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
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Item ENERGY ANALYSIS OF A METRO TRANSIT SYSTEM FOR SUSTAINABILITY AND EFFICIENCY IMPROVEMENT(2023) Higgins, Jordan Andrew; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The industrial sector in the US accounted for 33% of the overall energy consumption and 23% of total GHG Emissions in 2022, necessitating the need for energy efficiency and decarbonization of this sector. This study identifies common opportunities and challenges while performing energy audits for the State of Maryland public transportation maintenance complex and proposes site-specific energy efficiency measures. Utilizing performance indices such as Energy Use Intensity (EUI) and load factor from end-use energy data, as well as walkthrough observations from energy audits, energy efficiency measures specific to each facility were formulated to augment the overall energy performance. Additionally, energy modeling helped pinpoint the additional scope of energy efficiency improvements that could have potential significant energy performance improvements and reduce on-site GHG emissions. Among the energy conservation measures considered, the re-sizing and decarbonization of HVAC equipment has the greatest contribution to energy and GHG savings, with a 100% decrease in natural gas, a 37% decrease in electricity use annually, and net decrease of 272 Mton CO2. This study aims to highlight the similarities and differences in existing transportation and maintenance facilities and the applicable technology(ies) that could streamline and serve as a guide for energy audits for transportation maintenance facilities by demonstrating the most common energy efficiency measures and subsequent achievable savings for these facilities.Item LOW-POWER AND SECURE IMPLEMENTATION OF NEURAL NETWORKS BY APPROXIMATION(2022) Xu, Qian; Qu, Gang; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Neural networks (NN), one type of machine learning (ML) algorithms, have emerged as a powerful paradigm for sophisticated applications such as pattern recognition and natural language processing. In this dissertation, we study how to apply the principle of approximate computing to solve two challenging problems in neural networks, namely energy efficiency and security. More specifically, we investigate approximation across three stacks in the implementation of NN models: computation units, data storage, and the NN model itself. Computation units, such as adders and multipliers, have been one of the main targets for power-efficient implementation of neural networks. Many low-power approximate adders and multipliers have been proposed. NNs also require complex operations like logarithms, despite the heavy usage and high energy consumption on such operations, they are not optimized for energy efficiency. Our first contribution is a truncation-based approximation method that can balance the computation precision and energy consumption of the popular floating-point logarithmic operation. We derive a formula for the most energy-efficient implementation of the logarithm unit given an error variance range. Based on this theoretical result, we propose BWOLF (Bit-Width Optimization for Logarithmic Function) to determine the bit-width of operands in the logarithms computation in order to minimize the energy consumption in delivering the required computation precision. We evaluate the efficacy of BWOLF in terms of energy savings on two widely used applications: Kullback-Leibler Divergence and Bayesian Neural Network. The experimental results validate the correctness of our analysis and show significant energy savings, from 27.18% to 95.92%, over the full-precision computation and a baseline approximation method based on uniform truncation. Storage approximation by reducing the supply voltage for dynamic random access memory (DRAM) is effective in saving the power for neural networks. However, this will introduce physical errors in DRAM and could impact the performance of NN models. In the second part of this dissertation, we explore the potential of storage approximation in improving NN system’s security in training data privacy. More specifically, we consider the Model Inversion Attacks (MIAs) that extrapolate the training data from model parameters. Our proposed solution -- MIDAS: Model Inversion Defenses with an Approximate memory System -- intentionally introduces memory faults by overscaling voltage to thwart MIA without compromising the original ML model. We use detailed SPICE simulations to build the DRAM fault model and evaluate MIDAS against state-of-the-art MIAs. Experiments demonstrate that MIDAS can effectively protect training data from run-time MIAs. In terms of the Pearson Correlation Coefficient (PCC) similarity between the original training data and the recovered version, MIDAS reduces the PCC value by 55% and 40% for shallow and deep neural networks under 1.5% accuracy relaxation. Although MIDAS shows promising security benefits through storage approximation, such approximation modifies the neural network parameters and may reduce the NN model’s accuracy. In the third part of this dissertation, we propose model approximation which aims at generating an approximate NN model to correct the errors during training and consequently reduce the possible degradation of NN’s classification results. We demonstrate this concept on gradient inversion attacks which utilize transmitted gradients between the nodes in a federated learning system to reconstruct the training data. Therefore, we propose DAGIA, a Data Augmentation defense against Gradient Inversion Attacks, to deliberately extend the training dataset and report the corresponding gradient updates to protect the original data. For multiple data augmentation techniques, we empirically evaluate the trade-off between test accuracy and information leakage to select the best technique for DAGIA. According to the Structural Similarity (SSIM) between reconstructed training data and the original CIFAR-10 dataset, the experimental results show that DAGIA can reduce the SSIM by 54% with a slightly increased test accuracy for the ConvNet model. In summary, this dissertation focuses on the role of approximation in energy efficiency and security during the implementation of neural networks. We show that computation units for complex operators can be approximated to reduce energy, the storage for neural network weights can be approximated to improve both energy efficiency and security (against information leak), and the NN model itself could be approximated during training for security enhancement. This dissertation work demonstrates that approximation is a promising method to improve the performance of neural networks. It opens the door to applying the principle of approximate computing to the implementation and optimization of neural networks where there are abundant opportunities for approximation.Item DETERMINING MEASUREMENT REQUIREMENTS FOR WHOLE BUILDING ENERGY MODEL CALIBRATION(2020) Dahlhausen, Matthew Galen; Srebric, Jelena; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Energy retrofits of existing buildings reduce grid requirements for new generation and reduce greenhouse gas emissions. However, it is difficult to estimate energy savings, both at the individual building and entire building stock level, because building energy models are poorly calibrated to actual building performance. This uncertainty has made it difficult to prioritize research and development and incentive programs for building technologies at the utility, state, and federal level. This research seeks to make it easier to generate building energy models for existing buildings, and to calibrate buildings at the stock level, to create accurate commercial building load forecasts. Once calibrated, these building models can be used as seeds to other building energy model calibration approaches and to help utility, state, and federal actors to identify promising energy saving technologies in commercial buildings. This research details the economics of a building energy retrofit at a singular building; contributes significantly to the development of ComStock, a model of the commercial building stock in the U.S.; identifies important parameters for calibrating ComStock; and calibrates ComStock for an example utility region of Fort Collins, CO against individual commercial building interval data. A study of retrofit costs finds that measure cost and model uncertainty are the most significant sources of variation in retrofit financial performance, followed financing cost. A wide range of greenhouse gas pricing scenarios show they have little impact on the financial performance of whole building retrofits. A sensitivity analysis of ComStock model inputs across an exhaustive range of models identifies 19 parameters that explain 80 of energy use and 25 parameters that explain 90% of energy use. Building floor area alone explains 41% of energy use. Finally, a comparison of ComStock to Fort Collins, CO interval meter data shows a 6.92% normalized mean bias error and a 16.55% coefficient of variation of root mean square error based on normalized annual energy per floor area. Improvements in meter classification and ComStock model variability will further improve model fit and provide an accurate means of modeling the commercial building stock.Item Designing and Evaluating Next-Generation Thermographic Systems to Support Residential Energy Audits(2018) Mauriello, Matthew Louis; Froehlich, Jon E; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Buildings account for 41% of primary energy consumption in the United States—more than any other sector—and contribute to an increasing portion of carbon dioxide emissions (33% in 1980 vs. 40% in 2009). To help address this problem, the U.S. Department of Energy recommends conducting energy audits to identify sources of inefficiencies that contribute to rising energy use. One effective technique used during energy audits is thermography. Thermographic-based energy auditing activities involve the use of thermal cameras to identify, diagnose, and document energy efficiency issues in the built environment that are visible as anomalous patterns of electromagnetic radiation. These patterns may indicate locations of air leakages, areas of missing insulation, or moisture issues in the built environment. Sensor improvements and falling costs have increased the popularity of this auditing technique, but its effectiveness is often mediated by the training and experience of the auditor. Moreover, given the increasing availability of commodity thermal cameras and the potential for pervasive thermographic scanning in the built environment, there is a surprising lack of understanding about people’s perceptions of this sensing technology and the challenges encountered by an increasingly diverse population of end-users. Finally, there are few specialized tools and methods to support the auditing activities of end-users. To help address these issues, my work focuses on three areas: (i) formative studies to understand and characterize current building thermography practices, benefits, and challenges, (ii) human-centered explorations into the role of automation and the potential of pervasive thermographic scanning in the built environment, and (iii) evaluations of novel, interactive building thermography systems. This dissertation presents a set of studies that qualitatively characterizes building thermography practitioners, explores prototypes of novel thermographic systems at varying fidelity, and synthesizes findings from several field deployments. This dissertation contributes to the fields of sustainability, computer science, and HCI through: (i) characterizations of the end-users of thermography, (ii) critical feedback on proposed automated thermographic solutions, (iii) the design and evaluation of a novel longitudinal thermography system designed to augment the data collection and analysis activities of end-users, and (iv) design recommendations for future thermographic systems.Item Approximate Computing Techniques for Low Power and Energy Efficiency(2018) Gao, Mingze; Qu, Gang; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Approximate computing is an emerging computation paradigm in the era of the Internet of things, big data and AI. It takes advantages of the error-tolerable feature of many applications, such as machine learning and image/signal processing, to reduce the resources consumption and delivers a certain level of computation quality. In this dissertation, we propose several data format oriented approximate computing techniques that will dramatically increase the power/energy efficiency with the insignificant loss of computational quality. For the integer computations, we propose an approximate integer format (AIF) and its associated arithmetic mechanism with controllable computation accuracy. In AIF, operands are segmented at runtime such that the computation is performed only on part of operands by computing units (such as adders and multipliers) of smaller bit-width. The proposed AIF can be used for any arithmetic operation and can be extended to fixed point numbers. AIF requires additional customized hardware support. We also provide a method that can optimize the bit-width of the fixed point computations that run on the general purpose hardware. The traditional bit-width optimization methods mainly focus on minimizing the fraction part since the integer part is restricted by the data range. In our work, we utilize the dynamic fixed point concept and the input data range as the prior knowledge to get rid of this limitation. We expand the computations into data flow graph (DFG) and propose a novel approach to estimate the error during propagation. We derive the function of energy consumption and apply a more efficient optimization strategy to balance the tradeoff between the accuracy and energy. Next, to deal with the floating point computation, we propose a runtime estimation technique by converting data into the logarithmic domain to assess the intermediate result at every node in the data flow graph. Then we evaluate the impact of each node to the overall computation quality, and decide whether we should perform an accurate computation or simply use the estimated value. To approximate the whole graph, we propose three algorithms to make the decisions at certain nodes whether these nodes can be truncated. Besides the low power and energy efficiency concern, we propose a design concept that utilizes the approximate computing to address the security concerns. We can encode the secret keys into the least significant bits of the input data, and decode the final output. In the future work, the input-output pairs will be used for device authentication, verification, and fingerprint.Item OCCUPANT BEHAVIOR IN BUILDING ENERGY MANAGEMENT: BEHAVIORAL CHARACTERIZATION, INTERVENTION AND FORECASTING(2018) Lu, Yujie; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the advent of the climate change and global warming, there is a need to adopt a diversified approach to address climate change; this is especially the case of promoting building energy conservation. This dissertation is one of the first studies that focuses on the occupant behavior in the building energy conservation, in particular three dimensions. First, this study aims to propose a behavior-based model that investigates impact of renters’ rebound effect on building retrofit saving amount and to design the shared saving scheme among major stakeholders during their decision-making process. With demonstration of a real retrofitting project in a university campus, the rebound effect was identified to significantly extend the payback period of retrofit contracts and such the prolonged duration is partially determined by renters’ risk attitudes towards monetary incentives. Second, the study compares two message delivering means, paper-based (e.g. stickers) versus instant messaging tool (e.g. WeChat), as a platform for sharing energy-saving information and promoting occupant energy conservation in China. It was found that WeChat is the most effective intervention in reducing energy consumption, but the effects are short-lived. Using stickers, comparatively, produces more sustained results with long-term engagement of households. The changes in certain occupant energy behaviors are also correlated with individuals’ perception of responsibility and quality of life to explain the heterogeneity of individual behaviors. Third, the study examines the interaction effect between occupant personality, energy behavior and intervention strategies with algorithms that can identify the optimal intervention strategy tailored for each household. This is followed by an improved Support Vector Regression (SVR) model that is capable of predicting household electricity consumption under optimal intervention strategies according to occupant behavior and personality traits. The proposed intervention lead to an average reduction of 12.1% in monthly household energy consumption compared with conventional behavioral interventions. The methods and algorithms developed from this study are pioneer works providing implications to measure the influence of occupant behaviors on energy saving amounts, to enrich and diversify behavioral intervention strategies, and to design incentives, programs and policies that effectively regulate occupant behaviors, collectively contributing to the demand-side energy management in buildings.Item CO2 TRANSCRITICAL REFRIGERATION WITH MECHANICAL SUBCOOLING: ENERGY EFFICIENCY, DEMAND RESPONSE AND THERMAL STORAGE(2018) Bush, John; Radermacher, Reinhard; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines two important concepts: improvements to transcritical carbon dioxide (CO2) refrigeration systems being deployed in supermarkets, and their potential use for demand response and load shifting in a utility-connected application. As regulatory pressure increases to reduce the use of ozone depleting and greenhouse gases as refrigerants, the heating, ventilation, air conditioning and refrigeration (HVAC&R) industry is moving towards alternative refrigerants including natural substances such as carbon dioxide. CO2 has already gained traction as the refrigerant of choice for supermarket applications in some countries, but deployment in warmer climates has been slower due to concerns over efficiency when the cycle operates in transcritical mode. Among the cycle enhancements considered to overcome these concerns is the use of dedicated mechanical subcooling. Laboratory testing was performed on a transcritical booster system with mechanical subcooling to quantify the system performance with and without the subcooler. Data was used to develop and validate transient models, which in turn were used to study the system-wide effects of demand response, particularly short-term shedding of medium or low temperature load. Systems can provide value to the electric grid if they can be responsive to changes in electric utility generation, as indicated by direct calls to shed load or price signals. To further expand the potential usefulness of the refrigeration cycle in grid-interactive operation, the integration of thermal storage is considered. In particular, the integration of thermal storage into the subcooling system is investigated. The mechanical subcooler is used to “charge” a storage media (such as water or another phase change material) overnight, and the storage media allows the subcooler to turn off during peak hours. This allows the system to shift load and allow temporary reduction in electric power usage without a reduction in delivered refrigerating capacity. These two paths are potentially complementary: the load shifting of the integrated thermal storage provides long-term load reduction, while direct load shedding in evaporators allows more agile, short-term reductions. The models developed and validated with laboratory data and expanded upon with thermal energy storage and demand response approaches provide new learnings into enhanced load shifting and demand response capability. The findings of this work show that particularly in time-of-use rate structures with a high ratio of on-peak to off-peak pricing, the thermal storage and load shedding strategies here can provide a reduction in total refrigerating energy cost, even though the changes proposed introduce a slight increase in daily energy under the simulated conditions. In a simulated hot day for Baltimore, Maryland, the energy consumption was 2.6% higher using the thermal storage system than without. In the most extreme case, comparing an aggressive real-world Time-of-Use rate with thermal storage and load shedding against a flat-rate case from the same utility and no controls or storage, a cost savings reduction of 21% was calculated. Comparing baseline operation against a controlled load-shifting strategy under the same time-of-use rate plan, the cost reduction was in the range of 2.8-8.7% depending upon the specific plan.Item SIMULATION AND ANALYSIS OF ENERGY CONSUMPTION FOR TWO COMPLEX AND ENERGY-INTENSIVE BUILDINGS ON UMD CAMPUS(2017) Savage, Dana Mason; Ohadi, Michael M; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Microbiology Building and Hornbake Library are two multi-purpose and complex buildings, and are among the highest energy-intensive buildings on the University of Maryland College Park Campus. This thesis details the energy analysis and energy consumption models developed to identify energy savings opportunities for these two buildings. Three reports are given per building: one – a comprehensive summarization of relevant building information; two – a utility analysis, including an energy benchmarking study, evaluating the relative performance of each facility; three – a detailed energy model to replicate current operation and simulate potential energy savings resulting from no-and-low cost energy conservation measures. In total, 11 of the 12 measures simulated are strongly recommended for implementation. The predicted combined energy and utility savings are respectively 18,648.4 MMBtu and $436,128 annually. These actionable proposals to substantially reduce the buildings’ energy consumption contribute to the University’s commitment to achieve greater energy efficiency throughout campus.Item Security and Energy Efficiency in Resource-Constrained Wireless Multi-hop Networks(2016) Paraskevas, Evripidis; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent decades, there has been a huge improvement and interest from the research community in wireless multi-hop networks. Such networks have widespread applications in civil, commercial and military applications. Paradigms of this type of networks that are critical for many aspects of human lives are mobile ad-hoc networks, sensor networks, which are used for monitoring buildings and large agricultural areas, and vehicular networks with applications in traffic monitoring and regulation. Internet of Things (IoT) is also envisioned as a multi-hop network consisting of small interconnected devices, called ``things", such as smart meters, smart traffic lights, thermostats etc. Wireless multi-hop networks suffer from resource constraints, because all the devices have limited battery, computational power and memory. Battery level of these devices should be preserved in order to ensure reliability and communication across the network. In addition, these devices are not a priori designed to defend against sophisticated adversaries, which may be deployed across the network in order to disrupt network operation. In addition, the distributed nature of this type of networks introduces another limitation to protocol performance in the presence of adversaries. Hence, the inherit nature of this type of networks poses severe limitations on designing and optimizing protocols and network operations. In this dissertation, we focus on proposing novel techniques for designing more resilient protocols to attackers and more energy efficient protocols. In the first part of the dissertation, we investigate the scenario of multiple adversaries deployed across the network, which reduce significantly the network performance. We adopt a component-based and a cross-layer view of network protocols to make protocols secure and resilient to attacks and to utilize our techniques across existing network protocols. We use the notion of trust between network entities to propose lightweight defense mechanisms, which also satisfy performance requirements. Using cryptographic primitives in our network scenario can introduce significant computational overhead. In addition, behavioral aspects of entities are not captured by cryptographic primitives. Hence, trust metrics provide an efficient security metric in these scenarios, which can be utilized to introduce lightweight defense mechanisms applicable to deployed network protocols. In the second part of the dissertation, we focus on energy efficiency considerations in this type of networks. Our motivation for this work is to extend network lifetime, but at the same time maintain critical performance requirements. We propose a distributed sleep management framework for heterogeneous machine-to-machine networks and two novel energy efficient metrics. This framework and the routing metrics are integrated into existing routing protocols for machine-to-machine networks. We demonstrate the efficiency of our approach in terms of increasing network lifetime and maintaining packet delivery ratio. Furthermore, we propose a novel multi-metric energy efficient routing protocol for dynamic networks (i.e. mobile ad-hoc networks) and illustrate its performance in terms of network lifetime. Finally, we investigate the energy-aware sensor coverage problem and we propose a novel game theoretic approach to capture the tradeoff between sensor coverage efficiency and energy consumption.Item Age of Information and Energy Efficiency in Communication Networks(2015) Dutra da Costa, Maice; Ephremides, Anthony; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on two important aspects of communication systems, namely energy efficiency and age of information. Both aspects have received much less attention than traditional performance metrics, such as throughput and delay. The need to improve the energy efficiency in communication networks is apparent, given the high demand for power consuming applications to be implemented in devices with limited energy supplies. Additionally, improvements in energy efficiency are encouraged by possible reductions in network operation costs, and by the increasing awareness of the environmental impact caused by the information and communication technologies. In this dissertation, energy efficiency is studied in the context of a cognitive wireless network, in which users have different priorities to access the network resources, possibly interfering and cooperating among themselves. A new parametrization is proposed to characterize performance trade-offs associated with energy efficiency for non-cooperative and cooperative network models. Additionally, a game theoretic model is proposed to study resource allocation in a cooperative cognitive network, accounting for energy efficiency in the utility functions. Age of information is a relatively new concept, which aims to characterize the timeliness of information. It is relevant to any system concerned with timeliness of information, and particularly relevant when information is used to make decisions, but the value of the information is degraded with time. This is the case in many applications of communications and control systems. In this dissertation, the age of information is first investigated for status update communication systems. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of information at the destination node is the time elapsed since the last received update was generated. The status update systems are modeled using queuing theory. We propose models for status update systems capable of managing the packets before transmission, aiming to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update. We also propose a new framework, based on the concept of age of information, to analyze the effect of outdated Channel State Information (CSI) on the performance of a communication link in which the source node acquires the CSI through periodic feedback from the destination node. The proposed framework is suitable to analyze the trade-off between performance and timeliness of the CSI, which is a fundamental step to design efficient adaptation functions and feedback protocols.