Defenses

Author: Edwin Fishman

Title: The Natural Response of Uniform in Air and Partially Submerged in a Quiescent Water Body

Date/time: April 24th at 10:00am

Location: 3179K Aerospace Engineering Conference Room, Glenn L. Martin Hall.

Committee members:
Professor James Duncan, Advisor & Chair
Professor Miao Yu
Professor Kenneth Kiger

Abstract:

The free vibration of three aluminum plates (.4 m wide, 1.08 m long) oriented horizontally is studied experimentally under two fluid conditions, one with the plate surround by air and the other with the bottom plate surface in contact with a large undisturbed pool of water.  Measurements of the out of plane deflection of the upper surfaces of the plates are made using cinematic Digital Image Correlation (DIC) over the center portion of the surface  and optical tracking of the center point. Three plate geometries and boundary conditions are studied: A uniform plate with 6.35 mm thickness pinned at the two opposite narrrow ends (UP), a uniform plate with 4.83 mm thickness simply supported at one narrow end and clamped at the opposite end (UC), and a stepped plate with thickness varying from 12.7 mm to 6.35 mm pinned at two opposite narrow ends (SP). The plate’s free response is induced using an impact hammer at three locations along the center-line of the plate. Video frames of the plate’s motion are collected from stereoscopic cameras and processed using DaVis-Strainmaster and MATLAB to extract full-field displacements as a function of time. 2-degree-of-freedom displacements of the plate center are collected from tracking the center target’s motion. Time and frequency response plots are presented for comparison between the half-wet and air cases and analysis of their dynamics. It is found that the added mass of the water results in lower measured natural frequencies and modified mode shapes. These results are compared to mode shapes/frequencies produced in Creo Simulate and found to agree. Further experiments are discussed.

Author: Zahra Nozarijouybari

Title: Optimal Probing of Battery Cycles for Machine Learning-Based Model Development 

Date/time: April 29th at 2:00pm

Location: 2162 DeWALT Conference Room, Glenn L. Martin Hall.

Committee members:
Professor Hosam Fathy, Advisor & Chair
Professor Mark Fuge
Professor Steven A. Gabriel
Professor Teng Li
Professor Chunsheng Wang, Dean’s Representative

Abstract:

Author: Matthew Keeler

Title: DESIGN NOVELTY EVALUATION THROUGH ORDINAL EMBEDDING: COMPARISON OF NOVELTY AND TRIPLET ERRORS

Date/time: April 19th at 12:00pm

Location: 2164 DeWALT Seminar Room, Glenn L. Martin Hall.

Committee members:
Professor Mark D. Fuge, Chair/Advisor
Professor Shapour Azarm
Professor Nikhil Chopra

Abstract:
A practical and well-studied method for computing the novelty of a design is to construct an ordinal embedding via a collection of pairwise comparisons between items (called triplets), and use distances within that embedding to compute which designs are farthest from the center. Unfortunately, ordinal embedding methods can require a large number of triplets before their primary error measure—the triplet violation error—converges. But if our goal is accurate novelty estimation, is it really necessary to fully minimize all triplet violations? Can we extract useful information regarding the novelty of all or some items using fewer triplets than classical convergence rates might imply? This thesis addresses this question by studying the relationship between triplet violation error and novelty score error when using ordinal embeddings.

We find that estimating the novelty of a set of items via ordinal embedding can require significantly fewer human provided triplets than is needed to converge the triplet error, and that this effect is modulated by the type of triplet sampling method (random versus uncertainty sampling). Having learned this, we propose the use of a custom metric we call the ‘Expected Model Change’ (EMC) which we use to observe when novelty information in the embedding has stopped updating under newly labeled triplets, so that conservative bounding functions need not be used. Moreover, to avoid the dangers of low accuracy in selecting the dimension of the ordinal embedding, we propose use of the Expected Model Change for tuning the embedding dimension to an appropriate value. In this framework, we explore the convergence properties of ordinal embeddings reconstructed from triplets taken from a variety of synthetic and real-world design spaces.

Author: Cashen Diniz

Title: Denoising the Design Space: Diffusion Models for Accelerated Airfoil Shape Optimization

Date/time: April 16th at 9:30am

Location: 2164 DeWALT Seminar Room, Glenn L. Martin Hall.

Committee members:
Professor Mark D. Fuge, Chair/Advisor
Professor James Baeder
Professor Michael Otte

Abstract:

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.

Author: Anthony Fabbri

Title of dissertation: Second Wave Mechanics

Date/time: April 16th, 2024 at 2:00pm

Location: 2162 DeWALT Conference Room, Glenn L. Martin Hall.

Committee members:

• Dr. Jeffrey Herrmann, Advisor and Chair
• Dr. Cole Miller
• Dr. Yi Xu

Abstract:

The COVID-19 pandemic experienced very well-documented “waves” of the virus’ progression, which can be analyzed to predict future wave
behavior. Common wave shape patterns can be identified in real-time
data, to predict the pattern of mutations that have recently occurred,
but have not become popularly known as yet, to predict the remaining
future outcome of the wave. By only considering the patterns in the
data that could possibly have acted in tandem to generate the observed
results, many false patterns can be eliminated, and, therefore, hidden
variables can be estimated to a very high degree of probability.
Similar mathematical relationships can reveal hidden variables in
other underlying differential equations.

Author: Matthew Weiner

Title of dissertation: A Framework For Remaining Useful Life Prediction and Optimization For Complex Engineering Systems

Date/time: April 15th, 2024 at 10:00am

Zoom: https://umd.zoom.us/j/6365088363

Location: 2164 DeWALT Conference Room, Glenn L. Martin Hall.

Committee members:

• Dr. Shapour Azarm, Advisor and Chair
• Dr. Katrina Groth, Co-Advisor
• Dr. Yunfei Zhao

Abstract:

Author: Yekanth Ram Chalumuri

Title of dissertation: TOWARDS AUTOMATION OF HEMORRHAGE DIAGNOSTICS AND THERAPEUTICS

Date/time:  April 10th, 2024 at 12pm

Location:  ERF 1207 (IREAP Building)

Committee members:

Dr. Jin-Oh Hahn, Advisor and Chair

Dr. Yang Tao, Dean’s Representative

Dr. Hosam Fathy

Dr. Mark Fuge

Dr. Yancy Diaz-Mercado

Dr. Andrew Reisner

Abstract:

This thesis primarily focuses on developing algorithms and methodologies to achieve the broader aim of advancing the care in hemorrhage diagnostics and therapeutics, especially in low resource settings. The first goal of this thesis is to develop algorithms that can detect hemorrhage using non-invasive physiological signals. Our results showed that our machine learning algorithm can successfully classify various types of hypovolemia. We also developed a physics-based approach to detect and estimate internal hemorrhage even when the body is being compensated by fluid resuscitation using continuous non-invasive hemoglobin measurements. We evaluated the model in silico and determined the maximum acceptable measurement noise that can make these algorithms effective.

The second objective is to advance the regulatory aspects of physiological closed-loop control systems in maintaining blood pressure at a desired value during hemorrhage. 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 naïve. We developed a physics-based model that can predict blood pressure, heart rate, cardiac output, and hematocrit during hemorrhage and resuscitation and validated using a validation framework that uses sampled digital twins for validation along with more rigorous validation metrics. Overall, the goal of this research is to improve the care and outcomes of patients with hemorrhagic shock by utilizing machine learning, inference, and modeling approaches for early detection and treatment.

Author: Sidney Molnar

Title of dissertation: Metareasoning Strategies to Correct Navigation Failures of Autonomous Ground Robots

Date/time: April 8th, 2024 at 10:00am

Zoom: https://umd.zoom.us/j/9892794999?omn=94673990602

Location: 2164 DeWALT Conference Room, Glenn L. Martin Hall.

Committee members:

• Dr. Jeffrey Herrmann, Advisor and Chair
• Dr. Michael Otte
• Dr. Shapour Azarm

Abstract: Due to the complexity of autonomous systems, theoretically perfect path planning algorithms sometimes fail because of unexpected behaviors that occur when these systems interact with various sub-processes like perception, mapping, and goal planning. These failures can prevent the success of a mission, especially in complex and unexplored environments. Metareasoning, or “thinking about thinking,” is one approach to mitigate these planning failures. This project introduces a novel metareasoning approach that employs various methods of monitoring and control to identify and address path planning irregularities that lead to failures. All methods were integrated into the ARL ground autonomy stack, which utilizes both global and local path planning ROS nodes. The monitoring methods proposed include listening to messages from the planning algorithms, assessing the environmental context of the robot, expected progress methods that evaluate the robot’s progress based on its movement capabilities from a milestone checkpoint, and fixed radius methods that use parameters selected based on mission objectives to assess progress from a milestone checkpoint. The control policies introduced are metric-based sequential policies which select new planner combinations based on benchmark robot performance metrics, context-based pairs policies that assess the effects of switching between two planner combinations, and a restart policy that relaunches a new instance of the same planner combination. The study evaluated which combinations of monitoring and control policies improved or degraded navigation performance by assessing how close the robot could get to the final mission goal. Additionally, this thesis suggests areas for further research to determine the conditions under which metareasoning can improve navigation.

Author: Christina Nikiforidou

Title of dissertation: Locomotion on Granular Media: Reduced-Order Models

Date/time:  April 3rd, 2024 at 9:30am

Location: 2164 DeWALT Conference Room, Glenn L. Martin Hall.

Committee members:

Dr. Balakumar Balachandran, Advisor and Chair

Dr. Peter Chung 

Dr. Teng Li

Abstract: (attached)

Author: Gurtajbir Herr

Title of dissertation:  ON DATA-BASED MAPPING AND NAVIGATION OF UNMANNED GROUND VEHICLES

Date/time:  April 3rd, 2024 at 12:00pm

Location: AVW 1146 (ISR seminar room)

Committee members:

Dr. Nikhil Chopra, Advisor and Chair

Dr. Anubhav Datta, Dean’s Representative

Dr. Balakumar Balachandran

Dr. Miao Yu

Dr. Yancy Diaz-Mercado

Abstract:

Unmanned ground vehicles (UGVs) have seen tremendous advancement in their capabilities and applications in the past two decades. With several key algorithmic and hardware breakthroughs and advancements in deep learning, UGVs are quickly becoming ubiquitous, finding applications as self-driving cars, in remote site inspections, in hospitals and shopping malls, among several others. Motivated by their large-scale adoption, this dissertation aims to enable the navigation of UGVs in complex environments. In this dissertation, we develop a supervised learning-based navigation algorithm that utilizes model predictive control (MPC)  for providing training data. Improving MPC performance by data-based modelling of complex vehicle dynamics is then addressed. Finally, this dissertation deals with detecting and registering transparent objects that may deteriorate navigation performance.

Navigation in dynamic environments poses unique challenges, particularly due to the limited knowledge of the decisions made by other agents and their objectives. In this dissertation, we propose a solution that utilizes an MPC-based planner as an expert to generate high-quality motion commands for a car-like robot operating in a simulated dynamic environment. These commands are then used to train a deep neural network, which learns to navigate. The deep learning-based planner is further enhanced with safety margins to improve its effectiveness in collision avoidance. We then evaluate the performance of our method through simulations and real-world experiments, demonstrating its superior performance in terms of obstacle avoidance and successful mission completion. This research has practical implications for the development of safer and more efficient autonomous vehicles.

Many real-world applications rely on MPC to control UGVs due to its safety guarantees and constraint satisfaction properties. However, the performance of such MPC-based solutions is heavily reliant on the accuracy of the motion model. This dissertation addresses this challenge by exploring a data-based approach to discovering vehicle dynamics. Unlike existing physics-based models that require extensive testing setups and manual tuning for new platforms and driving surfaces, our approach leverages the universal differential equations (UDEs) framework to identify unknown dynamics from vehicle data. This innovative approach, which does not make assumptions about the unknown dynamics terms and directly models the vector field, is then deployed to showcase its efficacy. This research opens up new possibilities for more accurate and adaptable motion models for UGVs. 
With the increasing adoption of glass and other transparent materials, UGVs must be able to detect and register them for reliable navigation. Unfortunately, such objects are not easily detected by LiDARs and cameras. In this dissertation, we study algorithms for detecting and including glass objects in a Graph SLAM framework. We use a simple and computationally inexpensive glass detection scheme to detect glass objects. We present the methodology to incorporate the identified objects into the occupancy grid maintained by such a framework. We also address the issue of drift accumulation that can affect mapping performance when operating in large environments.