Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Feasibility Analysis of Coupling FDS Modeling with Machine Learning for Situational Awareness in Aircraft Hangars(2022) Davis, Alison Marie; Milke, James A; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Situational awareness is a critical factor in maintaining the safety of firefighters and can be largely improved in buildings using distributed sensors that provide real-time data. A two-phase approach is used to increase situational awareness in aircraft hangars. Phase I consists of modeling a hangar with an open door in Fire Dynamics Simulator (FDS), with a high density of smoke, temperature, CO and CO2 sensors located at the ceiling. Fuels of interest including Douglas fir, polyethylene, paper, JP-8, and propane are modeled in six potential fire locations, with five locations along the centerline of the hangar and one in the corner of the hangar. Additionally, wind and beams at the ceiling are added to the simulation to determine the impact on the products of combustion that the sensors pick up. Phase II uses the data acquired from the FDS simulations to inform and build machine learning models that utilize supervised learning techniques to identify the location of the fire, the magnitude of the fire and the composition of the fuel that is burning. It is determined that temperature and smoke are the key products of combustion needed for these analyses. The location of the fire is identified within a circular area with a 5 m radius by using temperature measurements, thus reducing the amount of input data needed for the machine learning models. The magnitude of the fire is predicted using temperature as inputs to a heat release rate (HRR) model using a fully connected, three-layer, feed forward neural network. The composition of the fuel is predicted using a linear support vector machine that supports multi-class classification, using products of temperature and smoke obscuration as inputs. The location model is 80% accurate, the HRR model is 85% accurate and the fuel composition model varies between 62% and 91% accuracy depending on the classification goals. These results prove the feasibility of machine learning applications in an aircraft hangar setting.Item DETECTION OF RAIN-ON-SNOW EVENTS AND ITS IMPACT ON PASSIVE MICROWAVE-BASED SNOW WATER EQUIVALENT RETRIEVAL(2018) Ryan, Elizabeth Meghan; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rain-on-snow (ROS) events can impact snow stratigraphy via generation of wet snow and ice crust(s) within the snowpack. Considering the assumptions of most passive microwave-based snow water equivalent (SWE) retrievals, which include a dry and homogenous snowpack, ROS events could significantly impact SWE retrieval accuracy. This study explored the feasibility of various approaches to detect ROS events using multiple data types (i.e., satellite observations, model output, and in-situ measurements). Agreement in ROS events detected varied among the different data types. Only ~10% of suspected ROS events were flagged using the satellite-based algorithm. Alternatively, ~50% of suspected ROS events were flagged using the model-based algorithm, whereas ~40% of suspected ROS events were flagged using the in-situ measurements-based algorithm. Findings were unable to speak to the impact of ROS events on SWE retrieval accuracy due to the lack of in-situ SWE measurements; however, a slight pattern in local fluctuations was observed.Item Identification of Precursor Signals to Impending Cooking Related Fires(2015) Zevotek, Robin; Milke, Jim A; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cooking related fires continue to be the leading cause of fires in homes. In an effort to reduce the number of cooking fires Underwriters Laboratories (UL) and the University of Maryland (UMD) partnered to evaluate detection of cooking related fires. An experimental protocol was developed to examine if precursor signals capable of predicting an impending fire can be detected to provide adequate warning prior to flaming fire. A series of eleven different experiments were conducted to acquire signals from sensors located at or near an electric coil range. The data recorded was analyzed to identify element gas temperature, carbon monoxide concentration, optical density and ionization signal as potential indicators of an impending fire. Further work is needed to evaluate sensor threshold values and the algorithms developed for other cooking styles and cooking appliances.Item THE DESIGN AND STUDY OF THE SUB-MILLIMETER WAVE LENGTH GYROTRON AND FUNDAMENTAL AND SECOND CYCLOTRON HARMONICS(2015) Pu, Ruifeng; Granatstein, Victor L.; Nusinovich, Gregory S.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation documents research activities directed toward designing high power, high efficiency gyrotrons to operate in the sub-millimeter wavelength region. The gyrotron is to produce pulsed RF power at 670 GHz, with possible application to a novel scheme for detecting concealed radioactive materials. High efficiency is to be achieved by designing a cavity resonator in which the electrons interact with the high-order TE31,8 mode. The choice of resonating mode helps to alleviate Ohmic losses in the cavity walls, and simulation results show that the output efficiency could be more than 30%. The design study takes into account a variety of known effects that could affect efficiency, such as orbital velocity spread, voltage depression and after-cavity interaction. The 670GHz gyrotron was built using the resonator design; operation confirmed that record high efficiency was achieved at an output power level of about 200 kilowatts. In addition, the issue of radial spread in electron guiding centers, which is related to the design of the magnetron injection gun used in the 670GHz gyrotron, was also examined. This spread degrades the interaction between the electrons and the RF field. This often overlooked issue is important for future electron gun designs; this thesis presents analytical methods for estimating how much the degradation affects gyrotron efficiency. The analytical method was verified with numerical simulation, showing that the efficiency's sensitivity to spread in guiding centers is highly dependent on the location of an annular electron beam: when the beam is injected in the inner peak of the desired mode, the radial spread should be kept to less than 1/3 of the RF wavelength. Finally, the dissertation investigates the possibility of further extending the operating parameters of the gyrotron by using the second harmonic of the electron cyclotron resonance. An average output power could be increased by operating the gyrotron continuously rather than in pulses. Using the second cyclotron harmonic allows the magnetic field requirement for resonance condition to be reduced by a factor of two, so that in 670 GHz gyrotrons the pulsed solenoid can be replaced with a cryo-magnet. The investigation shows that for the TE31,8 mode at the second cyclotron harmonic, the operating mode has only one competing mode at the fundamental cyclotron harmonic that could present a mode stability issue. Numerical simulation shows that this mode is TE11,6, the operating mode can suppress this mode, while achieving 20% interaction efficiency. Results also reveal that the resonator for the operating mode at second cyclotron harmonic must be modified to increase the Q-factor. Continuously operating gyrotrons using cryo-magnets have been used for plasma heating in controlled thermonuclear fusion research, albeit at lower frequency than the 670 GHz of the current study.Item Model Based Optimization and Design of Secure Systems(2013) Malik, Waseem Ansar; Martins, Nuno C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)ABSTRACT Title of dissertation: MODEL BASED OPTIMIZATION AND DESIGN OF SECURE SYSTEMS Waseem Ansar Malik, Doctor of Philosophy, 2013 Dissertation directed by: Prof. Nuno C. Martins Department of Electrical and Computer Engineering University of Maryland, College Park Dr. Ananthram Swami Computational and Information Sciences Directorate Army Research Laboratory Control systems are widely used in modern industry and find wide applications in power systems, nuclear and chemical plants, the aerospace industry, robotics, communication devices, and embedded systems. All these systems typically rely on an underlying computing and networking infrastructure which has considerable security vulnerabilities. The biggest threat, in this age and time, to modern systems are cyber attacks from adversaries. Recent cyber attacks have practically shut down government websites affecting government operation, undermined financial institutions, and have even infringed on public privacy. Thus it is extremely important to conduct studies on the design and analysis of secure systems. This work is an effort in this research direction and is mainly focused on incorporating security in the design of modern control systems. In the first part of this dissertation, we present a linear quadratic optimal control problem subjected to security constraints. We consider an adversary which can make partial noisy measurements of the state. The task of the controller is to generate control sequences such that the adversary is unable to estimate the terminal state. This is done by minimizing a quadratic cost while satisfying security constraints. The resulting optimization problems are shown to be convex and the optimal solution is computed using Lagrangian based techniques. For the case when the terminal state has a discrete distribution the optimal solution is shown to be nonlinear in the terminal state. This is followed by considering the case when the terminal state has a continuous distribution. The resulting infinite dimensional optimization problems are shown to be convex and the optimal solution is proven to be affine in the terminal state. In the next part of this dissertation, we analyze several team decision problems subjected to security constraints. Specifically, we consider problem formulations where there are two decision makers each controlling a different dynamical system. Each decision maker receives information regarding the respective terminal states that it is required to reach and applies a control sequence accordingly. An adversary makes partial noisy measurements of the states and tries to estimate the respective terminal states. It is shown that the optimal solution is affine in the terminal state when it is identical for both systems. We also consider the general case where the terminal states are correlated. The resulting infinite dimensional optimization problems are shown to convex programs and we prove that the optimal solution is affine in the information available to the decision makers. Next, a stochastic receding horizon control problem is considered and analyzed. Specifically, we consider a system with bounded disturbances and hard bounds on the control inputs. Utilizing a suboptimal disturbance feedback scheme, the optimization problem is shown to be convex. The problem of minimizing the empirical mean of the cost function is analyzed. We provide bounds on the disturbance sample size to compute the empirical minimum of the problem. Further, we consider the problem where there are hard computational constraints and complex on-line optimization is not feasible. This is addressed by randomly generating both the control inputs and the additive disturbances. Bounds on sample sizes are provided which guarantee a notion of a probable near minimum. Model uncertainty is also incorporated into the framework and relevant bounds are provided which guarantee a probable near minimax value. This work finds many applications in miniature devices and miniature robotics. In the final part of this dissertation, we consider a centralized intrusion detection problem with jointly optimal sensor placement. A team of sensors make measurements regarding the presence of an intruder and report their observations to a decision maker. The decision maker solves a jointly optimal detection and sensor placement problem. For the case when the number of sensors is equal to the number of placement points, we prove that uniform placement of sensors is not strictly optimal. We introduce and utilize a majorization based partial order for the placement of sensors. For the case when the number of sensors is less than or equal to six, we show that for a fixed local probability of detection (probability of false alarm) increasing the probability of false alarm (probability of detection) results in optimal placements that are higher on a majorization based partial order.Item Neutron Detection by Scintillation of Noble-Gas Excimers(2012) McComb, Jacob Collin; al-Sheikhly, Mohamad; Nuclear Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Neutron detection is a technique essential to homeland security, nuclear reactor instrumentation, neutron diffraction science, oil-well logging, particle physics and radiation safety. The current shortage of helium-3, the neutron absorber used in most gas-filled proportional counters, has created a strong incentive to develop alternate methods of neutron detection. Excimer-based neutron detection (END) provides an alternative with many attractive properties. Like proportional counters, END relies on the conversion of a neutron into energetic charged particles, through an exothermic capture reaction with a neutron absorbing nucleus (10B, 6Li,3He). As charged particles from these reactions lose energy in a surrounding gas, they cause electron excitation and ionization. Whereas most gas-filled detectors collect ionized charge to form a signal, END depends on the formation of diatomic noble-gas excimers (Ar2*, Kr2*, Xe2*). Upon decaying, excimers emit far-ultraviolet (FUV) photons, which may be collected by a photomultiplier tube or other photon detector. This phenomenon provides a means of neutron detection with a number of advantages over traditional methods. This thesis investigates excimer scintillation yield from the heavy noble gases following the boron-neutron capture reaction in 10B thin-film targets. Additionally, the thesis examines noble-gas excimer lifetimes with relationship to gas type and gas pressure. Experimental data were collected both at the National Institute of Standards and Technology (NIST) Center for Neutron Research, and on a newly developed neutron beamline at the Maryland University Training Reactor. The components of the experiment were calibrated at NIST and the University of Maryland, using FUV synchrotron radiation, neutron imaging, and foil activation techniques, among others. Computer modeling was employed to simulate charged-particle transport and excimer photon emission within the experimental apparatus. The observed excimer scintillation yields from the 10B(n,α)7Li reaction are comparable to the yields of many liquid and solid neutron scintillators. Additionally, the observed slow triplet-state decay of neutron-capture-induced excimers may be used in a practical detector to discriminate neutron interactions from gamma-ray interactions. The results of these measurements and simulations will contribute to the development and optimization of a deployable neutron detector based on noble-gas excimer scintillation.Item AVISARME: Audio Visual Synchronization Algorithm for a Robotic Musician Ensemble(2012) Berman, David Ross; Chopra, Nikhil; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis presents a beat detection algorithm which combines both audio and visual inputs to synchronize a robotic musician to its human counterpart. Although there has been considerable work done to create sophisticated methods for audio beat detection, the visual aspect of musicianship has been largely ignored. With advancements in image processing techniques, as well as both computer and imaging technologies, it has recently become feasible to integrate visual inputs into beat detection algorithms. Additionally, the proposed method for audio tempo detection also attempts to solve many issues that are present in current algorithms. Current audio-only algorithms have imperfections, whether they are inaccurate, too computationally expensive, or suffer from terrible resolution. Through further experimental testing on both a popular music database and simulated music signals, the proposed algorithm performed statistically better in both accuracy and robustness than the baseline approaches. Furthermore, the proposed approach is extremely efficient, taking only 45ms to compute on a 2.5s signal, and maintains an extremely high temporal resolution of 0.125 BPM. The visual integration also relies on Full Scene Tracking, allowing it to be utilized for live beat detection for practically all musicians and instruments. Numerous optimization techniques have been implemented, such as pyramidal optimization (PO) and clustering techniques which are presented in this thesis. A Temporal Difference Learning approach to sensor fusion and beat synchronization is also proposed and tested thoroughly. This TD learning algorithm implements a novel policy switching criterion which provides a stable, yet quickly reacting estimation of tempo. The proposed algorithm has been implemented and tested on a robotic drummer to verify the validity of the approach. The results from testing are documented in great detail and compared with previously proposed approaches.Item Nucleic Acid Extraction and Detection Across Two-Dimensional Tissue Samples(2010) Armani, Michael Daniel; Shapiro, Benjamin; Smela, Elisabeth; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Visualizing genetic changes throughout tissues can explain basic biological functions and molecular pathways in disease. However, over 90% of mammalian messenger RNA (mRNA) is in low abundance (<15 copies per cell) making them hard to see with existing techniques, such as in-situ hybridization (ISH). In the example of diagnosing cancer, a disease caused by genetic mutations, only a few cancer-associated mRNAs can be visualized in the clinic due to the poor sensitivity of ISH. To improve the detection of low-abundance mRNA, many researchers combine the cells across a tissue sample by taking a scrape. Mixing cells provides only one data point and masks the inherent heterogeneity of tissues. To address these challenges, we invented a sensitive method for mapping nucleic acids across tissues called 2D-PCR. 2D-PCR transfers a tissue section into an array of wells, confining and separating the tissue into subregions. Chemical steps are then used to free nucleic acids from the tissues subregions. If the freed genetic material is mRNA, a purification step is also performed. One or more nucleic acids are then amplified using PCR and detected across the tissue to produce a map. As an initial proof of concept, a DNA map was made from a frozen tissue section using 2D-PCR at the resolution of 1.6 mm per well. The technique was improved to perform the more challenging task of mapping three mRNA molecules from a frozen tissue section. Because the majority of clinical tissues are stored using formalin fixation and not freezing, 2D-PCR was improved once more to detect up to 24 mRNAs from formalin-fixed tissue microarrays. This last approach was used to validate genetic profiles in human normal and tumor prostate samples faster than with existing techniques. In conclusion, 2D-PCR is a robust method for detecting genetic changes across tissues or from many tissue samples. 2D-PCR can be used today for studying differences in nucleic acids between tumor and normal specimens or differences in subregions of the brain.