A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Analyzing Inverse Design Problems from a Topological Perspective(2024) Chen, Qiuyi; Fuge, Mark; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Inverse design (ID) problems are inverse problems that aim to rapidly retrieve the subset of valid designs having the desired performances and properties under the given conditions. In practice, this can be solved by training generative models to approximate and sample the posterior distributions of designs. However, little has been done to understand their mechanisms and limitations from a theoretical perspective. This dissertation leverages theoretical tools from general and differential topology to answer these three questions of inverse design: what does a set of valid designs look like? How helpful are the data-driven generative models for retrieving the desired designs from this set? What topological properties affect the subset of desired designs? The dissertation proceeds by dismantling inverse (design) problems into two major subjects: that is, the representing and probing of a given set of valid designs (or data), and the retrieval of the desired designs (or data) from this given set. It draws inspiration from topology and geometry to investigate them and makes the main contributions below: 1. Chapter 3 details a novel representation learning method called Least Volume, which has properties similar to nonlinear PCA for representing datasets. It can minimize the representation's dimension automatically and, as shown in Chapter 4, conducts contrastive learning when applied to labeled datasets. 2. Two conditional generative models are developed to generate performant 2-D airfoils and 3-D heat sinks in Chapter 5 and 6 respectively. They can produce realistic designs to warm-start further optimization, with the relevant chapters detailing their acceleration effects. 3. Lastly, Chapter 7 describes how to use Least volume to solve high-dimensional inverse problems efficiently. Specifically, using examples from physic system identification, the chapter uncovers the correlation between the inverse problem's uncertainty and its intrinsic dimensions.Item Deployment of Large Vision and Language Models for Real-Time Robotic Triage in a Mass Casualty Incident(2024) Mangel, Alexandra Paige; Paley, Derek; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the event of a mass casualty incident, such as a natural disaster or war zone, having a system of triage in place that is efficient and accurate is critical for life-saving intervention, but medical personnel and resources are often strained and struggle to provide immediate care to those in need. This thesis proposes a system of autonomous air and ground vehicles equipped with stand-off sensing equipment designed to detect and localize casualties and assess them for critical injury patterns. The goal is to assist emergency medical technicians in identifying those in need of primary care by using generative AI models to analyze casualty images and communicate with the victims. Large language models are explored for the purpose of developing a chatbot that can ask a casualty where they are experiencing pain and make an informed assessment about injury classifications, and a vision language model is prompt engineered to assess a casualty image to produce a report on designated injury classifiers.Item Studies in Differential Privacy and Federated Learning(2024) Zawacki, Christopher Cameron; Abed, Eyad H; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the late 20th century, Machine Learning underwent a paradigm shift from model-driven to data-driven design. Rather than field specific models, advances in sensors, data storage, and computing power enabled the collection of increasing amounts of data. The abundance of new data allowed researchers to fit flexible models directly to observed data. The influx of information made possible numerous advances, including the development of novel medicines, increases in efficiency of markets, and the proliferation of vast sensor networks. However, not all data should be freely accessible. Sensitive medical records, personal finances, and private IDs are all currently stored on digital devices across the world with the expectation that they remain private. However, at the same time, such data is frequently instrumental in the development of predictive models. Since the beginning of the 21st century, researchers have recognized that traditional methods of anonymizing data are inadequate for protecting client identities. This dissertation's primary focus is the advancement of two fields of data privacy: Differential Privacy and Federated Learning. Differential Privacy is one of the most successful modern privacy methods. By injecting carefully structured noise into a dataset, Differential Privacy obscures individual contributions while allowing researchers to extract meaningful information from the aggregate. Within this methodology, the Gaussian mechanism is one of the most common privacy mechanisms due to its favorable properties such as the ability of each client to apply noise locally before transmission to a server. However, the use of this mechanism yields only an approximate form of Differential Privacy. This dissertation introduces the first in-depth analysis of the Symmetric alpha-Stable (SaS) privacy mechanism, demonstrating its ability to achieve pure-Differential Privacy while retaining local applicability. Based on these findings, the dissertation advocates for using the SaS privacy mechanism in protecting the privacy of client data. Federated Learning is a sub-field of Machine Learning, which trains Machine Learning models across a collection (federation) of client devices. This approach aims to protect client privacy by limiting the type of information that clients transmit to the server. However, this distributed environment poses challenges such as non-uniform data distributions and inconsistent client update rates, which reduces the accuracy of trained models. To overcome these challenges, we introduce Federated Inference, a novel algorithm that we show is consistent in federated environments. That is, even when the data is unevenly distributed and the clients' responses to the server are staggered in time (asynchronous), the algorithm is able to converge to the global optimum. We also present a novel result in system identification in which we extend a method known as Dynamic Mode Decomposition to accommodate input delayed systems. This advancement enhances the accuracy of identifying and controlling systems relevant to privacy-sensitive applications such as smart grids and autonomous vehicles. Privacy is increasingly pertinent, especially as investments in computer infrastructure constantly grow in order to cater to larger client bases. Privacy failures impact an ever-growing number of individuals. This dissertation reports on our efforts to advance the toolkit of data privacy tools through novel methods and analysis while navigating the challenges of the field.Item OPTIMAL PROBING OF BATTERY CYCLES FOR MACHINE LEARNING-BASED MODEL DEVELOPMENT(2024) Nozarijouybari, Zahra; Fathy, Hosam HF; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines the problems of optimizing the selection of the datasets and experiments used for parameterizing machine learning-based electrochemical battery models. The key idea is that data selection, or “probing” can empower such models to achieve greater fidelity levels. The dissertation is motivated by the potential of battery models to enable theprediction and optimization of battery performance and control strategies. The literature presents multiple battery modeling approaches, including equivalent circuit, physics-based, and machine learning models. Machine learning is particularly attractive in the battery systems domain, thanks to its flexibility and ability to model battery performance and aging dynamics. Moreover, there is a growing interest in the literature in hybrid models that combine the benefits of machine learning with either the simplicity of equivalent circuit models or the predictiveness of physics-based models or both. The focus of this dissertation is on both hybrid and purely data-driven battery models. Moreover, the overarching question guiding the dissertation is: how does the selection of the datasets and experiments used for parameterizing these models affect their fidelity and parameter identifiability? Parameter identifiability is a fundamental concept from information theory that refers to the degree to which one can accurately estimate a given model’s parameters from input-output data. There is substantial existing research in the literature on battery parameter identifiability. An important lesson from this literature is that the design of a battery experiment can affect parameter identifiability significantly. Hence, test trajectory optimization has the potential to substantially improve model parameter identifiability. The literature explores this lesson for equivalent circuit and physics-based battery models. However, there is a noticeable gap in the literature regarding identifiability analysis and optimization for both machine learning-based and hybrid battery models. To address the above gap, the dissertation makes four novel contributions to the literature. The first contribution is an extensive survey of the machine learning-based battery modeling literature, highlighting the critical need for information-rich and clean datasets for parameterizing data-driven battery models. The second contribution is a K-means clustering-based algorithm for detecting outlier patterns in experimental battery cycling data. This algorithm is used for pre-cleaning the experimental cycling datasets for laboratory-fabricated lithium-sulfur (Li-S) batteries, thereby enabling the higher-fidelity fitting of a neural network model to these datasets. The third contribution is a novel algorithm for optimizing the cycling of a lithium iron phosphate (LFP) to maximize the parameter identifiability of a hybrid model of this battery. This algorithm succeeds in improving the resulting model’s Fisher identifiability significantly in simulation. The final contribution focuses on the application of such test trajectory optimization to the experimental cycling of commercial LFP cells. This work shows that test trajectory optimization is s effective not just at improving parameter identifiability, but also at probing and uncovering higher-order battery dynamics not incorporated in the initial baseline model. Collectively, all four of these contributions show the degree to which the selection of battery cycling datasets and experiments for richness and cleanness can enable higher-fidelity data-driven and hybrid modeling, for multiple battery chemistries.Item Denoising the Design Space: Diffusion Models for Accelerated Airfoil Shape Optimization(2024) Diniz, Cashen; Fuge, Mark D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Generative models offer the possibility to accelerate and potentially substitute parts of the often expensive traditional design optimization process. We present Aero-DDM, a novel application of a latent denoising diffusion model (DDM) capable of generating airfoil geometries conditioned on flow parameters and an area constraint. Additionally, we create a novel, diverse dataset of optimized airfoil designs that better reflects a realistic design space than has been done in previous work. Aero-DDM is applied to this dataset, and key metrics are assessed both statistically and with an open-source computational fluid dynamics (CFD) solver to determine the performance of the generated designs. We compare our approach to an optimal transport GAN, and demonstrate that our model can generate designs with superior performance statistically, in aerodynamic benchmarks, and in warm-start scenarios. We also extend our diffusion model approach, and demonstrate that the number of steps required for inference can be reduced by as much as ~86%, compared to an optimized version of the baseline inference process, without meaningful degradation in design quality, simply by using the initial design to start the denoising process.Item TOWARDS AUTOMATION OF HEMORRHAGE DIAGNOSTICS AND THERAPEUTICS(2024) Chalumuri, Yekanth Ram; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The main aim of the thesis is to advance the technology in the development ofalgorithms and methodologies that will advance the care in hemorrhage diagnostics and therapeutics in low resource settings. The first objective of this thesis is to develop algorithms to primarily detect internal hemorrhage using non-invasive multi-modal physiological sensing. We developed a machine learning algorithm that can classify various types of hypovolemia and is shown to be performing superior to the algorithms developed primarily based on vital signs. To address the limitations in the data-driven approaches, we explored physics-based approaches to detect internal hemorrhage. In silico analysis showed that our physics-based algorithms can not only detect hemorrhage but also can detect hemorrhage even when hemorrhage is being compensated by fluid resuscitation. The second objective is to advance the regulatory aspects of physiological closed-loopcontrol systems in maintaining blood pressure at a desired value during hemorrhage and resuscitation. Physiological closed-loop control systems offer an exciting opportunity to treat hemorrhage in low resource settings but often face regulatory challenges due to safety concerns. A physics-based model with rigorous validation can improve regulatory aspects of such systems but current validation techniques are very naive. We developed a physics-based model that can predict hemodynamics during hemorrhage and resuscitation and validated these factors using a validation framework that uses sampled digital twins. Then we utilized the validated model to evaluate its efficacy in predicting the performance capability of the model and virtual patient generator in predicting the closed-loop controller metrics of unseen experimental data. To summarize, we tried to improve the hemorrhage care using novel algorithmdevelopment and in silico validation and evaluation of computation models that can be used to treat hemorrhage.Item Cardiovascular Physiological Monitoring Based on Video(2023) Gebeyehu, Henok; Wu, Min; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Regular, continuous monitoring of the heart is advantageous to maintaining one’s cardiovascular health as it enables the early detection of potentially life-threatening cardiovascular diseases. Typically, the required devices for continuous monitoring are found in a clinical setting, but recent research developments have advanced remote physiological monitoring capabilities and expanded the options for continuous monitoring from home. This thesis focuses on further extending the monitoring capabilities of consumer electronic devices to motivate the feasibility of reconstructing Electrocardiograms via a smartphone camera. First, the relationship between skin tone and remote physiological sensing is examined as variations in melanin concentrations for people of diverse skin tones can affect remote physiological sensing. In this work, a study is performed to observe the prospect of reducing the performance disparity caused by melanin differences by exploring the sites from which the physiological signal is collected. Second, the physiological signals obtained from the previous part are enhanced to improve the signal-to-noise ratio and utilized to infer ECG as parts of a novel technique that emphasizes interpretability as a guiding principle. The findings in this work have the potential to enable and promote the remote sensing of a physiological signal that is more informative than what is currently possible with remote sensing.Item Ordering Non-Linear Subspaces for Airfoil Design and Optimization via Rotation Augmented Gradients(2023) Van Slooten, Alec; Fuge, Mark; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Airfoil optimization is critical to the design of turbine blades and aerial vehicle wings, among other aerodynamic applications. This design process is often constrained by the computational time required to perform CFD simulations on different design options, or the availability of adjoint solvers. A common method to mitigate some of this computational expense in nongradient optimization is to perform dimensionality reduction on the data and optimize the design within this smaller subspace. Although learning these low-dimensional airfoil manifolds often facilitates aerodynamic optimization, these subspaces are often still computationally expensive to explore. Moreover, the complex data organization of many current nonlinear models make it difficult to reduce dimensionality without model retraining. Inducing orderings of latent components restructures the data, reduces dimensionality reduction information loss, and shows promise in providing near-optimal representations in various dimensions while only requiring the model to be trained once. Exploring the response of airfoil manifolds to data and model selection and inducing latent component orderings have potential to expedite airfoil design and optimization processes. This thesis first investigates airfoil manifolds by testing the performance of linear and nonlinear dimensionality reduction models, examining if optimized geometries occupy lower dimensional manifolds than non-optimized geometries, and by testing if the learned representation can be improved by using target optimization conditions as data set features. We find that autoencoders, although often suffering from stability issues, have increased performance over linear methods such as PCA in low dimensional representations of airfoil databases. We also find that the use of optimized geometry and the addition of performance parameters have little effect on the intrinsic dimensionality of the data. This thesis then explores a recently proposed approach for inducing latent space orderings called Rotation Augmented Gradient (RAG) [1]. We extend their algorithm to nonlinear models to evaluate its efficacy at creating easily-navigable latent spaces with reduced training, increased stability, and improved design space preconditioning. Our extension of the RAG algorithm to nonlinear models has potential to expedite dimensional analyses in cases with near-zero gradients and long training times by eliminating the need to retrain the model for different dimensional subspacesItem Development of Low-Cost Autonomous Systems(2023) Saar, Logan Miles; Takeuchi, Ichiro; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A central challenge of materials discovery for improved technologies arises from the increasing compositional, processing, and structural complexity involved when synthesizing hitherto unexplored material systems. Traditional Edisonian and combinatorial high-throughput methods have not been able to keep up with the exponential growth in potential materials and relevant property metrics. Autonomously operated Self-Driving Labs (SDLs) - guided by the optimal experiment design sub-field of machine learning, known as active learning - have arisen as promising candidates for intelligently searching these high-dimensional search spaces. In the fields of biology, pharmacology, and chemistry, these SDLs have allowed for expedited experimental discovery of new drugs, catalysts, and more. However, in material science, highly specialized workflows and bespoke robotics have limited the impact of SDLs and contributed to their exorbitant costs. In order to equip the next generation workforce of scientists and advanced manufacturers with the skills needed to coexist with, improve, and understand the benefits and limitations of these autonomous systems, a low-cost and modular SDL must be available to them. This thesis describes the development of such a system and its implementation in an undergraduate and graduate machine learning for materials science course. The low-cost SDL system developed is shown to be affordable for primary through graduate level adoption, and provides a hands-on method for simultaneously teaching active learning, robotics, measurement science, programming, and teamwork: all necessary skills for an autonomous compatible workforce. A novel hypothesis generation and validation active learning scheme is also demonstrated in the discovery of simple composition/acidity relationships.Item STUDY OF PHASE EQUILIBRIA AND DIFFUSION IN SEVERAL BINARY AND MULTINARY ALLOY SYSTEMS(2023) Wang, Chuangye; Zhao, Ji-Cheng; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study leveraged the high-throughput experiments and high-throughput calculations to study the thermodynamic and kinetic behaviors, and mechanical properties of different alloy systems. CALculation of PHAse Diagrams (CALPHAD) and machine learning (ML) are two computational approaches to predict the phase equilibria of alloys and were adopted to study the phase formation of 2436 high-entropy alloys (HEAs). HEAs were found to form 100% BCC at VEC < 6.87 and form essentially 100% FCC at VEC > 9.16 experimentally, this is consistent with the CALPHAD calculations (VEC = valence electron concentration). ML trained models can reach more than 90% accuracy in predicting BCC/B2, BCC/B2 + FCC, and FCC phases. An autonomous materials search engine (AMASE) method was developed by collaborators to map the phase diagram of the thin-film Sn-Bi system in a closed-loop method, which speeds up the phase diagram mapping and thermodynamic assessment processes over the traditional grid mapping. In the NSF sponsored project, the diffusion-multiple approach was employed to map the phase diagrams of the ternary subsystems of the Cr-Fe-Ni-Nb system. Wavelength-dispersive spectroscopy (WDS) mapping was adopted to measure the compositions in the triple-junction areas of diffusion multiples, leading to improve the efficiency of constructing phase diagrams in comparison with the previous practice of using electron probe microanalysis (EPMA) line scans. The WDS mapping method was demonstrated in the experimentally determined ternary phase diagram of Fe-Nb-Ni at 1100 °C. The measured tie-line data was then provided to collaborators to obtain more accurate predictions of the phase stability of topologically close-packed (TCP) phases for future improvement of the Ni-based thermodynamic databases. Besides thermodynamic calculations, mobility assessments of 25 binary systems with single-phase BCC or FCC structure were performed using the 1-parameter Z-Z-Z binary diffusion model. The data will be useful input to robust diffusion coefficient (mobility) databases. Hardness testing was performed to study the solid solution hardening effects on eight Mg-X (X = Al, Ca, Ce, Gd, Li, Sn, Y, Zn) binary systems using liquid-solid diffusion couples and on three binary systems (Mo-Nb, Mo-Ta, and Nb-Ta) of refractory elements using novel macro-gradient samples made by electron beam welding of stacked wedge-samples.