standards, since experimental testing of different geometries and configurations of these ventilators are complex, difficult, and costly, this study aims to implement Computational Fluid Dynamics (CFD) tools to investigate their performance. To do this, a combination of 9 various louvre panels was designed with 1, …

Harnessing The Power Of Neural Networks For The Investigation Of Solar-Driven Membrane Distillation Systems Under The Dynamic Operation Mode, Pooria Behnam, Masoumeh Zargar, Abdellah Shafieian, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system …

Ceramic Particles Reinforced Copper Matrix Composites Manufactured By Advanced Powder Metallurgy: Preparation, Performance, And Mechanisms, Yi-Fan Yan, Shu-Qing Kou, Hong-Yu Yang, Shi-Li Shu, Feng Qiu, Qi-Chuan Jiang, Lai-Chang Zhang

Research outputs 2022 to 2026

Copper matrix composites doped with ceramic particles are known to effectively enhance the mechanical properties, thermal expansion behavior and high-temperature stability of copper while maintaining high thermal and electrical conductivity. This greatly expands the applications of copper as a functional material in thermal and conductive components, including electronic packaging materials and heat sinks, brushes, integrated circuit lead frames. So far, endeavors have been focusing on how to choose suitable ceramic components and fully exert strengthening effect of ceramic particles in the copper matrix. This article reviews and analyzes the effects of preparation techniques and the characteristics of ceramic particles, including …

Upgrading Biomass To Bio-Oil: Catalytic Hydrodeoxygenation And Pyrolysis Of Vanillin And Anisole Biomass Model Compounds, Ahmad J. Yaghi

Theses

The purpose of this project is to investigate capacity of selected formulations of catalysts toward the hydrodeoxygenation (HDO) of biomass model compounds in the gas phase with the underlying aim to produce hydrocarbon cuts that can be directly used as transportation fuels. The first part of this project is to devoted to illustrate procedures for the synthesis of potent (HDO) catalysts. Synthesized catalysts encompass nickel (Ni), cobalt (Co) and palladium (Pd) supported on zeolite, cerium oxide (CeO₂) or alumina (Al₂O₃). Various characterization techniques such as X-ray Diffraction (XRD), Temperature Programmed Reduction (TPR) were deployed …