Electrical & Computer Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2765
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Item ML-ENABLED SOLAR PV ELECTRICITY GENERATION PROJECTION FOR A LARGE ACADEMIC CAMPUS TO REDUCE ONSITE CO2 EMISSIONS(2024) Zargarzadeh, Sahar; Babadi, Behtash; Ohadi, Michael; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Mitigating CO2 emissions is crucial in reducing climate change, as these emissions contribute to global warming and its adverse impacts on ecosystems. According to statistics, photovoltaic electricity is 15 times less carbon-intensive than natural gas and 30 times less than coal, making Solar Photovoltaic an attractive option among various methods of reducing electricity demand. This study aims to apply Machine Learning to predict future impact of solar PV-Generated electricity in reducing CO2 emissions based. The primary utility data source is from the University of Maryland's campus; with over half of the campus's energy consumption derived from electricity, therefore reducing electricity consumption to mitigate carbon emissions is paramount. 153 buildings on the campus were investigated, spanning the years 2015-2022. This study was conducted in four key phases. In the first phase, an open source tool, PVWatts was used to gather data to predict PV-generated energy. This served as the foundation for phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity on any period of time, leveraging machine learning to capture complex patterns in energy data for more accurate forecasts. The SHAP (SHapley Additive exPlanations) technique was incorporated into the proposed framework to enhance model explainability. Phase III involved calculating historical CO2 emissions based on past energy consumption data, providing a baseline for comparison. A meta-learning algorithm was implemented in the phase IV to project future CO2 emissions post-solar PV installation. This comparison facilitated the evaluation of different machine learning techniques for projecting emissions and assessing the university’s progress toward Maryland’s sustainability objectives. The ML-based tool developed in this study demonstrated that solar PV implementation could potentially reduce the campus’s footprint by approximately 18% for the studied clusters of buildings with the uncertainty level of about 1.7%, contributing to sustainability objectives and the promotion of cleaner energy use.Item Control and Stabilization of Soft Inverted Pendulum on a Cart(2023) Ajithkumar, Ananth; Chopra, Nikhil Dr.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Underactuated systems are systems that cannot be controlled to track any arbitrary trajectories in their configuration space. In this work, we introduce a novel soft-robotic pendulum on a cart system. This is an underactuated soft-robotic system with two degrees of under-actuation. We model the system, derive the kinematics, and motivated by the control strategies for classical underactuated systems, we study the swing-up control and stabilization of this system around the vertical equilibrium point. The switching-based control law uses an energy-based control for swing-up and LQR for stabilization once the system is within the region of attraction of LQR. The simulation results depict the efficaciousness of the developed control scheme. Further, in this thesis, we discuss the viability and feasibility of feedback linearization, partially feedback-linearize the system, and analyze the zero dynamics of the system.Item Integration of virus-like particle macromolecular bioreceptors in electrochemical biosensors(2016) Zang, Faheng; Ghodssi, Reza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rapid, sensitive and selective detection of chemical hazards and biological pathogens has shown growing importance in the fields of homeland security, public safety and personal health. In the past two decades, efforts have been focusing on performing point-of-care chemical and biological detections using miniaturized biosensors. These sensors convert target molecule binding events into measurable electrical signals for quantifying target molecule concentration. However, the low receptor density and the use of complex surface chemistry in receptors immobilization on transducers are common bottlenecks in the current biosensor development, adding to the cost, complexity and time. This dissertation presents the development of selective macromolecular Tobacco mosaic virus-like particle (TMV VLP) biosensing receptor, and the microsystem integration of VLPs in microfabricated electrochemical biosensors for rapid and performance-enhanced chemical and biological sensing. Two constructs of VLPs carrying different receptor peptides targeting at 2,4,6-trinitrotoluene (TNT) explosive or anti-FLAG antibody are successfully bioengineered. The VLP-based TNT electrochemical sensor utilizes unique diffusion modulation method enabled by biological binding between target TNT and receptor VLP. The method avoids the influence from any interfering species and environmental background signals, making it extremely suitable for directly quantifying the TNT level in a sample. It is also a rapid method that does not need any sensor surface functionalization process. For antibody sensing, the VLPs carrying both antibody binding peptides and cysteine residues are assembled onto the gold electrodes of an impedance microsensor. With two-phase immunoassays, the VLP-based impedance sensor is able to quantify antibody concentrations down to 9.1 ng/mL. A capillary microfluidics and impedance sensor integrated microsystem is developed to further accelerate the process of VLP assembly on sensors and improve the sensitivity. Open channel capillary micropumps and stop-valves facilitate localized and evaporation-assisted VLP assembly on sensor electrodes within 6 minutes. The VLP-functionalized impedance sensor is capable of label-free sensing of antibodies with the detection limit of 8.8 ng/mL within 5 minutes after sensor functionalization, demonstrating great potential of VLP-based sensors for rapid and on-demand chemical and biological sensing.Item Electro-Thermal Codesign in Liquid Cooled 3D ICs: Pushing the Power-Performance Limits(2013) Shi, Bing; Srivastava, Ankur; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The performance improvement of today's computer systems is usually accompanied by increased chip power consumption and system temperature. Modern CPUs dissipate an average of 70-100W power while spatial and temporal power variations result in hotspots with even higher power density (up to 300W/cm^2). The coming years will continue to witness a significant increase in CPU power dissipation due to advanced multi-core architectures and 3D integration technologies. Nowadays the problems of increased chip power density, leakage power and system temperatures have become major obstacles for further improvement in chip performance. The conventional air cooling based heat sink has been proved to be insufficient for three dimensional integrated circuits (3D-ICs). Hence better cooling solutions are necessary. Micro-fluidic cooling, which integrates micro-channel heat sinks into silicon substrates of the chip and uses liquid flow to remove heat inside the chip, is an effective active cooling scheme for 3D-ICs. While the micro-fluidic cooling provides excellent cooling to 3D-ICs, the associated overhead (cooling power consumed by the pump to inject the coolant through micro-channels) is significant. Moreover, the 3D-IC structure also imposes constraints on micro-channel locations (basically resource conflict with through-silicon-vias TSVs or other structures). In this work, we investigate optimized micro-channel configurations that address the aforementioned considerations. We develop three micro-channel structures (hotspot optimized cooling configuration, bended micro-channel and hybrid cooling network) that can provide sufficient cooling to 3D-IC with minimum cooling power overhead, while at the same time, compatible with the existing electrical structure such as TSVs. These configurations can achieve up to 70% cooling power savings compared with the configuration without any optimization. Based on these configurations, we then develop a micro-fluidic cooling based dynamic thermal management approach that maintains the chip temperature through controlling the fluid flow rate (pressure drop) through micro-channels. These cooling configurations are designed after the electrical parts, and therefore, compatible with the current standard IC design flow. Furthermore, the electrical, thermal, cooling and mechanical aspects of 3D-IC are interdependent. Hence the conventional design flow that designs the cooling configuration after electrical aspect is finished will result in inefficiencies. In order to overcome this problem, we then investigate electrical-thermal co-design methodology for 3D-ICs. Two co-design problems are explored: TSV assignment and micro-channel placement co-design, and gate sizing and fluidic cooling co-design. The experimental results show that the co-design enables a fundamental power-performance improvement over the conventional design flow which separates the electrical and cooling design. For example, the gate sizing and fluidic cooling co-design achieves 12% power savings under the same circuit timing constraint and 16% circuit speedup under the same power budget.